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Cover Feature:New Quartz and Zircon Si Isotopic Reference Materials for Precise and Accurate SIMS Isotopic MicroanalysisYu Liu, Xian-Hua Li, Paul S. Savage, Guo-Qiang Tang, Qiu-Li Li, Hui-Min Yu, and Fang Huang

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Yu-Feng Li (Institute of High Energy Physics,

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Zheng Wang (Shanghai Institute of Ceramics,

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Zhenli Zhu (China University of Geosciences,

China)

Zhi Xing (Tsinghua University, China)

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In This Issue:

New Quartz and Zircon Si Isotopic Reference Materials for Precise and Accurate SIMS Isotopic Microanalysis

Yu Liu, Xian-Hua Li, Paul S. Savage, Guo-Qiang Tang, Qiu-Li Li, Hui-Min Yu, and Fang Huang.................................................99

Continuous Online Leaching System Coupled with Inductively Coupled Plasma Mass Spectrometry for Assessment of

Cr, As, Cd, Sb, and Pb in Soils

Alastair Kierulf, Michael Watts, Iris Koch, and Diane Beauchemin...................................................................................................107

A Highly Efficient Method for the Accurate and Precise Determination of Zinc Isotopic Ratios in Zinc-rich Minerals

Using MC-ICP-MS

Xiaojuan Nie, Zhian Bao, Peng Liang, Kaiyun Chen, Chunlei Zong, and Honglin Yuan.................................................................117

Depth Profiling at a Steel-aluminum Interface Using Slow-flow Direct Current Glow Discharge Mass Spectrometry

Gagan Paudel, Geir Langelandsvik, Sergey Khromov, Siri Marthe Arbo, Ida Westermann,

Hans Jørgen Roven, and Marisa Di Sabatino.........................................................................................................................................126

Zircon ZS - a Homogenous Natural Reference Material for U–Pb Age and O–Hf Isotope Microanalyses

Xiao-Xiao Ling, Qiu-Li Li, Chuan Yang, Zhu-Yin Chu, Lian-Jun Feng, Chao Huang, Liang-Liang Huang,

Hong Zhang, Zhen-Hui Hou, Jun-Jie Xu, Yu Liu, Guo-Qiang Tang, Jiao Li, and Xian-Hua Li......................................................134

Cadmium Isotope Analysis of Environmental Reference Materials via Microwave Digestion–Resin

Purification–Double-Spike MC-ICP-MS

Qiang Dong, Cailing Xiao, Wenhan Cheng, Huimin Yu, Jianbo Shi, Yongguang Yin, Yong Liang, and Yong Cai.......................145

Coal Proximate Analysis Based on Synergistic Use of LIBS and NIRS

Shunchun Yao, Huaiqing Qin, Shuixiu Xu, Ziyu Yu, Zhimin Lu, and Zhe Wang............................................................................154

High-precision Fe Isotope Analysis on MC-ICPMS Using a 57Fe-58Fe Double Spike Technique

Xin Li, Yongsheng He, Shan Ke, Ai-Ying Sun, Yin-Chu Zhang, Yang Wang, Ru-Yi Yang, and Pei-Jie Wang.............................164

Review of In-situ Online LIBS Detection in the Atmospheric Environment

Qihang Zhang and Yuzhu Liu.................................................................................................................................................................174

Electron Probe Microanalysis in Geosciences: Analytical Procedures and Recent Advances

Shui-Yuan Yang, Shao-Yong Jiang, Qian Mao, Zhen-Yu Chen, Can Rao, Xiao-Li Li, Wan-Cai Li,

Wen-Qiang Yang, Peng-Li He, and Xiang Li.........................................................................................................................................186

ISSN: 0195-5373/e-ISSN: 2708-521X; CODEN: ASPND7. JAN/FEB 2022, 43(2), 99–199.

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Reference style:

1. T. Luo, Z. C. Hu, W. Zhang, Y. S. Liu, K. Q. Zong, L. Zhou, J. F. Zhang, and S. S. Hu, Anal. Chem., 2018, 90,

9016–9024. https://doi.org/10.1021/acs.analchem.8b01231

2. N. Dauphas and O. Rouxel, Mass Spectrom. Rev., 2006, 25, 515–550. https://doi.org/10.1002/mas.20078

3. H. Z. Lu and H. R. Fan, Fluid Inclusion. Beijing, Science Press, 2004.

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www.at-spectrosc.com/as/article/pdf/20211110 99 At. Spectrosc. 2022, 43(2), 99–106.

New Quartz and Zircon Si Isotopic Reference Materials for

Precise and Accurate SIMS Isotopic Microanalysis

Yu Liu,a Xian-Hua Li,a,b,* Paul S. Savage,c Guo-Qiang Tang,a,b Qiu-Li Li,a,b Hui-Min Yu,d,e and Fang Huangd,e a State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, P. R. China

b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, P. R. China

c School of Earth and Environmental Sciences, University of St Andrews, Bute Building, Queen’s Terrace, St Andrews, KY16 9TS, United Kingdom

d CAS Key Laboratory of Crust-Mantle Materials and Environments, School of Earth and Space Sciences, University of Science and Technology of China,

Hefei 230026, P. R. China

e CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei 230026, P. R. China

Received: November 24, 2021; Revised: April 17, 2022; Accepted: April 23, 2022; Available online: April 25, 2022.

DOI: 10.46770/AS.2021.1110

ABSTRACT: Here we report the Si isotope compositions of four potential reference materials, including one fused quartz

glass (Glass-Qtz), one natural quartz (Qinghu-Qtz), and two natural zircons (Qinghu-Zir and Penglai-Zir), suitable for in-situ Si

isotopic microanalysis. Repeated SIMS (Secondary Ion Mass Spectrometry) analyses demonstrate that these materials are more

homogeneous in Si isotopes (with the spot-to-spot uncertainty of 0.090-0.102‰), compared with the widely used NIST RM 8546

(previously NBS-28) quartz standard (with the spot-to-spot uncertainty poorer

than 0.16‰). Based on the solution-MC-ICP-MS determination, the

recommended 𝛿30Si values are −0.10 ± 0.04 ‰ (2SD), −0.03 ± 0.05 ‰ (2SD),

−0.45 ± 0.06 ‰ (2SD), and −0.34 ± 0.06 ‰ (2SD), for Glass-Qtz, Qinghu-Qtz,

Qinghu-Zir, and Penglai-Zir, respectively. Our results reveal no detectable matrix

effect on SIMS Si isotopic microanalysis between the fused quartz glass (Glass-

Qtz) and natural quartz (Qinghu-Qtz) standards. Therefore, we propose that this

synthetic quartz glass may be used as an alternative, more homogenous standard

for SIMS Si isotopic microanalysis of natural quartz samples.

INTRODUCTION

Silicon (Si) has three stable isotopes (28Si, 92.23% abundance; 29Si,

4.68%; and 30Si, 3.09%), and variations in Si isotopic composition

are typically expressed as 𝛿30SiNBS-28, which is the permil deviation

of 30Si/28Si ratio relative to the NSB-28 quartz reference standard

(N.B. NBS-28 is the historical name, and it is now referred to as

NIST RM 8546; see section 2 for the mathematical definition).

Following the pioneering works of Reynolds and Verhoogen1 and

Allenby2 in the 1950s, and the development and application of

MC-ICP-MS,3 Si isotopes have been widely used to address

various problems in Earth sciences.4 More recently, the application

of microbeam techniques such as SIMS and laser ablation (LA)-

MC-ICP-MS to Si isotopic analysis has allowed for the

investigation of Si isotopic variations at the micron scale,

significantly improving our knowledge of metal-silicate

differentiation of planetary bodies, the biogeochemical cycle of

silicon, and plate tectonics in the Early Earth.5, 6

Compared with bulk analytical methods such as Solution (S)-

MC-ICP-MS (where samples are homogenized during dissolution

and matrix elements are chemically removed prior to analysis),

well-characterized, homogeneous (to the micron scale), matrix-

matched reference materials (RMs) are prerequisites for high-

precision isotopic microanalysis using SIMS and LA-MC-ICP-

MS. Additionally, for SIMS analysis, the so-called "topography

effect"7-9 introduces extra instrument mass fraction (IMF) due to

the sample surface elevation and tilt. Thus, the surface of the

analytical targets should be sufficiently flat. However, due to the

hardness difference between the samples (usually mineral crystals)

and the epoxy resin matrix, the sample grains such as quartz and

zircon typically protrude from the epoxy resin after polishing. This

www.at-spectrosc.com/as/article/pdf/20211110 At. Spectrosc. 2022, 43(2), 99–106.

relief is especially evident in crystals with grain sizes smaller than

100 µm.

The NBS-28 quartz standard is not only the primary standard

for Si isotopic compositions but is also commonly used as the

standard for in situ Si and O isotope microanalysis. However, this

quartz standard is provided as a powder with relatively small grain

size (~100 µm). This small grain size means that the standard is

difficult to adequately flatten during sample preparation. Hence,

the "topography effect" is obvious during SIMS Si isotope analysis

of NBS-28, hampering in situ precise Si isotopic microanalysis

applications. For instance, the spot-to-spot precision of Si isotope

analysis of NBS-28 using SIMS is about 𝛿30Si ± 0.3 ‰ (2SD),9, 10

significantly poorer than ~0.1 ‰ (2SD) for NIST glass 610.9 Even

though the DTCA-X (dynamic transfer contrast aperture in the X

direction) correction can be used to minimize the "topography

effect", the spot-to-spot precision is still around 0.15 ‰ (2SD)

which is generally worse than those of unknown samples (with

larger grain sizes).9 Therefore, NBS-28 quartz has significant

drawbacks as a suitable standard for high-precision SIMS Si

isotope microanalysis due to its small grain size; a related issue is

that NBS-28 is not a homogeneous “fused” sample, so there is the

potential for Si isotopic heterogeneity between and within grains

at the micron scale. Therefore, it is imperative to develop standards

with sufficient grain size and homogenous isotopic compositions

for SIMS Si isotopic microanalysis to allow for a precision better

than 𝛿30Si ± 0.1 ‰.

Herein, we present the results of a long-term study of Si isotopic

measurements using SIMS and S-MC-ICP-MS techniques on four

reference materials (previously developed for O-isotope

microanalysis), including one fused quartz glass (Glass-Qtz), one

natural quartz (Qinghu-Qtz), and two natural zircons (Qinghu-Zir

and Penglai-Zir). Our analytical results demonstrate that these

reference materials are homogeneous in Si isotopes at the micron

scale and can be used for high-precision in situ Si microanalysis.

SAMPLE DESCRIPTION AND

PREPARATION

Six samples were selected in this study, including two natural

quartz samples (NBS-28 and Qinghu-Qtz), artificial quartz glass

(Glass-Qtz), NIST-610 glass, and two zircon samples, Qinghu-Zir

and Penglai-Zir. Silicon isotopic compositions are expressed as:

𝛿 𝑆𝑖30 = [( 𝑆𝑖30 𝑆𝑖28⁄ )𝑆𝑎𝑚𝑝𝑙𝑒

( 𝑆𝑖30 𝑆𝑖28⁄ )𝑁𝐵𝑆−28

⁄ − 1]

× 1000

where the (30Si/28Si)NBS-28 = 0.0341465 ± 0.0000015 11 was used

to calculate the “measured” 𝛿30Si of each sample.

The NSB-28 quartz is distributed by the National Institute of

Standards and Technology. It is commonly-utilized as the

reference material for both Si and O isotope analysis, with O

isotope analysis giving a bulk value of 𝛿18OVSMOW = 9.57 ± 0.20 ‰

(2SD).12 Perhaps more importantly, it is the globally accepted

primary standard for Si isotopic compositions. Therefore, NBS-28

𝛿30Si ≡ 0.

The Qinghu-Qtz quartz, with dozens of grams in weight and ~

500 µm in average grain size, was separated from the Qinghu

monzonite pluton in the southwestern Nanling Range, South

China.13 Previous analyses show that it is homogenous in oxygen

isotopes, with 𝛿18OVSMOW = 8.49 ± 0.20 ‰ (2SD).14

Glass-Qtz is a fused quartz glass that was produced in the

Jingshi Company (Taicang, Jiangsu Province, China) by fusing

high-purity quartz sand at 1700 °C.14 It was used as an in-house

standard for oxygen isotope microanalysis at IGG-CAS,14 with

𝛿18OVSMOW value of 1.68 ± 0.08 ‰ (2SD). The glass has a grain

size of several centimeters, and there are dozens of grams available

of the standard. It was crushed into shards of several hundreds of

microns for SIMS analysis.

The NIST-certified reference material SRM 610 glass (NIST-

610) was also analyzed in this study to monitor the external

reproducibility of the instrument and to evaluate the IMF variation

between different matrices. Literature values for this RM are

𝛿18OVSMOW = 10.91 ± 0.18 ‰ (2SD)15 and 𝛿30SiNBS-28 = 0.03 ±

0.11 ‰ (2SD).16

Qinghu-Zir and Penglai-Zir zircons are two Chinese National

Certified Reference Materials, with reference numbers

GBW04705 and GBW04482, respectively. The Qinghu-Zir

zircon was separated from the Qinghu quartz monzonite. It has

long been used as a standard for zircon U-Pb dating.13 It is also

homogenous in oxygen isotopic compositions, with 𝛿18OVSMOW =

5.39 ± 0.22 ‰ (2SD).13 The Penglai-Zir zircon standard is picked

from zircon megacrysts (ranging between several millimeters and

over one centimeter in size) from the Early Pliocene alkaline

basalts in northern Hainan Island, South China.17 It has been used

as a standard for zircon O-Hf isotope microanalysis, with

𝛿18OVSMOW = 5.31 ± 0.10 ‰ (2SD) and 176Hf/177Hf = 0.282906 ±

0.0000010 (2SD).17

NIST-610 glass shards and NBS-28, Glass-Qtz, and Qinghu-

Qtz quartz grains were embedded on the surface of one-inch epoxy

resin Mount A. Qinghu-Zir, and Penglai-Zir zircons were cast in

Mount B. In addition, a large piece (centimeter-sized) of NIST-610

was selected and prepared in Mount C. To minimize the position

effect (X-Y effect),7 all analysis points were selected within a 12-

mm diameter area. The sample mounts were polished with 3000

and 5000 mesh sandpaper successively prior to SIMS analysis. A

polishing paste was avoided throughout sample mount preparation

100


to reduce the "topography effect” influence. The polished mounts

were cleaned several times using alcohol and deionized water.

Afterward, the mounts were dried and gold-coated under vacuum,

then placed in the vacuum chamber of the SIMS for ~ 24 hours

before analysis to reduce the interference of hydrides.

EXPERIMENTAL METHODS

In situ Si isotope measurements using SIMS. In situ Si isotope

measurements in this study were conducted on the Cameca IMS-

1280 Large geometry Multi-Collector SIMS, located at the

Institute of Geology and Geophysics, Chinese Academy of

Sciences, in Beijing.

To overcome the low yield of silicon signal from quartz and

zircon in SIMS analysis, a ~15 µm Cs+ primary beam with an

intensity of 7 to 12 nA was tuned at the potential of +10 kV. A ~20

µm raster size was employed to avoid a deep crater. A high-density

electron beam from a normal incidence electron gun (NEG) was

tuned to obtain a maxim emission current of ~2.3 mA. During

analysis, the entrance slit of 200 µm and field aperture of 5500 µm

were used. Two Faraday cups located at the L'2 and H1 positions

of the multi-collector system were used to measure the 28Si and 30Si signals simultaneously, connected to the pre-amplifiers with

resistors of 1010 and 1012 Ω, respectively. Both detectors were

equipped with a ~700 µm exit slit to obtain a broad flat top mass

peak. Based on different NEG tunings, the typical secondary ion

yield of 28Si was 5.4 to 7.1×107 cps/nA for quartz samples and 3.6

to 4.4×107 cps/nA for the zircon samples, respectively.

With a data integration time of 120 s, the total analytical time of

each analysis was ~ 4.5 min, which included a pre-sputtering of 30

s to ensure a stable secondary ion intensity. Before data acquisition,

the secondary ion beam centering procedure, including the

dynamic transfer field aperture (DTFA-X and DTFA-Y) and the

DTCA-X, was applied automatically to minimize the influence of

the "topography effect". Drift correction against time and DTCA-

X correction9 were performed when necessary. Our previous

study9 showed that using the linear relationship between the

measured 𝛿30Si value and the DTCA-X parameters in the same

session to correct the obtained Si-isotope data can effectively

improve the accuracy and precision of in situ Si isotope analysis.

Specific principles and detailed correction methods can be found

in Liu et al.9 Our measured Si isotopic compositions were

normalized to the value from the S-MC-ICP-MS analysis to

compare the results from different analytical sessions.

During in situ silicon isotope analysis using SIMS, the apparent

mass resolution (MRP) M/∆M was ~1470 (~10 % peak height)

with an ∆M of about 0.020 a.m.u., due to the exit slit setting of 700

µm. Thus, the 29Si1H− hydride peak was challenging to separate

Fig. 1 SIMS mass spectrum of 30Si−

on quartz in this study, the peaks are

30Si−, 29Si1H−, and 28Si1H2−

from left to right. The magnetic field was set to

the left quarter of the main peak to avoid interference from hydrides.

from the 30Si− signal effectively. As shown in Fig.1, the lower

intensity peak on the right side of the 30Si− was 29Si1H−, while the

lowest intensity was 28Si1H2−. The magnetic field was locked at the

left quarter of the flat top of the 30Si− signal. Under this condition,

the 29Si1H− intensity was dropped by more than two orders of

magnitude. Thus, the influence of its tailing on 30Si− was about ten

ppm, which can be ignored during the measurement of 30Si−.

Bulk analysis of Si isotopes using S-MC-ICP-MS. Two multi-

collector mass spectrometer laboratories, one in the School of

Earth and Environmental Sciences, University of St Andrews

(UA), and one in the Chinese Academy of Sciences' Key

Laboratory of Crust-Mantle Materials and Environments at the

University of Science and Technology of China (USTC), were

involved in the Si isotope characterization of the geo standards via

S-MC-ICP-MS. The two quartz samples, Qinghu-Qtz and Glass-

Qtz, were measured in both laboratories. The two zircon samples,

Qinghu-Zir and Penglai-Zir, were determined at UA. A brief

description of the methods is given below.

At St Andrews, all samples were dissolved prior to Si isotope

purification using the NaOH alkali fusion method, first described

by Georg et al.3 Briefly, the mineral separates were powdered to a

smaller grain size, and then between 2 and 10mg of sample

powder was weighed into a high-purity (>99%) silver crucible.

The NaOH flux (semi-conductor grade, Merck) was then added to

the crucible in pellet form – usually, a minimum of 100mg and a

maximum of 200mg of flux was utilized. The crucible containing

the sample powder and flux was then placed in a muffle furnace,

preheated to 720°C, and left for around 15 minutes for the fusion

to take place. Following the fusion, the crucibles were removed

from the furnace and quenched in MQ-e water (in 30ml PFA vials)

and left overnight. Subsequently the fusion cake was transferred

to pre-cleaned storage bottles, diluted with enough MQ-e water to

result in a solution of between 10 and 25ppm Si, and acidified with

101


enough conc. high purity HNO3 to both neutralize the NaOH and

reduce the pH of the solution to ~ 2.

The sample Si was purified for S-MC-ICP-MS analysis using

the single-stage cation exchange procedure first described in

Georg et al.3 and further discussed in Savage and Moynier

(2013).18 Here, enough sample solution containing 20µg of Si was

loaded into a BioRad Polyprep column containing 1.8 mL of

precleaned AG50W-X12, 200–400 mesh cation exchange resin,

Bio-Rad, USA. The Si, at low pH, is in the neutral H4SiO4 or

anionic H3SiO4− forms and so can be eluted immediately using

5ml of MQ-e water (whereas matrix cations are retained on the

resin). Once the Si was eluted, it was acidified to 0.22M HNO3

before MC-ICPMS analysis. Silicon yield from the columns is

always >99% and total procedural blanks are ≤ 50ng of Si, which

is negligible compared to the 20µg of Si in sample.

Si isotopes were measured on a Thermo-Fischer Neptune-Plus

MC-ICP-MS instrument. Samples were introduced into the

instrument via an ESI PFA 75µl min-1 Microflow nebulizer, into

the Thermo SIS spray chamber. The instrument was fitted with

nickel “Jet” sampler and H-skimmer cones and operated at

medium mass resolution (M/∆M ≈ 7500) to resolve the large

molecular interferences on the 29Si and 30Si isotope beams. Sample

isotope ratios were measured using the “sample-standard

bracketing" procedure, using NBS-28 as the bracketing standard.

Isotope beams were measured in the L3 (28Si), C (29Si), and H3

(30Si) Faraday collectors and with 25 cycles of ~8 sec integrations,

bracketed by on-peak blank measurements. The blank corrections,

isotope ratios and δ30Si and δ29Si values were calculated offline.

At USTC, the sample powder mixed with high purity NaOH

powder was heated in a silver crucible at 720 for 10 min to

produce a water-soluble metastable silicate. The crucible was kept

at the temperature of 80 for 12 h on a hotplate. Nitric acid was

added to the sample solution to attain a solution acidity of 1%

HNO3 (v/v) for column chemistry. A cation exchange resin (2 mL

of AG50W-X12, 200–400 mesh, Bio-Rad, USA) was used to

purify Si. The sample solution, containing ~30 μg of Si, went

through the column after the resin was sequentially cleaned with 3

mol L−1 HNO3, 6 mol L−1 HNO3, and 6 mol L−1 HCl and then

conditioned with water. Silicon was collected right after the

sample solution was loaded and was further eluted with 6 mL of

water. The yield of Si was >99%, and the total procedural blank

was ~20 ng, which is negligible relative to the ~30 μg of Si loaded

into the column.

Silicon isotope ratios were analyzed with an MC-ICP-MS

instrument (Neptune Plus from Thermo-Fisher Scientific). During

analysis, nickel cones (H skimmer and Jet sampler, Thermo-Fisher

Scientific) were used. Three stable isotopes of Si (28Si, 29Si, and 30Si) were collected using FCs on L3, C, and H3, respectively, at

Fig. 2 External reproducibility of δ30Si on NIST-610 using SIMS. The data

were collected on a piece of centimeter-sized NIST-610 over 4 analytical

sessions.

a medium resolution (>5500). The mass bias of the instrument

during isotope measurements was corrected using the sample–

standard bracketing method.

RESULTS

External reproducibility of SIMS Si-isotope microanalysis. A

large piece of NIST-610 glass on Mount C was determined to

check the “best” external reproducibility of SIMS Si-isotope

analysis using the current instrument settings. In the analytical

period from February to March 2021, four repeated experiments

were carried out on NIST-610 glass. After rejecting six discrete

points with a deviation larger than 2 SD, the remaining 90 Si-

isotopic measurements were included in the statistics (listed in

Table S1 and plotted in Fig. 2). After normalizing to the reference

value (−0.03 ‰),19 the overall external error of the 90 spot

measurements is 0.072‰ (all uncertainties throughout the text are

quoted at the 2 SD level unless otherwise indicated). These data

show improved external reproducibility compared with those

reported in the previous study (~0.11 ‰).9

Si-isotopic homogeneity at micron scales. In situ silicon isotopic

compositions of samples on Mount A and B (including NIST-610,

NBS-28, Glass-Qtz, Qinghu-Qtz, Qinghu-Zir, and Penglai-Zir)

were determined over several analytical sessions, resulting in over

1100 data (Table S2). The samples on Mount A were determined

concurrently over ten sessions (NIST-610 was only determined in

five sessions), and the zircon samples on Mount B were measured

separately. To study the homogeneity of each sample, the data was

organized primarily by sample. The internal error of each Si

isotope spot measurement (Table S2) includes the SIMS counting

statistic error and propagation of the “best” external error of the

NIST-610 glass standard (0.072 ‰, see Section 4.1). The IMF

102


Table 1. Summary of SIMS 𝛿30Si homogeneity check of the six samples

Sample

Primary

beam

(nA)

Sessions n 2SD (‰)

over all

2SD (‰)

range in each

session

Glass-Qtz 7.3-10.9 10 234 (-7) 0.090 0.06-0.13

Qinghu-Qtz 7.4-10.9 10 207 (-6) 0.102 0.06-0.14

NBS-28 7.4-10.1 10 181(-7) 0.160 0.11-0.31

NIST-610 9.4-10.7 5 85(-4) 0.112 0.07-0.12

Qinghu-Zir 6.0-13.1 7 260 (-4) 0.094 0.07-0.13

Penglai-Zir 8.1-15.7 5 148 (-3) 0.096 0.08-0.10

Fig. 3 δ30Si homogeneity analysis of Glass-Qtz, Glass-Qtz, NBS-28, NIST-

610, Qinghu-Zir, and Penglai-Zir. The data in Table S2 were used.

Table 2. Solution MC-ICP-MS data summary of Glass-Qtz, Qinghu-Qtz,

Qinghu-Zir, and Penglai-Zir

Sample δ30Si 2SD 2SE δ29Si 2SD 2SE n

Glass-Qtz

USTC_1* -0.09 0.02 0.01 -0.06 0.02 0.01 6

USTC_2 -0.12 0.02 0.01 -0.06 0.05 0.03 3

USTC_3 -0.12 0.01 0.01 -0.05 0.07 0.04 3

USTC_4 -0.11 0.05 0.02 -0.04 0.03 0.01 6

UA** -0.09 0.06 0.03 -0.04 0.03 0.01 5

Grand Average -0.10 0.04 0.01 -0.05 0.04 0.01 23

Qinghu-Qtz

USTC_1 -0.02 0.05 0.02 0.00 0.05 0.02 6

USTC_2 -0.04 0.05 0.02 -0.02 0.08 0.03 9

UA** -0.03 0.05 0.02 -0.02 0.02 0.01 5

Grand Average -0.03 0.05 0.01 -0.01 0.06 0.01 20

Qinghu-Zir UA** -0.45 0.06 0.03 -0.25 0.03 0.02 5

Penglai-Zir UA** -0.34 0.06 0.03 -0.19 0.04 0.02 5

* Represents the data from the University of Science and Technology of China.

** Represents the data from School of Earth and Environmental Sciences, University of St Andrews.

corrected results were normalized to their reference values (or S-

MC-ICP-MS determined value). The results are summarized in

Table 1 and shown in Fig. 3.

A total of 234 SIMS Si-isotope measurements were performed

for Glass-Qtz quartz within ten sessions. The overall external

reproducibility of 0.09 ‰ was achieved, with a rejection of seven

measurements beyond the 2 SD error range. Similarly, 207 Si-

isotope measurements were collected for the Qinghu-Qtz sample

within ten analytical sessions. After six analyses were rejected, the

reproducibility of the remaining analysis was 0.10 ‰. Within the

ten sessions, a total of 181 spot analyses were conducted on NBS-

28 quartz. With seven outlined, the external reproducibility was

0.16 ‰, similar to those reported in the previous works.9, 10 Within

five sessions, 81 out of 85 analyses on NIST 610 glass gave

external reproducibility of 0.11‰.

In situ silicon isotopic compositions of the Qinghu-Zir zircon

were measured in seven sessions. A total of 260 (4 rejected for the

significant deviation and abnormal internal precision) data sets

yielded an external reproducibility of 0.094 ‰. A total of 148 (3

rejected) SIMS Si-isotopic analyses were conducted on the

Penglai-Zir zircon in five sessions, with an external reproducibility

of 0.096 ‰.

Si-isotope results using the S-MC-ICP-MS method. All Si-

isotope data determined by S-MC-ICP-MS are listed in Table 2

and Table S3. To monitor the accuracy and reproducibility of Si

isotope data, some external standards, including Diatomite,

BHVO-2, AGV-2 and BCR-2, were analyzed in both UA and

USTC. The δ30Si and δ29Si values of these standards agree well

with literature data within error (Table S3) and demonstrate that

the analytical methods used by two laboratories can achieve good

levels of precision and reproducibility.

Both USTC and UA laboratories gave consistent Si isotopic

results for the Glass-Qtz and Qinghu-Qtz quartz samples. For

Glass-Qtz quartz, the δ30Si and δ29Si value obtained at USTC were

−0.11 ± 0.04 ‰ and −0.05 ± 0.04 ‰ (n = 18), identical within

errors with those obtained at UA (δ30Si = −0.09 ± 0.06 ‰, and

δ29Si = −0.04 ± 0.03 ‰, n = 5). The grand mean of the data sets

gives δ30Si and δ29Si of −0.10 ± 0.04 ‰ and −0.05 ± 0.04 ‰ (n =

23). Similarly, the δ30Si and δ29Si values for the Qinghu-Qtz quartz

obtained from the two laboratories are identical within errors, with

a grand mean of −0.03 ± 0.05 ‰ and −0.01 ± 0.06 ‰ (n =20),

respectively.

DESCUSSION

New reference materials of Si isotope microanalysis. The NIST-

610 glass standard has previously been shown to be homogeneous

with respect to both chemical and isotopic compositions9, 20. The

NIST-610 glass shard we used on mount C is larger than 10 mm

for SIMS Si isotopic microanalysis, effectively eliminating the

“topography effect” of SIMS measurement. Our SIMS Si isotopic

results of NIST-610 give the internal and external (spot-to-spot)

103


precision of ~0.05 ‰ and ~0.07 ‰ (Fig. 2), respectively,

representing the current “highest level” of precision and accuracy

of SIMS Si microanalysis.

Figure 3 summarizes the Si-isotope homogeneity of different

samples at micron scales using SIMS analysis, including two

quartz crystals, one fused glass O-isotope standard, the NIST-610

glass, and two zircon age and O-Hf isotope standards. All analyses

of six standards show Gaussian distributions, suggesting they are

homogeneous in Si isotopes. The Glass-Qtz, Qinghu-Qtz, Qinghu-

Zir, Penglai-Zir, and NIST-610 standards have the external

precision of 0.090 ‰, 0.102 ‰, 0.094 ‰, 0.096 ‰, and 0.112 ‰,

respectively, suggesting that all of them are suitable for the Si

isotope microanalysis. Among them, the Glass-Qtz quartz has the

best level of homogeneity of Si isotopes. Based on S-MC-ICP-MS

analyses, the δ30Si value of −0.10 ± 0.04 ‰ (2SD), −0.03 ± 0.05 ‰

(2SD), −0.45 ± 0.06 ‰ (2SD), and −0.34 ± 0.06 ‰ (2SD) are

recommended for Glass-Qtz, Qinghu-Qtz, Qinghu-Zir, and

Penglai-Zir, respectively. Considering Qinghu-Qtz quartz and

Qinghu-Zir zircon are separated from the same rock, we can

calculate Si-isotope fractionation between quartz and zircon:

δ30Si(qtz) – δ30Si(zir) = ∆30Si(qtz-zir) = 0.42 ± 0.06 ‰, which

corresponds to ~671 oC based on the first‑principles calculation of

equilibrium fractionation of Si isotopes in quartz and zircon

system.21 This Si-isotope thermometry result agrees with the O-

isotope equilibrium temperature of 656 ± 62 oC in quartz and

zircon,22 further supporting the reliability of our Si isotope results.

Compared with the NIST-610 glass standard, quartz, and zircon

standards described above, our NBS-28 quartz analyses provide a

much poorer external precision of about 0.16 ‰ for Si isotopes at

the micron scale, similar to those reported in previous works9, 10.

Therefore, we posit that NBS-28 is not an ideal standard for high-

precision Si isotopic microanalysis; although it is likely

homogeneous in Si isotopes at the milligram level typically

processed for S-MC-ICPMS measurements, we suggest that a

suite of well-characterized standards (such as those described

above) that are homogeneous at the micron level vs. NBS-28 in

terms of its Si isotope composition are more ideal for SIMS 𝛿30Si

and 𝛿29Si measurements.

Glass-Qtz as an alternative standard for precise SIMS quartz

Si isotopic microanalysis. Our SIMS analytical results

demonstrate that the Glass-Qtz quartz has excellent homogeneous

Si-isotopic compositions. However, it is noteworthy that the

structure of synthetic uncrystallized quartz glass is different from

that of natural crystallized quartz crystals. Therefore, when

assessing the use of a synthetic quartz standard (such as Glass-Qtz

quartz) for bracketing natural SIMS quartz Si-isotope analysis, we

must consider the potential for a matrix effect between the two. As

described in experimental section, all quartz standards Glass-Qtz,

Qinghu-Qtz, NBS-28, and NIST-610 were concurrently measured

using SIMS. These analytical results can be used to investigate the

Table 3. The IMF determination between the natural quartz samples and

the synthetic samples in ten sessions

Sample

name

Mean value of

𝛿30Si

Calculated

IMF

ΔIMF 2SD

Session 1

25/08/2020

Glass-Qtz -0.30 -0.20 0.00 0.06

NBS28 -0.18 -0.18 0.02 0.13

Qinghu -0.22 -0.19 0.00 0.11

Session 2

28/08/2020

Glass-Qtz -0.15 -0.05 0.00 0.11

NBS28 -0.09 -0.09 -0.04 0.11

Qinghu -0.08 -0.05 0.01 0.12

Session 3

06/10/2020

Glass-Qtz -0.01 0.09 0.00 0.08

NBS28 0.07 0.07 -0.01 0.15

Qinghu 0.02 0.05 -0.04 0.11

Session 4

7/16/2020

Glass-Qtz -0.70 -0.60 0.00 0.13

NBS28 -0.59 -0.59 0.02 0.14

Qinghu -0.51 -0.48 0.13 0.14

Session 5

31/01/2021

Glass-Qtz 0.38 0.48 0.00 0.11

NBS28 0.45 0.45 -0.03 0.22

Qinghu 0.47 0.50 0.02 0.12

NIST610 8.05 8.08 7.60 0.12

Session 6

06/02/2021

Glass-Qtz -0.11 -0.01 0.00 0.08

NBS28 -0.09 -0.09 -0.08 0.19

Qinghu -0.05 -0.02 -0.01 0.10

NIST610 7.49 7.52 7.53 0.11

Session 7

07/02/2021

Glass-Qtz -0.76 -0.66 0.00 0.10

NBS28 -0.68 -0.68 -0.03 0.18

Qinghu -0.66 -0.63 0.03 0.10

Session 8

24/02/2021

Glass-Qtz -0.13 -0.03 0.00 0.06

NBS28 -0.02 -0.02 0.00 0.15

Qinghu -0.07 -0.04 -0.02 0.06

NIST610 7.58 7.55 7.57 0.08

Session 9

25/02/2021

Glass-Qtz 0.02 0.12 0.00 0.08

NBS28 0.14 0.14 0.02 0.15

Qinghu 0.09 0.12 0.00 0.08

NIST610 7.84 7.87 7.75 0.12

Session 10

18/03/2021

Glass-Qtz 0.12 0.22 0.00 0.08

NBS28 0.20 0.20 -0.02 0.11

Qinghu 0.18 0.21 0.00 0.11

NIST610 7.94 7.97 7.75 0.07

feasibility of using Glass-Qtz quartz glass as an alternative

standard for precise SIMS quartz Si isotopic microanalysis.

Reference values of NBS-28 (𝛿30Si = 0 ‰) and NIST-610 glass

(−0.03 ‰)19, and our newly obtained 𝛿30Si values for Qinghu-Qtz

(−0.03 ‰) and Glass-Qtz (−0.10 ‰) are used as recommended

values. The difference between the calculated IMF and the

reference value (IMFSIMS− IMFRef) was defined as ΔIMF hereafter.

Taking the IMF value of Glass-Qtz as the benchmark (ΔIMF=0)

and the external standard deviation as the corresponding ΔIMF

error, the ∆IMFs of the other three standards were calculated.

Analytical data can be found in Table S4, and the results are

summarized in Table 3 and plotted in Fig. 4. According to the

calculated results of data sets from 10 sessions, the SIMS Si-

isotope values of Qinghu-Qtz and NBS-28 natural quartzes are

consistent within analytical errors with those obtained by the S-

MC-ICP-MS method. The average ∆IMF of Qinghu-Qtz and

NBS-28 were 0.00 ± 0.04 ‰ (with session four rejected) and

−0.01 ± 0.06 ‰, respectively, which indicates that no matrix effect

between quartz glass (Glass-Qtz) and natural quartz crystal can be

detected under current external reproducibility (~0.1 ‰，2SD) of

104


Fig. 4 The ΔIMFSi of the silicon isotope compositions between quartz and

glass were determined in ten sessions using SIMS. There is no detectable

matrix effects between the synthetic quartz glass (Glass-Qtz) and two

natural quartz samples (NBS-28 and Qinghu-Qtz) within the external

precisions.

SIMS Si-isotope analysis.

On the contrary, the NIST-610 glass has an ∆IMF value ranging

from 7.53 to 7.75 ‰, which is neither close to zero nor constant.

Although NIST-610 glass is considerably homogenous in Si

isotopes on the micron scale, it is not suitable as an RM for SIMS

Si isotope microanalysis.

CONCLUSIONS

In this study, we improve the external reproducibility of SIMS Si-

isotope microanalysis from 0.11 ‰ (2SD)9 to 0.07 ‰ (2SD) for

large shards of NIST 610 glass. Four standards previously utilized

for SIMS O-isotope microanalysis, including Glass-Qtz, Qinghu-

Qtz, Qinghu-Zir, and Penglai-Zir, are herein proved to be

homogeneous in their Si-isotopic compositions at the micron scale,

and their Si-isotopes have been precisely determined by S-MC-

ICP-MS. Thus, they can be used as standards for both O- and Si-

isotope microanalysis, allowing the SIMS isotope data to be recast

relative to VSMOW and NBS-28. We also demonstrate that there

is no detectable matrix effect of SIMS Si isotope microanalysis

between the synthetic quartz (Glass-Qtz) and natural quartz

standards at the 0.1‰ precision level. Therefore, the synthetic

Glass-Qtz quartz glass can be used as an alternative RM for precise

SIMS quartz Si isotope analysis.

ASSOCIATED CONTENT

The supporting information (Tables S1-S4) is available at www.at-

spectrosc.com/as/home

AUTHOR INFORMATION

Xian-Hua Li received his BSc in 1983

from the University of Science and

Technology of China, and PhD in 1988

from the Institute of Geochemistry,

Chinese Academy of Sciences (CAS).

He is a research professor of

geochemistry at the Institute of Geology

and Geophysics, CAS and the chief

professor of geochemistry in the

University of CAS. His major research

interests are isotope geochronology,

geochemistry, isotopic microanalysis and

their applications to igneous petrogenesis,

chemical geodynamics and evolution of Earth and Moon. He has been

working as editor-in-chief of Atomic Spectroscopy, associate editor of

Precambrian Research, Geological Magazine, and American Journal of

Science, and member of editorial committee in many other academic

journals. Xian-Hua Li is author or co-author of over 400 articles published

in peer-reviewed scientific journals, with an h-index of 101 (Web of

Science). He was elected as a Fellow of the Geological Society of America

in 2007, Academician of CAS in 2019, and named as Clarivate Analytics

Global Highly Cited Scientist, ESI Global Highly Cited Scholar and

Elsevier China Highly Cited Scholar.

Corresponding Author

*X.-H. Li

Email address: [email protected]

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS

We thank Hong-Xia Ma for her excellent sample preparation work.

We also thank two anonymous reviewers for their helpful

comments, which greatly improved this manuscript. This work

was financially supported by the National Key R&D Program of

China (2018YFA0702600) and National Science Foundation of

China (41890831).

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Continuous Online Leaching System Coupled with Inductively

Coupled Plasma Mass Spectrometry for Assessment of Cr, As, Cd,

Sb, and Pb in Soils

Alastair Kierulf,a Michael Watts,b Iris Koch,c and Diane Beauchemina,*

aDepartment of Chemistry, 90 Bader Lane, Queen’s University, Kingston, Ontario, K7L 3N6, Canada

bInorganic Geochemistry, Centre for Environmental Geochemistry, British Geological Survey, Nottingham, NG12 5GG, UK

cDepartment Chemistry and Chemical Engineering, Royal Military College of Canada, 12 Verité Avenue, P.O. Box 17000 Station Forces, Kingston, ON,

Canada

Received: August 20, 2021; Revised: October 29 2021; Accepted: November 01, 2021; Available online: November 08, 2021.

DOI: 10.46770/AS.2021.827

ABSTRACT: Incidental ingestion of soil containing Cr, As, Cd, Sb, and Pb has been attracting global attention as it can

significantly impact human health. Many bioaccessibility methods have been developed to simulate the amount of contaminants

extracted by gastrointestinal fluids following incidental ingestion. Although the continuous online leaching method (COLM) offers

various advantages over conventional batch bioaccessibility methods, such as reduced analysis time, elemental source apportionment,

and isotopic analysis, it has not yet been applied to soil and directly compared to validated, published methods. This study uses the

COLM with simulated gastrointestinal fluids from the United States Environmental Protection Agency (US EPA), United States

Pharmacopeia (USP), and unified bioaccessibility method (UBM) to measure the bioaccessibility of Cr, As, Cd, Sb, and Pb in NIST

2710, NIST 2710a, NIST 2711a, and BGS 102. When the US EPA gastrointestinal fluid was used, no significant difference was

observed between the COLM bioaccessible + residual, aqua regia extraction, or certificate concentrations for all the elements and

soils studied. Furthermore, COLM bioaccessibility was within the

acceptable range of control limits and bioavailability (animal)

studies for most reference materials. In addition, no statistically

significant difference was observed between either the US EPA

batch method or the stomach phase of the UBM batch method and

the stomach stage of the COLM, indicating that the COLM could

be incorporated into current bioaccessibility analyses to improve

soil contamination characterization in the future.

INTRODUCTION

Cr, As, Cd, Sb, and Pb can be toxic, carcinogenic, or have other

neurological or adverse health effects, and therefore, their presence

in soil can adversely affect humans, other animals, and the natural

environment.1 Cd, for instance, can cause nephrotoxicity in

humans. Cr and Sb have been observed to have adverse health

effects in rats. Pb can cause neurotoxicity in children. As is a

known carcinogen.1 In addition, Sb and Cr have been recognized

as risk drivers in human health risk assessment. Various methods

have been validated to determine As, Pb, and Cd in soils. Some

soil types are naturally rich in one or more of these elements, while

others can be contaminated with these elements through human

activities. Therefore, the United Nations Sustainable Development

Goals aim to reduce illness and death resulting from hazardous soil

pollution and contamination (Target 3.9),2 provide access to safe

green and public spaces (Target 11.7), reduce the release of

chemicals into soil and their adverse impacts on the human health

and environment (Target 12.4), and restore degraded land and soil

to achieve a land degradation-neutral world (Target 15.3). Over

1300 active contaminated Superfund sites have been identified by

the United States Environmental Protection Agency (US EPA).3

The Federal Contaminated Sites Action Plan has identified 4982

active contaminated sites in Canada.4 Across Europe, 340,000


contaminated sites will likely require remediation according to the

European Environment Agency.5 To facilitate the remediation of

these sites and assessment of other sites, agencies often perform a

human health risk assessment.

Although humans can be exposed to Cr, As, Cd, Sb, and Pb in

soils through dermal absorption or inhalation, the most significant

exposure occurs through incidental ingestion.6 One of modeling

this ingestion is by using bioaccessibility methods.

Bioaccessibility is the amount made available for absorption into

the circulatory system by the gastrointestinal fluids, whereas

bioavailability is the amount actually absorbed into the

bloodstream (Fig. S1). Bioavailability methods are time-

consuming and expensive. In addition, animal subjects (such as

pigs, mice, or monkeys)7-9 need to be fed contaminated soils to

evaluate the uptake and excretion of Cr, As, Cd, Sb, and Pb, which

is conducted by analyzing their urine, feces, blood, and sometimes

tissues. By contrast, bioaccessibility methods use artificial

gastrointestinal fluids to assess the solubility of elements, which

theoretically estimates the maximum possible bioavailable

concentration. The relevance of such methods is established

through a strong correlation between bioaccessibility and

bioavailability, i.e., their validation, according to specific

criteria.10,11

The US EPA employs a method in which samples are agitated

for 1 h in simulated gastric fluids at 37 °C.12 This method is widely

used and has been validated for Pb13 and As.14,15 It has also been

used to predict the relative bioavailability from bioaccessibility

measurements. Many studies have also utilized a method based on

the United States Pharmacopeia (USP)16 that incorporates artificial

saliva, gastric, and intestinal fluids.17–19 In addition, the

Bioaccessibility Research Group of Europe has developed a

method called the unified bioaccessibility method (UBM), in

which samples are mixed for 1 h with simulated saliva and gastric

fluids, and then subjected to an additional 4 h of mixing in

duodenal and bile fluids.20 This method has also been adopted by

the International Organization of Standardization (ISO) and

validated to assess the oral bioavailability of Pb, As, and Cd in an

interlaboratory trial.21

The methods discussed above use certified reference materials

(CRMs) for quality control and validation studies. Some

commonly used CRMs for these purposes are produced by the

National Institute of Standards and Technology (NIST),

specifically NIST 2710, NIST 2710a, and NIST 2711a.

Additionally, the British Geological Survey (BGS) produced BGS

102, a reference material that has been extensively studied for use

with the UBM.21–24 The published bioavailability results and

historic use of these CRMs with validated methods are important

for assessing non-validated methods.

All these methods employ inductively coupled plasma mass

spectrometry (ICPMS) for elemental analysis because of its high

sensitivity, low detection limits, and a large linear dynamic

range.25 Although ICPMS can be sensitive to matrix effects and

spectroscopic interferences,26 flow injection (FI) can reduce these

matrix effects,27 and inert (He) or reactive (H2) gases can react with

possible interfering ions in a collision reaction cell or a collision

reaction interface (CRI). While these approaches can facilitate

analyte detection, they can also lower the sensitivity.28

A significant drawback of the aforementioned bioaccessibility

methods is that they require a long extraction time prior to ICPMS

analysis, which can be avoided by using the continuous online

leaching method (COLM). To use the COLM, the sample is placed

in a mini-column. Each gastrointestinal fluid is then sequentially

pumped through the mini-column and directly to the nebulizer of

the instrument. The gastrointestinal fluids and mini-column are

then submerged in a water bath maintained at 37 °C to simulate

the average human body temperature. Fig. 1 illustrates the COLM

setup with an ICPMS detector and a low-pressure manifold. Since

the gastrointestinal fluid is directly sent to the ICPMS instrument,

the sample-handling is greatly reduced, which in turn reduces any

potential contamination. Additionally, the extracts produce

transient time-resolved peaks that provide real-time information

on the leaching kinetics of elements and allow for the analysis of

multiple gastrointestinal fluids in under 30 min.17 These temporal

profiles can allow the identification of different sources of

elements, as differential leaching can be observed in real time.17 In

addition, it allows isotopic source apportionment, which can help

Fig. 1 Schematic representation of the continuous online leaching method (COLM) using a low-pressure manifold.


identify the source of the contaminants (for instance, from Pb

added to gasoline).29,30 The COLM has been used for seafood,18

wheat,31 bread,32 and rice;33 however, it has not yet been used for

soils, or directly compared to other validated bioaccessibility

methods.

Unlike food samples, soils contain refractory components. This

study aims to verify whether the COLM can be successfully

applied to soils. It also directly compares the COLM with other

soil bioaccessibility methods, including the validated US EPA

method, USP method, and validated UBM, in order to justify its

continued and wide-spread application.

EXPERIMENTAL

Certified reference materials. Four CRMs, representing soils

typically used as controls in bioaccessibility studies, were selected

for the study as they have available bioavailability results for

comparison: NIST 2710, NIST 2710a, NIST 2711a, and BGS

102.The details of these CRMs can be found in Table S1. They

were mixed prior to weighing (0.5–1.0 g, depending on the

method). When not in use, the CRMs were stored in the dark at

room temperature.

Reagents. All methods used doubly deionized water (DDW)

purified to a resistivity of 18.2 MΩ cm using either an Arium Pro

UV/DI (Sartorius Stedim Biotech, Göttingen, Germany) or Milli-

Q Direct 8 (Millipore Sigma, Burlington, MA, USA) water

purification system. The acids were of American Chemical

Society (ACS) reagent grade. Analytical-grade HCl and HNO3

were used for the UBM. A DST-1000 sub-boiling distillation

system (Savillex, Minnetonka, MN, USA) was used for the

purification of acids for the aqua regia extraction.

The USP bioaccessibility fluids were prepared in accordance

with previous studies17,34 and analyzed using the COLM. Enzymes

were omitted from the stock solutions and added only on the day

of analysis. For saliva stock, 0.896 g of KCl (ACS grade; Sigma-

Aldrich), 0.200 g of KSCN (ACS grade; Sigma-Aldrich), 0.888 g

of NaH2PO4 (ACS grade; Sigma-Aldrich), 0.570 g of Na2HPO4

(ACS grade; Sigma-Aldrich), 0.298 g of NaCl (ACS grade;

BioShop, Burlington, ON, Canada), 0.072 g of NaOH (ACS grade;

BioShop), 0.200 g of urea (ACS grade; Sigma-Aldrich), 0.015 g

of uric acid (ACS grade; Sigma-Aldrich), and 0.050 g of mucin

(ACS grade; Sigma-Aldrich) were mixed and diluted to 1 L with

DDW. The pH was maintained at 6.5 by adding 0.2 mol L-1 NaOH.

To prepare the gastric stock solution, 2.00 g of NaCl and 7.0 mL

of HCl (ACS grade; Fisher Scientific, Ottawa, ON, Canada) were

combined and diluted to 1 L with DDW and the pH was

maintained at 1.2 using HCl. To prepare the intestinal stock, 6.80

g of KH2PO4 (ACS grade; Fisher Scientific, NJ, USA) and 77 mL

of 0.2 mol L-1 NaOH were mixed and diluted to 1 L with DDW

after adjusting to pH 6.8 using NaOH. On the day of analysis,

enzymes were added by combining 250 mL of the stock with

0.0729 g of ɑ-amylase (USP grade; Sigma-Aldrich) for saliva,

0.800 g of pepsin (USP grade; Sigma-Aldrich) for gastric juice,

and 2.50 g of pancreatin (a mixture of amylase, lipase, and

protease) (USP grade; Sigma-Aldrich) for intestinal fluid.

US EPA batch method. Following the standard operating

procedure,12 three 1-g aliquots of soil were weighed into 125-mL

high-density polyethylene (HDPE) bottles. A simulated gastric

fluid was prepared by mixing 30.03 g of glycine (Reagent Plus

Grade; Sigma-Aldrich, Oakville, ON, Canada) in up to 1 L of

DDW at 37 °C and then adding HCl (ACS reagent grade; Fisher

Scientific, Saint-Laurent, QC, Canada) to obtain a pH of 1.50 ±

0.05. Then, 100 mL of gastric fluid was added to each HDPE bottle

and set to rotate end-over-end in an Innova 4230 refrigerated

incubator shaker (Eppendorf (previously New Brunswick

Scientific), Enfield, CT, USA) at 37 °C for 1 h at 60 rpm. The

sample extracts were filtered through a 0.45-µm nylon syringe

filter (National Scientific, Claremont, CA, USA) and diluted 100-

fold with DDW.

Continuous online leaching method (COLM). To overcome the

back-pressure experienced with densely packed samples18 when

1-g soil aliquots were used, a Spectra SYSTEM P4000 HPLC

pump (Thermo Fisher Scientific, Waltham, MA, USA) was used

with high-pressure metal mini-columns (emptied guard columns).

These mini-columns were 6-cm long with a 1-cm outer diameter

and 0.6-cm inner diameter. Two ~0.6-cm-diameter disks of

Whatman filter paper (Cytiva, Marlborough, MA, USA) were

placed at each end of the mini-column filled with 1 g of soil.

During the analysis, the prepared mini-columns were submerged

in a 37 °C water bath (VWR, Mississauga, ON, Canada) and the

artificial gastrointestinal fluids (also maintained at 37 °C) were

pumped through them. Elution took 5–15 min, depending on the

gastrointestinal fluid and sample. Eluents from the mini-column

were combined with an In internal standard using a Y-connector

and nebulized into the ICPMS instrument. External calibration

was performed using matrix-matched standards (prepared in each

gastrointestinal fluid) and an FI manifold with a 100-µL injection

loop. As this method was used with US EPA and USP

gastrointestinal fluids, each was denoted as COLM-G (for the

glycine-HCl fluid of the US EPA) and COLM-USP, respectively.

Unified bioaccessibility method (UBM). The UBM fluids were

prepared in accordance with ISO 17924:201820 and following a

previously reported method.24 First, 1 L of inorganic and organic

components of each gastrointestinal fluid was prepared according

to the ISO protocol and then combined in a 2-L HDPE bottle at

37 °C. For the batch method, 0.6-g aliquots of soil were combined

with 9.0 mL of saliva in a polycarbonate centrifuge tube and hand

shaken for 10 s. Then, 13.5 mL of gastric fluid was added, and the

pH was adjusted to 1.2 ± 0.5. This resulting fluid was then mixed

end-over-end in a custom-built water bath for 1 h at 37 °C. The

samples were then centrifuged at 4500 × g for 15 min, decanted,


and acidified with 9.0 mL of 0.1 M HNO3 for storage prior to

analysis. This made up the “stomach” phase. For the “stomach +

intestinal” phase, the same steps were followed as above; however,

after rotation for 1 h, the pH was maintained between 1.2 and 1.5.

Then, 27 mL of the duodenal fluid and 9 mL of the bile fluid were

added, and the pH was adjusted to 6.3 ± 0.5 by adding 37% (v/v)

HCl or 1 M NaOH dropwise. The samples were then mixed for

another 4 h in a water bath at 37 °C. Finally, the pH was recorded,

the samples were centrifuged at 4500 × g for 15 min, decanted,

and acidified with 9.0 mL of 0.1 M HNO3 for storage. All samples

were diluted 10-fold prior to the ICPMS analysis.

For the COLM-UBM, a mini-column was prepared following a

method described in previous studies. Low-pressure

polytetrafluoroethylene (PTFE) tubing, a peristaltic pump, and a

FI manifold31 were used. To reduce the back-pressure, 0.6 g soil

was used. Each mini-column had an inner diameter of 7 mm, an

outer diameter of 8 mm, and a length of 5 cm. Glass wool (Acros

Organics, Fair Lawn, NJ, USA) was cleaned overnight in 10%

(v/v) HNO3, soaked in artificial saliva to matrix-match, dried in air,

and then stored in an airtight bag. The mini-columns were

prepared by rolling 0.6 g of soil in glass wool and inserting it into

the tubing. A glass wool plug was inserted at each end to secure

the sample in place. Three blank mini-columns were prepared by

inserting glass wool without any samples. Saliva, gastric juice,

duodenal fluid, and bile fluid were sequentially pumped through

the mini-columns while continuously monitoring the eluted

elements by ICPMS.

Aqua regia extraction. Residual soil CRM from each mini-

column was placed in a PTFE digestion vessel (Savillex) and dried.

Additionally, 1 g of fresh CRM (matching that of the residual) was

weighed into a separate digestion vessel to determine the total

extractable concentration of each element. Then, 5 mL of aqua

regia (3:1 v/v HCl: HNO3) was made fresh daily and added to each

digestion vessel. Aqua regia was also added to one blank vessel

without soil. These vessels were sealed and extracted on a hotplate

at ~180 °C for 2 h. The samples were then transferred to Falcon

tubes, filtered through a 0.45-µm hydrophilic polyvinylidene

fluoride filter (Foxx Life Sciences, Salem, NH, USA), and diluted

1000-fold with DDW.

Instrumentation. Samples, excluding those extracted with the

UBM, were analyzed using a Varian 820MS quadrupole-based

ICPMS instrument equipped with a Pt sampler cone with a 0.9-

mm diameter opening and a CRI Ni skimmer cone with a 0.4-mm

diameter opening. The samples were introduced into a PTFE

concentric nebulizer and a Peltier-cooled Scott-type double-pass

spray chamber maintained at 3 °C. Torch alignment was

conducted each day using 5 µg L-1 of a tuning solution containing

Be, Mg, Co, In, Ce, Pb, and Ba in 1% HNO3. Batch

bioaccessibility, total concentrations, and residual data were

acquired in the steady-state mode with five points per peak, 20

scans per replicate, and five replicates per sample, whereas the

COLM data were acquired in the time-resolved mode with three

points per peak, one scan per replicate, and a dwell time of 80 ms.

Other operating conditions are summarized in Table S2. The

samples were analyzed with a 5 µg L-1 In internal standard added

online using a Y-connector and matrix-matched external

calibration.

Samples extracted using the UBM were analyzed with an

Agilent 7500 quadrupole-based ICPMS instrument equipped with

Ni cones and an octupole collision reaction system using He and

H2 gases for interference reduction. The samples were introduced

using a SeaSpray glass concentric nebulizer (AMETEK UK,

Leicester, UK) and a quartz Scott-type spray chamber. Torch

alignment and tuning were conducted daily using aqueous multi-

element solutions. Batch UBM samples were analyzed in the

steady-state mode and COLM-UBM data were acquired in the

time-resolved mode with 50-ms measurements and a sample-

uptake rate of 1 mL min-1. The samples were analyzed by adding

5 µg L-1 of In internal standard to the nebulizer using a T-connector

and matrix-matched external calibrations. For the UBM batch

method, 25 µg L-1 of multi-element standard solution and a major

elemental solution were analyzed at the beginning and end of the

analysis daily and after every 20 samples.

Data processing. All data were imported into Microsoft Excel

(Microsoft, Redmond, WA, USA) for further processing. For the

data acquired in the steady-state mode, the samples were internally

standard-corrected, converted from count-per-second (c/s) to mg

L-1 using external calibration, blank subtracted, and converted to

mg kg-1 using the dilution factors and original sample masses. For

all the data acquired in the time-resolved mode, elution profiles

were internal standard corrected by dividing the analyte signal by

the In signal point by point, baseline-corrected by taking an

average of the baseline on either side of the peak and subtracting

from the points along the peak. The peak areas were computed

using the following modified trapezoidal equations:35

𝑇𝑛 = ∆𝑋 [∑ 𝑓(𝑥𝑖)

𝑛

𝑖=0

−𝑓(𝑥0)

2−

𝑓(𝑥𝑛)

2]

Where ∆𝑋 = (𝑏 − 𝑎)/𝑛, Tn is the peak area, f(x0) is the signal

at the onset of the peak, f(xi) is the signal at point i along the peak,

f(xn) is the signal at the end of the peak, a is the first time point of

the trapezoid, b is the last time point of the trapezoid, and n is the

number of equally spaced trapezoids under the peak. A peak area

was obtained for each extraction, which was then converted to mg

kg-1 using external calibration and sample masses.

The percent bioaccessibility was calculated by dividing the total

bioaccessible concentration with the total concentration extracted

by aqua regia. To compare the data sets, an F test for variance was

first performed to compute the appropriate Student’s t-tests. Next,

analysis of variance (ANOVA) was conducted using the XLSTAT

add-on in Microsoft Excel.36


Table 1. Bioaccessible concentrations in mg kg-1 (mean ± standard Deviation, n=3) of Cr, As, Cd, Sb, and Pb in the stomach-equivalent phase of all

bioaccessibility methods

CRM Method Cr As Cd Sb Pb

NIST 2710 USEPA batch 0.019 ± 0.024 195.8 ± 2.0 19.69 ± 0.50 3.81 ± 0.26 4362 ± 54

COLM-G 0.019 ± 0.013 195 ± 27 19.9 ± 7.0 3.91 ± 0.55 4500 ± 2200

NIST 2710a USEPA batch 0.26 ± 0.22 309 ± 41 7.48 ± 0.98 9.9 ± 1.1 3165 ± 540

COLM-G 0.27 ± 0.46 318 ± 540 7.5 ± 6.7 10 ± 14 3400 ± 3400

NIST 2711a USEPA batch 0.072 ± 0.039 33.00 ± 0.35 57.5 ± 1.6 3.94 ± 0.46 1204 ± 23

COLM-G 0.072 ± 0.017 33.8 ± 6.0 47.5 ± 4.2 3.89 ± 0.16 1213 ± 301

BGS 102

USEPA batch 3.58 ± 0.28 1.51 ± 0.61 0.319 ± 0.058 0.91 ± 0.14 35 ± 36

COLM-G 3.6 ± 3.9 1.50 ± 0.22 0.32 ± 0.27 0.92 ± 0.29 35 ± 14

COLM-USP 29 ± 40 4.5 ± 6.1 0.14 ± 0.18 0.13 ± 0.18 11.3 ± 8.7

UBM batch 35.66 ± 0.65 3.90 ± 0.22 0.2177 ± 0.0073 0.0325 ± 0.0062 17.2 ± 2.6

COLM-UBM 34.652 ± 0.033 4.2955 ±

0.0037 0.21787 ± 0.00021 0.032435 ± 0.000035 11.535 ± 0.039

Table 2. Analysis of variance (ANOVA) and type III sum of squares (SS)

between the results from the US EPA batch method, COLM-G, COLM-

USP, COLM-UBM, and UBM batch method for Cr, As, Cd, Sb, and Pb,

including the number of observations (n), degrees of freedom (DF),

coefficient of determination (R2), mean squares (MS), F statistic (F), and P-

value (P) associated with the F statistic. The P-values in bold indicate a

significant (P≤ 0.05) difference in the methods at the 95% confidence

interval

Cr As Cd Sb Pb

n 14 14 14 14 14

DF 9 9 9 9 9

R² 0.486 0.23 0.251 0.901 0.348

DF 4 4 4 4 4

SS 3018 27 0.073 2.46 1884

MS 755 7 0.018 0.615 471

F 2.125 0.672 0.754 20.484 1.2

P 0.16 0.628 0.58 0.0002 0.375

RESULTS AND DISCUSSION

Reproducibility with the COLM versus batch methods. All

analyte concentrations were above the detection limits (Table S3)

that were calculated using three times the standard deviation of the

blank signal. The ranges of percent relative standard deviations

(%RSDs) across all elements (Table S4) were 1–130% for the US

EPA batch method, 4–170% for the COLM-G, 1–110% for the

UBM, 9–64% for the COLM-UBM, and 74–170% for the

COLM-USP. Mean %RSDs for each method were 27% for the US

EPA batch method, 66% for the COLM-G, 130% for the COLM-

USP, 26% for the UBM, and 29% for the COLM-UBM. Thus, it

appears that the %RSDs with the COLM using a low-pressure

system (COLM-UBM) are generally smaller than those observed

with the COLM using a high-pressure system (COLM-G and

COLM-USP), even when the same CRM was used. In fact, with

the low-pressure system, the average %RSD for the COLM-UBM

is similar to that with the UBM, whereas the range of RSDs is

narrower with the COLM-UBM than that obtained with the UBM.

In contrast, both the range of RSDs and the mean %RSD were

greater than those obtained using the batch method when a high-

pressure system was used for the COLM. Given the inherent

heterogeneity of soils, better reproducibility was expected with 1-

g aliquots for COLM-G and COLM-USP than with 0.5-g aliquots

with COLM-UBM; however, the inverse result was observed. The

generally high RSDs with the COLM when using a high-pressure

system are thus completely unexpected. We will investigate this

topic in a future work.

Stomach-equivalent extractions. A comparison of the

bioaccessible concentrations of the stomach-equivalent phases

obtained by different methods is shown in Table 1. BGS 102 was

the only CRM analyzed by all three methods, as it has been

specifically developed for bioaccessibility and was used in the

validation of the UBM for As, Pb, and Cd bioaccessibility. The

stomach-equivalent phase represents the bioaccessible amount in

both saliva and gastric fluids. Because the NIST CRMs were

previously used for validation of the USEPA method for As and

Pb, they were also used for direct comparison with the COLM-G.

For the US EPA method (where no saliva is used), the gastric phase

is assumed to also extract what would have been extracted by

saliva.37 With ANOVA, no statistically significant difference was

observed between the batch, COLM-G, COLM-USP, and COLM-

UBM methods for Cr, As, Cd, and Pb (Table 2). However, for Sb,

a significant difference was observed. Further investigation into

the model parameters (Table 3) indicated significant differences

between the methods with different extraction fluids. However, no

difference was observed between the US EPA batch and the

COLM-G for Sb. Thus, the differences in the method conditions

between the US EPA batch and COLM-UBM, COLM-USP, and

UBM batches likely explain the different results for Sb.

Additionally, none of these methods is validated for Sb

determination, and the certificate of BGS 102 provides no

guidance concentration for Sb (Table S1). In any case, additional


Table 3. Model parameters for the ANOVA between the results from the COLM-G, COLM-USP, COLM-UBM, UBM batch method and US EPA batch

method for Sb, including the Student’s t (t) and P-value (P) associated with the Student’s t-test. The P-values in bold indicate a significant (P≤ 0.05) difference

between the US EPA batch method and the other methods at the 95% confidence Interval

Method Value Standard error T P Lower bound

(95%)

Upper bound

(95%)

COLM-G 0.007 0.142 0.052 0.96 -0.313 0.328

COLM-UBM -0.877 0.142 -6.199 <0.0001 -1.197 -0.557

COLM-USP -0.779 0.142 -5.507 <0.0001 -1.1 -0.459

UBM batch -0.877 0.158 -5.544 <0.0001 -1.235 -0.519

US EPA batch 0 0

Table 4. Comparison between the COLM-UBM and conventional UBM batch extraction phases, and stomach-phase COLM-UBM and certificate

bioaccessibility concentrations in mg kg-1 (mean ± standard deviation, n=3) of Cr, As, Cd, Sb, and Pb in BGS 102 using Student’s t-test at 95% confidence

interval

Cr As Cd Sb Pb

COLM-

UBM Stomach 34.652 ± 0.033

4.2955 ±

0.0037 0.21787 ± 0.00021 0.032435 ± 0.000035 11.535 ± 0.039

UBM batch Stomach 35.66 ± 0.65 3.90 ± 0.22 0.2177 ± 0.0073 0.0325 ± 0.0061 17.2 ± 2.6

tcalc 2.683 3.1501 0.034 0.023 3.712

ttable 4.303 4.303 4.303 4.303 4.303

Certificate bioaccessibility 5.4 ± 2.4 13 ± 6

tcalc 1.782 0.772

ttable 2.131 2.262

COLM-

UBM Stomach + Intestinal 56.666 ± 0.091

6.1631 ±

0.0053 0.29223 ± 0.00031 0.050498 ± 0.000051 37.34 ± 0.12

UBM batch Stomach + Intestinal 2.7 ± 1.2 2.63 ± 0.14 0.061 ± 0.065 0.0839 ± 0.0040 0.42 ± 0.20

tcalc 78.099 42.42 6.111 14.521 10.336

ttable 4.303 4.303 4.303 4.303 4.303

Fig. 2 Percent bioaccessibilities (± standard deviation) of Cr, As, Cd, and

Pb in BGS 102 extracted using the COLM-UBM, UBM batch

bioaccessibility method (n = 3), certificate values,22 and bioavailability

values40 for As and Pb. No certificate or bioavailability value has been

reported for Cr and Cd (indicated as “N/A”).

information that was not available with batch methods was

provided by the COLM. Although not shown here, double peaks

that are characteristic of differential sources of elements17 resulted

in real-time elution profiles from BGS 102 for Pb when glycine-

HCl or gastric fluid was used with the COLM. We will use this

information along with Pb isotope ratios for Pb source

apportionment in another study.

UBM vs. COLM. BGS 102 was selected for this analysis as it was

used in the validation of the UBM and developed specifically for

bioaccessibility. The concentrations obtained using the

conventional UBM batch method and COLM-UBM were

compared to the certificate bioaccessibility values (available for

Pb and As) for BGS 102 (Table S5) and presented as percent

bioaccessibilities alongside the published bioavailability values38

(Fig. 2). During the UBM, the saliva and gastric phases are

combined to form the “stomach” phase and the duodenal and bile

phases are combined to form the “stomach + intestinal” phase. The

“stomach + intestinal” extractions have lower concentrations than

the “stomach” phase, as reported in a previous multi-elemental

bioaccessibility analysis of BGS 102 (Table S5 and Fig. 2).21,23,39

This result can be explained based on the lower solubility of

elements at a higher pH of 6.3 of the duodenal and bile fluids.23

For the sum of the saliva and stomach phases during the COLM,

no statistically significant difference was observed at the 95%

confidence interval between either the stomach phase of the UBM

or certificate bioaccessibility values (Table 4). However, the

combined stomach and intestinal fluid evidently differed between

the two methods because the sample was exposed to the duodenal

and bile phases separately and sequentially with COLM-UBM,

which further increased the amount of the analyte released and

provided a more conservative estimate of the risk. Although

multiple samples can be extracted simultaneously with the UBM,

all four gastrointestinal fluids can be extracted in less than 1 h with

the COLM-UBM, while it takes over 5 h with the UBM.

US EPA vs. COLM. Fig. 3 shows that the COLM-G and US EPA

batch bioaccessible concentrations of Cr, As, Cd, Sb, and Pb in the


Fig. 3 Comparison of the concentrations (in mg kg-1 ± standard deviation, n = 3) of Cr, As, Cd, Sb, and Pb in NIST 2710, NIST 2710a, NIST 2711a, and BGS

102 using COLM-G, aqua regia extraction, and certificate values.

CRMs are similar despite the COLM-G requiring a fraction of the

time needed for the US EPA batch method. The values determined

by the two methods did not show any statistically significant

difference for any element (Table S6). The matrix spike recovery

during the analysis of BGS 102 using the US EPA batch method

was 96.6%.

Table 5 compares the percent bioaccessibility for Pb and As

obtained using the US EPA batch method and COLM-G based on

the available control limits12 and bioavailability studies40,41 for

each CRM (as control limits and bioavailability results for Cr, Cd,

and Sb are unavailable). For both methods, the percent

bioaccessibility for Pb agreed with both the control limits and


Table 5. Percent bioaccessibilities (mean ± standard deviation) of As and

Pb with the US EPA batch method and COLM-G compared to control

limits and bioavailability (animal) studies for NIST 2710, NIST 2710a,

NIST 2711a, and BGS 102

CRM Method As Pb

NIST 2710

USEPA batch 41.5 ± 3.5 87.9 ± 2.2

COLM-G 31.9 ± 4.4 81 ± 40

Control limitsa 17-60 63-88

Bioavailability40 37-50 73-79

NIST 2710a

USEPA batch 24.3 ± 3.9 60 ± 13

COLM-G 18 ± 31 61 ± 62

Control limits 24.4-52.5(41) 56-69(12)

Bioavailability 39-45(42) 39-84(43)

NIST 2711a

USEPA Batch 37.2 ± 6.2 85.5 ± 8.8

COLM-G 31.6 ± 5.6 60 ± 15

Control Limits N/Ac 70-89(12)

Bioavailability N/A 83-89(12)

BGS 102

USEPA Batch 1.82 ± 0.90 37 ± 50

COLM-G 1.48 ± 0.22 86 ± 34

Control limitsb 3.0-7.5 8.8-24

Bioavailability37 1.72-12.56 10.7-19.4 a Derived (see “Control limits for NIST 2710” in Supplemental material); b

Control limits for BGS 102 using the UBM for informational comparison

only; c N/A = No control values, concentrations reportedly too low. 12

Fig. 4 Comparison of COLM-USP bioaccessibility (including residual

phase) of Cr, As, Sb, Pb, and Cd in BGS 102 with aqua regia extraction

values (n = 3) and certificate values in mg kg-1 (± standard deviation). There

is no certificate value reported for Sb (indicated as “N/A”).

bioavailability ranges for most CRMs. In BGS 102, the higher Pb

bioaccessibility (using either method) and lower As

bioaccessibility than the reported ranges were likely because the

control limits were determined with the UBM, which uses

different gastrointestinal fluids.

Verification of the mass balance for the COLM. Fig. 3

compares the sum of bioaccessible plus residual concentrations

using the COLM-G to the concentrations obtained by the US EPA

batch method, aqua regia extraction, and certificate values. The

values determined by the COLM-G sum and aqua regia extraction,

COLM-G sum and certificate values, and aqua regia extraction

and certificate values according to Student’s t-test at the 95%

confidence interval were not statistically significant for any of the

elements.

A similar comparison was performed using COLM-USP on

BGS 102 (Fig. 4). Again, the individual Student’s t-tests between

the COLM-USP sum and the aqua regia extraction concentrations,

or the COLM-USP sum and the certificate values did not show any

statistically significant difference at the 95% confidence interval.

Additionally, most elements were extracted into the gastric phase,

with only Pb having a significant portion in the intestinal phase.

As previously reported for soil,37 all elements showed almost no

extraction into the saliva phase.

Generally, the COLM could be incorporated into both the US

EPA and UBM methods without any discrepancy in

bioaccessibility, as demonstrated by ANOVA and Student’s t-test.

The COLM could also provide valuable information about real-

world samples through source apportionment17 and isotope ratio

analysis30.

CONCLUSIONS

The COLM was used to measure the bioaccessibility of Cr, As, Cd,

Sb, and Pb in NIST 2710, NIST SRM 2710a, NIST SRM 2711a,

and BGS 102 using gastrointestinal fluids obtained using the US

EPA, USP, and UBM methods. With COLM-G, the bioaccessible

+ residual, total aqua regia extraction, and certificate

concentrations were not statistically significantly at the 95%

confidence interval. Pb bioaccessibility was within the control

limits and bioavailability ranges, whereas As was at the lower end

of the bioavailability ranges. Mass balance was additionally

verified by using the COLM-USP on BGS 102. Most of the

elements extracted in the gastric phase; very little extraction was

observed in saliva. ANOVA did not yield any statistically

significant difference between the COLM and the UBM stomach

and US EPA methods. The COLM-UBM agreed with the

certificate bioaccessibility values for BGS 102.

Overall, this investigation confirms that the COLM can be

incorporated into existing bioaccessibility protocols, particularly

for As and Pb. Although conventional batch methods can be used

to simultaneously perform multiple extractions, further refinement

of the COLM could allow for a more robust bioaccessibility

analysis. We plan to focus on eliminating the cause of degradation

in RSD when using a high-pressure system for the COLM in our

future work. Additional elements that affect human health, such as

Hg, Sn, and Cu, can also be investigated using the COLM. The

COLM would be particularly advantageous for Hg, as it provides

fewer opportunities to Hg for adsorption to vessel walls and loss

through volatilization. The development of a data processing

Excel add-in would further streamline the COLM analysis,

allowing for automatic peak integration and conversion from

count-per-second measurements to concentrations. Additionally,

the kinetic leaching information obtained through the COLM can


be used for source apportionment, which is not possible with batch

bioaccessibility methods. We will analyze soils with known

contaminant sources (such as Pb from gasoline) in our future work

to identify different sources of elements and isotopic source

apportionment, as it would allow for a more comprehensive soil

analysis.

ASSOCIATED CONTENT

Supporting Information

The Supporting Information (Table S1-S6, Fig S1, and Control

limits for NIST 2710) is available at www.at-


AUTHOR INFORMATION

Diane Beauchemin received her Ph.D.

in 1984 from Université de Montréal.

She is a professor (Full) and

Undergraduate Chair at Queen’s

University. Her research efforts are

focused on inductively coupled plasma

mass spectrometry (ICPMS) and ICP

optical emission spectrometry (OES)

from both fundamental and application

perspectives, and expanding the range

of application of ICPMS/OES to

geochemical exploration, risk assessment of food safety, characterization of

nanoparticles, and forensic analysis. She has been working as member of

editorial board for Atomic Spectroscopy. Diane Beauchemin won the Alan

Date Memorial Award (1988) from VG Elemental, the Distinguished

Service Award (2001) from Spectroscopy Society of Canada, the Maxxam

Award (2017) and Clara Benson Award (2019) from Canadian Society for

Chemistry, and the Gerhard Herzberg Award (2018) from the Canadian

Society for Analytical Sciences and Spectroscopy. She is author or co-

author of over 100 articles published in peer-reviewed scientific journals.


*D. Beauchemin


Notes


ACKNOWLEDGMENTS

The authors would like to gratefully acknowledge the Natural

Sciences and Engineering Research Council of Canada for

funding (RGPNM 39487-2018 to DB and RGPIN 2018-04885 to

IK). AK also thanks David Patch for training, Queen’s University

School of Graduate Studies for graduate awards, and Mitacs

Globallink for funding (FR38334).

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A Highly Efficient Method for the Accurate and Precise

Determination of Zinc Isotopic Ratios in Zinc-rich Minerals Using

MC-ICP-MS

Xiaojuan Nie, Zhian Bao, Peng Liang, Kaiyun Chen, Chunlei Zong, and Honglin Yuan*

State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi’an 710069, China

Received: October 21, 2021; Revised: December 01, 2021; Accepted: December 02, 2021; Available online: December 05, 2021.

DOI: 10.46770/AS.2021.910

ABSTRACT: This study proposes a highly efficient method for the direct determination of Zn isotopes in Zn-rich minerals,

without the use of column chromatography, via multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS).

Experiments (with or without column chromatography) were performed to evaluate the feasibility of directly obtaining

non-deviated Zn isotopic ratios by MC-ICP-MS. For Zn isotopes determined without the use of column chromatography, the

instrumental mass bias was corrected using the standard sample bracketing with Cu as the internal standard. The effects of acidity

and concentration mismatch and the matrix effect were strictly assessed in a wet-plasma mode. The Long-term reproducibilities of

δ66Zn and δ67Zn better than ± 0.03‰ (n = 42, 2 standard deviations (2s))

and ± 0.05‰ (n = 42, 2s), respectively, were achieved by repeatedly

measuring the NIST Standard Reference Materials (SRM) 682 solution

doped with trace matrix elements over four months. Zn-rich minerals

determined without employing column chromatography displayed little

drift in δ66Zn and δ67Zn values compared with minerals determined

using column chromatography, with Δ66Znwithout−with (Δ66Znwithout-with =

δ66Znwithout - δ66Znwith) ranging from -0.04 to +0.01‰ and Δ67Znwithout−with

ranging from -0.06 to +0.01‰. These results suggest that non-deviated

Zn isotopic ratios in Zn-rich minerals can be achieved without column

chromatography due to the low contents of undesired matrix elements.

INTRODUCTION

Zinc is a transition metal element with an atomic number of 30

and five stable isotopes, namely, 64Zn, 66Zn, 67Zn, 68Zn, and 70Zn

with relative abundances of 48.63%, 27.90%, 4.10%, 18.75%,

and 0.62%, respectively.1 Maréchal et al.2 developed a pioneering

high-precision method using multi-collector inductively coupled

plasma mass spectrometry (MC-ICP-MS) to determine Zn

isotope ratios, which has been increasingly adopted by the

researchers due to its faster and more sensitive and precise

measurements compared to thermal ionization mass

spectrometry (TIMS). Zn is usually present in the form of Zn2+

and widely involved in various geological and biological

processes. Zinc exists in different minerals or diverse

hydrothermal deposits and thus can be used as a tracer or an

indicator of an ore-forming process. Significant isotopic

variations for Zn have also been reported in

microbially-mediated sulfide ores. Thus far, Zn isotope

geochemistry has been successfully applied to studying the origin

and evolution of the solar system,3,4 ore genesis,5-7 contamination

sources,8,9 and life sciences.10-12 In particular, because of the

well-constrained formation conditions and high Zn abundance,

the Zn isotopic compositions are useful to study ore deposits,

especially for Zn-Pb deposits. For instance, sphalerite, a major

sulfide mineral present in different types of lead-zinc deposits,

and the zinc isotope composition of sphalerite is controlled by

composition of the source-rock(s), temperature-related and

kinetic fractionation during precipitation. Besides, the subsurface


cooling of hydrothermal fluids leads to precipitation of

isotopically light sphalerite (Zn sulfide), and that this process is a

primary cause of Zn isotope variation in hydrothermal fluids.

Other studies have shown that by understanding how chemical

processes that occur beneath the seafloor affect hydrothermal

fluid δ66Zn, Zn isotopes may be used as a tracer for studying

hydrothermal processes. Therefore, the Zn isotopes in sphalerite

have been successfully applied to reveal the metal source, kinetic

fractionation, and hydrothermal processes.5,6,13-15

Over the past two decades, column chromatography and mass

spectrometry have been essential for the precise and accurate

determination of Zn isotope ratios. Zn undergoes a slight isotopic

fractionation when exposed to anion exchange resins.2 Therefore,

a high Zn yield is a prerequisite for the precise and accurate

determination of Zn isotopic ratios with column chromatography.

In general, Zn solution is purified by AG MP-1 anion exchange

chromatography. An established method has been used for the

purification of Cu and Zn isotopes.2 Sossi et al.16 developed an

optimized separation methodology using AG1-X8 resin to

separate Cu, Fe, and Zn from each other and from matrix

elements in diverse rock samples. Their optimized method has

minimized the preparation time, reagent consumption and total

analytical blanks.

Previous studies have focused on the Fe and Cu isotope

determination of ore minerals without using column

chromatography. The good accuracy and precision have

indicated that ore minerals with a simple matrix could be

measured without employing column chromatography prior to

mass spectrometry.17-23 Some Fe-rich samples containing

meteoritic iron, Fe-oxides, and carbonate minerals were

dissolved in 6 M HCl and then purified using a two-step

precipitation of Fe(III) hydroxide with NH3. Finally, the Fe(OH)3

precipitate was dissolved in 0.1% HCl solution for the

determination of the Fe isotope.17,23 Zhang et al.23 used the

standard sample bracketing (SSB) technique with Ga as an

internal standard to determine Cu isotopic ratios in twelve

Cu-dominated minerals. They obtained 65Cuwithout−with ranging

from −0.04 to +0.02‰, indicating that Cu isotope ratios in

Cu-dominated minerals can be achieved without using column

chromatography.

Herein, we conducted comparative studies for each of the six

Zn-rich minerals (sphalerite, smithsonite, willemite,

hemimorphite, troostite, and hydrozincite). For each mineral, we

analyzed the sample with and without column chromatography.

The matrix effects of ten elements (Ni, Ti, Ge, Ag, Cd, Fe, Pb, Sb,

As, and Cu) were also evaluated. During the solution

nebulization multi-collector inductively coupled plasma mass

spectrometry (SN-MC-ICP-MS) analyses, the instrumental mass

bias was corrected using the SSB technique with Cu as an

internal standard. Our results indicate that the Zn isotope ratios

obtained with column chromatography are consistent with the

results obtained without the use of column chromatography.

Therefore, the non-deviated Zn isotope ratios can be acquired on

Zn-rich minerals can be acquired without using column

chromatography.

EXPERIMENTAL

Samples and reagents. All experiments were carried out in the

State Key Laboratory of Continental Dynamics (SKLCD),

Northwest University, China. Guaranteed reagent (GR) grade

nitric acid, hydrochloric acid, and hydrofluoric acid were distilled

separately twice in the sub-boiling distillation system

(Minnetonka, MN, USA). The ultrapure water with a resistivity

of 18.2 M cm-1 was obtained using a Milli-Q Element water

purification system (Elix-Millipore, Billerica, MA, USA). Two

milliliters of Bio-Rad AG MP-1M resin were packed in a

Bio-Rad Poly-Prep column with a diameter of 8 mm and a length

of 9 cm. All Polyfluoroalkoxy (PFA) vials were respectively

cleaned in GR grade HNO3, high-pure HNO3, and deionized

water prior to use.

The Zn-rich minerals, including sphalerite, smithsonite,

willemite, hemimorphite, troostite, and hydrozincite, were

analyzed under the microscope. Six sphalerites were collected

from different types of Pb-Zn deposits, including skarn (KKZ61),

sedimentary exhalative (SEDEX; Du2018-832 and

Du2018-8321), volcanic-hosted massive sulfide (VHMS;

DBK261), Mississippi valley (ZK2002-4), and hydrothermal

vein (GBW07270) types. Zn reference materials NIST SRM 683

and NIST SRM 682 were used as in-house standards in the

SKLCD. The Zn isotopic ratios of NIST SRM 683 and NIST

SRM 682 are δ66ZnJMC-Lyon= +0.12 ± 0.04‰ 24 and

δ66ZnJMC-Lyon= -2.45 ± 0.02‰, 25 respectively, relative to the

reference material JMC-Lyon. Alfa Cu (Stock #13867,

Lot#012782G, 1000 μg g-1) purchased from the Johnson Matthey

company (London UK) was used as an internal standard element

to correct the instrumental mass bias.

Experimental design. Approximately 5 mg of each Zn-rich

mineral sample was weighed into a 15 mL pre-cleaned PFA vial.

Each of the Zn-rich minerals was dissolved in 4 mL of

concentrated HF-HNO3 (3:1, v/v) mixture. Subsequently, the

capped beakers were placed on a hot plate at 120 oC for two days

to ensure full dissolution of the contents. All dissolved samples

were evaporated to dryness and then re-dissolved in aqua regia.

To ensure that the medium was completely converted to HCl

medium for the following experiment, 6 M HCl was added two

times.

To evaluate whether non-deviated Zn isotope ratios can be

determined without column chromatography, the sample solution

was divided into two parts for the two series of parallel

experiments. For the first series of the experiment, three similar


aliquots containing 20 µg of Zn from each solution was taken for

conducting the subsequent column chromatography. The Zn

solutions collected from column chromatography were

evaporated to dryness and re-dissolved in 2% HNO3 (v/v) for

mass spectrometry. The other parallel experiment measured the

remaining Zn-rich sample solutions directly using MC-ICP-MS

without column chromatography.

Chemical purification procedure. The chemical purification

procedures followed those described by Chen et al.26. The

column was a 10 mL Bio-Rad Poly-Prep column with 2 mL of

AG MP-1M (200 – 400 mesh) anion exchange resin. Before

loading into the column, the resin was alternatively washed with

2 mL of 0.5 M HNO3 and 2 mL of 6 M HCl six times. 10 mL of

6 M HCl was then added to the column for conditioning.

Thereafter, 50 μL of the dissolved sample containing about 20 µg

of Zn was loaded into the column, followed by loading 4 mL of 6

M HCl to elute most of the matrix elements (e.g., Na, Mg, Al, K,

Ca, Ni, Ti, and Mn). The Co, Cu, Ga, and Fe were eluted using

6mL of 0.5 M HCl. Finally, Zn was collected in 10 mL of 0.5 M

HNO3. The collected Zn fractions were evaporated to dryness

and dissolved in concentrated HNO3. Subsequently, the dissolved

solutions were re-evaporated to dryness. Before the Zn isotopic

ratio analysis, the solutions were dissolved with 2% HNO3 (v/v)

to 1 µg g-1. To verify the above procedures for Zn purification,

every 1 mL of the eluate was continuously collected to measure

the concentrations of matrix elements by an Agilent 7900

ICP-MS at the SKLCD, Northwest University.

SN-MC-ICP-MS analysis. High-precision Zn isotopic ratios

were determined by MC-ICP-MS at the SKLCD on the Nu

plasma 1700 MC-ICP-MS. During Zn isotope analysis, L4, L3,

L2, Ax, H2, and H4 Faraday cups were used to collect 62Ni, 63Cu, 64Zn, 65Cu, 66Zn, and 67Zn, respectively. A “wet” plasma with a

wet cone and a GE quartz nebulizer (100 μL min-1) was used to

determine Zn isotopic ratios. The SSB technique with Cu as an

internal standard was selected to correct the instrumental mass

bias. All Zn solutions were diluted with 2% (v/v) HNO3 to

achieve a Zn concentration of 1 μg g-1 in the final solutions. The

low mass resolution was used to resolve polyatomic interferences.

Each measurement consisted of one block with 30 cycles, each of

which had an integration time of 10 s. Samples were measured

relative to NIST SRM 683 solution, and Zn isotopic ratios were

reported in standard delta-notation in per mil relative to the

JMC-Lyon by the following formulas:

δ66Zn(‰) = ((66Zn/64Zn)sample/(66Zn/64Zn)NIST SRM 683 - 1) * 1000

+ 0.12

δ67Zn(‰) =((67Zn/64Zn)sample/ (67Zn/64Zn) NIST SRM 683 - 1) *

1000 + 0.18.

The specific operating parameters of Nu Plasma 1700 are

displayed in Table 1.


Before conducting the following experiments, the feasibility of

the experiment was explored to ensure that the mass

spectrometry process and design of experiments could be used.

The matrix elements (such as Ni, Ti, Ge, Ag, Cd, Fe, Pb, Sb, As,

and Cu) were doped into the NIST SRM 682 solution to obtain a

trace-element of the six types of sphalerites and five types of

Zn-rich minerals are reported in Table 2. In several sphalerite

samples, the content of Fe is very high, and the value of Fe/Zn in

DBK261 is 0.08. However, their Ca, As, Se and Cu contents are

very low with the As/Zn, Se/Zn, and Cu/Zn ratios approximately

0. Among other Zn-rich minerals, willemite has the highest

Mn/Zn ratio of 0.02, while troostite has the highest Fe/Zn ratio of

0.014. The contents of Mg, Al, Cu, Ti, and Ni are very low in all

the minerals. The Zn isotope ratios of the six sphalerite samples

and five Zn-rich minerals are presented in Table 3. The δ66Zn of

NIST SRM 682 solution without column chromatography was

-2.41 ± 0.04‰ (2s, n=3), which was identical with the reported

reference value of -2.45 ± 0.02‰ 25. All pairs (with and without

column) showed similar Zn isotopic ratios with Δ66Znwithout-with

ranging from -0.04 to 0.01‰ and Δ67Znwithout-with ranging from

-0.06 to 0.01‰ (Table 3).

Zn-rich mineral solution with a matrix element/Zn molar ratio

of 0.001. This solution was divided into fifteen aliquots, three of

which were directly measured by MC-ICP-MS, and the other

twelve were analyzed after subjecting them to chemical

separation. Subsequently, the Zn isotopic ratios with and without

the column chromatography were determined. Direct

measurements of the NIST SRM 682 solution doped with matrix

elements without column chromatography yielded a mean δ66Zn

value of -2.41 ± 0.05‰ (2s, n = 3), while measurements of the

column-treated solution yielded a mean δ66Zn value of -2.42 ±

0.05‰ (2s, n = 12). These values agree well with the reported

value of -2.45 ± 0.02‰ for reference δ66Zn 25, suggesting that the

purification process and mass spectrometry process were

feasible.

Table 1. Nu Plasma 1700 MC-ICP-MS operating parameters for Zn

isotope measurements

Instrumental parameters Nu plasma 1700

RF power 1300 W

Cooling gas 13.0 L min-1

Auxiliary gas 0.8 L min-1

Nebulizer 39 Psi

Background of 64Zn <3 mV

Sample cone and skimmer cone Ni orifice

Resolution mode Low resolution

Mass separation 0.5

Sample uptake 100 μL min-1


Table 2. Selected trace element compositions of Zn-rich minerals

Minerals Ca/Zn Fe/Zn Ti/Zn Ni/Zn As/Zn Se/Zn Mg/Zn Cd/Zn Cu/Zn

Sphalerite (KKZ61) 0.003 0.021 0 0 0.001 0.002 0 0.011 0

Sphalerite (Du2018-832) 0.001 0.031 0 0 0.001 0.001 0 0.002 0.001

Sphalerite (Du2018-8321) 0.001 0.013 0 0 0.001 0.001 0 0.002 0.001

Sphalerite (DBK261) 0.003 0.08 0 0 0.003 0.006 0 0.005 0.001

Sphalerite (ZK2002-4) 0.001 - - 0 0 0 0.017 0 0

Sphalerite (GBW07270) 0.005 0.031 0 0 0 0 0.018 0.002 0.002

Smithsonite 0.017 0.003 0 0 - - 0 - 0

Willemite 0.001 0.007 0 0 - - 0.002 - 0

Hemimorphite 0.003 0 0 0 - - 0 - 0.002

Troostite 0.002 0.014 0 0 - - 0 - 0

Hydrozincite 0.01 0 0 0 0 - 0.014 0 0

“-” denotes elements that could not be detected by Agilent 7900 ICP-MS due to their low signal intensities.

Table 3. Zinc isotope ratios of sphalerite samples and other types of Zn-rich minerals with/without column chromatography

Minerals δ66Znwithout (‰) 2s n δ66Znwith (‰) 2s n Δ66Znwithout-with (‰)

Sphalerite (KKZ61) 0.21 0.02 3 0.23 0.06 3 -0.02

Sphalerite (Du2018-832) -0.07 0.02 3 -0.04 0.04 3 -0.03

Sphalerite (Du2018-8321) 0.13 0.02 3 0.17 0.04 3 -0.04

Sphalerite (DBK261) 0.13 0.03 3 0.15 0.03 3 -0.02

Sphalerite (ZK2002-4) 0.20 0.03 3 0.22 0.03 3 -0.02

Sphalerite (GBW07270) -0.05 0.01 3 -0.03 0.03 3 -0.02

Smithsonite -0.27 0.01 3 -0.26 0.04 3 -0.01

Willemite 0.26 0.07 3 0.26 0.02 3 0.00

Hemimorphite 0.96 0.05 3 0.96 0.02 3 0.00

Troostite 0.09 0.05 3 0.11 0.01 3 -0.02

Hydrozincite 0.04 0.02 3 0.03 0.01 3 0.01

Minerals δ67Znwithout (‰) 2s n δ67Znwith (‰) 2s n Δ67Znwithout-with (‰)

Sphalerite (KKZ61) 0.31 0.04 3 0.35 0.06 3 -0.04

Sphalerite (Du2018-832) -0.09 0.02 3 -0.07 0.05 3 -0.02

Sphalerite (Du2018-8321) 0.21 0.04 3 0.27 0.09 3 -0.06

Sphalerite (DBK261) 0.21 0.06 3 0.23 0.06 3 -0.02

Sphalerite (ZK2002-4) 0.33 0.06 3 0.35 0.07 3 -0.02

Sphalerite (GBW07270) -0.08 0.01 3 -0.05 0.07 3 -0.03

Smithsonite -0.38 0.01 3 -0.38 0.06 3 0.00

Willemite 0.41 0.13 3 0.40 0.04 3 0.01

Hemimorphite 1.43 0.10 3 1.42 0.04 3 0.01

Troostite 0.13 0.05 3 0.15 0.04 3 -0.02

Hydrozincite 0.04 0.03 3 0.05 0.01 3 -0.01

Note: δZnwith and δZnwithout denote Zn isotope ratios of minerals that were processed with column and without column, respectively; 2s refers to 2 standard

deviations. Column “n” represents the total number of repeat analyses. Δ66Znwithout-with=δ66Znwithout-δ66Znwith, Δ

67Znwithout-with=δ67Znwithout-δ67Znwith.

Effects of acidity and concentration mismatch. Previous

studies have shown that the stable isotopic deviation of Zn can be

caused by the concentration and acidity mismatches during mass

spectrometry.26-28 In this study, we used 1.0 μg g-1 of Alfa Zn

solution diluted with 2.0% (v/v) HNO3 as the medium for the Zn

isotopic analyses. The effects of acidity mismatch between the

sample and the standard were tested using a series of 1.0 μg g-1

Alfa Zn solutions diluted with 1.5 to 2.4% (v/v) HNO3. The SSB


Fig. 1 Effects of different acidities of Alfa Zn solution on δ66Zn and

δ67Zn.Precisions are expressed as 2 standard deviations (2s), which are

calculated based on four repeated measurements. Dotted line indicates the

reference value. Grey area represents the 2s deviation range (0.05‰).

Fig. 2 Zn isotope ratio variations of Alfa Zn solution with different Zn

concentrations from 0.5 to 2.0 μg g-1. Errors are presented at the 2s level

obtained by four repeated measurements. Dotted line indicates the

reference value. Grey region expresses the 2s deviation range (0.05‰).

method with Cu as an internal standard was used to correct the

instrument mass bias in the zinc isotope analysis. The lesser the

effects of acidity mismatch on the test, the closer the values of

δ66ZnAlfa Zn and δ67ZnAlfa Zn should be to 0. Fig. 1 shows that the

differences in acidities between the sample and standard have a

significant influence on the Zn isotopic compositions during

mass spectrometry. When the difference in acidities between the

sample and standard is within 10%, the obtained δ66ZnAlfa Zn and

δ67ZnAlfa Zn values were within a deviation of 0.05‰. When the

difference is ≥ 20%, the offset in δ67ZnAlfa Zn can be up to 0.10‰,

which is significant. To eliminate the possible effects of the

acidity mismatch, all dried samples were diluted using 2.0% (v/v)

HNO3 to ensure acceptable acidity differences (<10%) before

conducting mass spectrometry.

Fixing the Zn concentration of the bracketing standard at 1.0

μg g-1, we measured a series of Alfa Zn solutions with different

Zn concentrations ranging from 0.5 to 2.0 μg g-1 to investigate the

effects of the concentration mismatch between the sample and

the standard. The lesser the effects of the concentration mismatch,

the closer the values of δ66ZnAlfa Zn and δ67ZnAlfa Zn should be to 0.

Our results indicate that the differences in concentrations do not

have a significant influence on the Zn isotopic compositions for

the wet plasma method (Fig. 2). For a Csample/Cstandard ratio

ranging from 0.5 to 2.0, the δ66ZnAlfa Zn and δ67ZnAlfa Zn values of

all samples agree well within the 0.05‰ deviation. To guarantee

the precision and accuracy of Zn isotopic ratios, the

Csample/Cstandard ratio was kept at less than ± 20% during mass

spectrometry.

Effects of matrix elements. Natural sphalerite minerals usually

comprise some trace elements, such as Ni, Ti, Ge, Ag, Cd, Fe, Pb,

Sb, As, and Cu, which remain in the Zn solutions that are not

subjected to column chromatography. The presence of these

residual matrix elements can potentially affect the accuracy and

precision of Zn isotopic analysis. In this study, we quantitatively

assessed the potential effects of the ten matrix elements (Ni, Ti,

Ge, Ag, Cd, Fe, Pb, Sb, As, and Cu) on Zn isotope measurements.

These matrix elements of different concentrations were doped

into 1.0 μg g-1 Alfa Zn standard solutions and bracketed using the

same pure Zn standard without any doping. The results of these

analyses were plotted in Fig. 3. For most matrix elements with

matrix element/Zn molar ratios up to 0.1, the δ66ZnAlfa Zn and

δ67ZnAlfa Zn values were consistent with the true value within the

0.05‰ deviation, suggesting that the influence of these elements

can be ignored during the measurements. When the molar ratio

of Cu/Zn ranged from 0.25 to 3, Zn isotopic ratios showed no

difference. Therefore, the addition of Cu in the sample would not

affect the results of Zn isotope ratio measurements, it is feasible

to use Cu as the internal standard.

However, small amounts of Ti and Ni residues in the Zn

solutions will affect Zn isotopic ratios because 64Ni+ and 48Ti16O+

can produce intense isobaric interferences on 64Zn+. The matrix

effects of Ti and Ni on Zn isotope ratios are also shown in Fig. 3.

When the Ti/Zn ratio is less than 0.01, δ66ZnAlfa Zn and δ67ZnAlfa Zn

values can be accurately obtained within the 0.05‰ deviation.

When the Ni/Zn ratio is less than 0.005, δ66ZnAlfa Zn and δ67ZnAlfa

Zn values with better accuracies can be obtained within the 0.05‰

deviation. Owing the low Ti and Ni contents in Zn-rich minerals

(Ti or Ni/Zn <0.001), the Ti polyatomic and Ni interferences are

negligible when the Zn isotope ratios are measured without

column chromatography.

Quality control of the Zn isotope ratios of samples. The NIST

SRM 682 solution doped with trace matrix elements was

repeatedly measured at different periods by MC-ICP-MS for

quality control. A total of 42 tests were conducted in December

2020, January 2021 and March 2021. Fig. 4 shows that the

variations of Zn isotope ratios of NIST SRM 682 solutions were

small within the four months, and the long-term precision of the

δ66Zn was ±0.03‰. The NIST SRM 682 solutions possessed a

mean δ66Zn of -2.41 ± 0.03‰ (2s, n = 42) over the four months,

which was consistent with the reference value within 2s deviation.

The results confirm that our method was useful to determine the


Fig. 3 Zn isotope ratio variation of Alfa Zn solutions after doping with different proportions of matrix elements relative to Zn. Zn concentration of samples

and bracketed standards (Alfa Zn) are the same (1μg g-1). Errors(2s) were obtained by four repeated measurements. Dotted line represents the reference

value. Grey region points out the 2s deviation range (0.05‰).

Zn isotope ratios in the Zn-rich solutions. The SSB with Cu as an

internal standard was used to correct the quality deviation of the

instrument for the accurate measurement of Zn isotope ratios in

Zn-rich solution. Moreover, the total procedural Zn blanks were

lower than 20 ng, which was negligible compared with the 20 µg

of Zn of the sample. Importantly, Zn-rich minerals analyzed

without column chromatography yielded similar Zn isotope

ratios compared with samples analyzed after column


Fig. 4 Long-term reproducibilities of δ66Zn and δ67Zn measurements of

NIST SRM 682 solution.

Fig. 5 Zinc three-isotope plot (δ67Zn

vs. δ66Zn) of all Zn-rich materials

analyzed in this study.

Fig. 6 δ66Zn and δ67Zn values for sphalerites and other types of Zn-rich

minerals with and without column chromatography, respectively. Errors

are presented at the 2s level and obtained through four repeated

measurements.

Fig. 7

Deviations of Δ66Znwithout-with

and Δ67Znwithout-with

of sphalerites and

other types of Zn-rich minerals, respectively.

chromatography, which indirectly supported the reliability of our

method. The Zn three-isotope plots of all Zn-rich minerals are

shown in Fig. 5. The δ66Zn and δ67Zn fractionation line (y = (1.48

± 0.01) x + (0.01 ± 0.01), R2 = 0.999) has a slope that is

consistent with the kinetic (1.48) and equilibrium (1.49)

fractionation within the error.29 The trend of isotopic fractionation

further confirms the efficiency of the direct determination of Zn

isotopic ratios without column chromatography for Zn-rich

minerals. This data demonstrates that the proposed

high-performance method has an excellent stability, repeatability,

accuracy, and precision.

Measurement of Zn isotopic ratios without column

chromatography. The Zn isotope ratios of sphalerite samples

and other Zn-rich minerals obtained by routine column

chromatography were served as reference values for comparison

in the following section. To obtain accurate isotopic ratios of

natural samples, matrix elements should be reduced or eliminated

before mass spectrometry. This should be apply to most isotopic

systems, otherwise, the matrix effects and isobaric interferences

would lead to mass discrimination of elements, such as Mg,30,31

Ca,32,33 Zr,34 Ba,35 and V.36 However, in the process of

MC-ICP-MS analysis, matrix effects can be significantly reduced

due to two factors: (a) the high zinc concentration in minerals

with the highest zinc content and (b) SSB technique with Cu as

the internal standard was used to correct the instrumental mass

discrimination. For some stable systems in simple matrix

samples, if the isobaric interference and matrix effect can be well

suppressed, accurate isotopic data can be obtained without

employing column chromatography. For example, Liu et al. 37

proposed that Ca isotope ratios can be measured without column


chemistry by TIMS using the 42Ca-43Ca double-spike technique.

Zhang et al. 23 suggested that the direct measurement of Cu

isotope ratios without column chromatography was possible

using the SSB technique with Ga as an internal standard. In the

determination of Zn isotopes by MC-ICP-MS, the influence of

matrix elements (such as Ni, Ti, Ge, Ag, Cd, Fe, Pb, Sb, As, and

Cu) on the zinc isotope ratios is limited (see section 3.2).

Therefore, we recommend using the SSB with Cu as an internal

standard bracketing method so that MC-ICP-MS can directly

measure the Zn isotopic ratios of Zn-rich materials without

column chromatography. In Fig. 6 and Fig. 7, the eleven tested

minerals show very small differences between the results

obtained with and without the chemical separation. The

Δ66Znwithout-with ranges from -0.04 to +0.01‰ and the

Δ67Znwithout-with ranges from -0.06 to +0.01‰. Among all the

minerals, nine samples possess Δ66Znwithout-with values ranging

from -0.02 to +0.01‰, and eight samples demonstrate

Δ67Znwithout-with values ranging from -0.02 to +0.01‰. Therefore,

this method can guarantee the measuring accuracy of the Zn

isotope ratio of Zn-rich minerals. In addition, if the analysis

without column chromatography can meet the needs of the

research within the range of the analytical error, this method can

make the sample handling easier and eliminate the need for the

sample separation time and save the cost of the reagents used.

Our data show that, the direct determination of Zn isotopes of

Zn-rich minerals by MC-ICP-MS does not require column

chromatography.

CONCLUSIONS

We introduced a highly efficient technique to determine Zn

isotopes directly in sphalerite samples and Zn-rich minerals

(smithsonite, willemite, hemimorphite, troostite, and hydrozincite)

by MC-ICP-MS without column separation. The results show

that Zn isotopic ratios obtained without column chromatography

are consistent with those obtained with column chromatography,

and the Δ66Znwith-without and Δ67Znwith-without values range from

-0.04 to +0.01‰ and from -0.06‰ to +0.01‰, respectively.

Because the acidity mismatch between the sample and the

standard solution has a significant influence on the Zn isotope

ratio, the acidity of the sample medium must be the same as that

of the standard solution. However, when the concentration of the

bracketing standard sample remains unchanged, differences in

the sample concentrations can barely influence the measurements

of the Zn isotopes. Due to low contents of matrix elements/Zn in

Zn-rich minerals, the effects of matrix elements (Ge, Ag, Cd, Fe,

Pb, Sb, and As) on the final Zn isotope ratio can be ignored.

Moreover, the added internal standard Cu would not affect the

measurements. Note the Ti/Zn molar ratio should be less than

0.01 and the Ni/Zn molar ratio should be less than 0.005 to

guarantee the accuracy of the Zn isotope analysis. Repeated

analyses of the NIST SRM 682 solution have shown reveal that

the external reproducibilities are better than ±0.03‰ (2s) and

±0.05‰ (2s) for δ66Zn and δ67Zn, respectively. Therefore, this

proposed high-performance method is advantageous to the

economical and efficient determination of Zn isotopic ratios in

Zn-rich minerals.

AUTHOR INFORMATION

Hong-Lin Yuan received his PhD in

2002 from the China University of

Geosciences (Wuhan). He is a research

professor of Geology, Northwest

University. His major research interests

are in situ micro-analyses using

femtosecond & nanosecond laser

ablation - quadruple & multiple

collector ICP-MS and its application in

geochemistry, isotope geochemistry,

analytical geochemistry. He has been

working as member of editorial board for Atomic Spectroscopy. He has

won Sun Xian-Shu Award, New Century Excellent Talents Support

Program of Ministry of Education, Hou Defeng Mineral and Rock

Geochemical Young Scientist Award, World Laboratory Wilhelm Simon

Fellowships etc.. He has published 140 SCI papers and obtained 8

national invention patents and 2 US patents.


*H. L. Yuan


Notes


ACKNOWLEDGMENTS

This work was financially supported by the National Natural

Science Foundation of China (42173033, 41825007, 42073051,

and 41803040) and the MOST Research Foundation from the

State Key Laboratory of Continental Dynamics (207010021).

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Depth Profiling at a Steel-aluminum Interface Using Slow-flow Direct

Current Glow Discharge Mass Spectrometry

Gagan Paudel,a,* Geir Langelandsvik,b Sergey Khromov, a Siri Marthe Arbo,c Ida Westermann,a

Hans Jørgen Roven,a and Marisa Di Sabatinoa

a Department of Materials Science and Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway

b Department of Materials and Nanotechnology, SINTEF Industry, 7491 Trondheim, Norway

c Department of Materials and Technology, SINTEF Manufacturing AS, 2831 Raufoss, Norway

Received: June 16, 2021; Revised: November 16, 2021; Accepted: November 17, 2021; Available online: Dec 03, 2021.

DOI: 10.46770/AS.2021.607

ABSTRACT: Direct current glow discharge mass spectrometry (dc-GDMS), which relies on sector field mass analyzers, is

not commonly used for depth profiling applications because of its slow data acquisition. Nevertheless, dc-GDMS has good

reproducibility and low limits of detection, which are analytical features that are encouraging for investigating the potential of

dc-GDMS for depth profiling applications. In this work, the diffusion of traces of chromium and nickel was profiled at the interface

of a steel-aluminum bilayer using a new sensitive dc-GDMS instrument. The depth profile of the non-treated sample was

compared with that of a heat-treated specimen at 400°C for 30 min. Scanning electron

micrographs, energy dispersive X-ray spectroscopy (EDS), and electron probe

microanalysis (EPMA) were used to study the diffusion process. The results of the

study show that both chromium and nickel are enriched at the steel-aluminum

interface, with higher concentrations of both elements for the heat-treated specimen.

Two peaks for both chromium and nickel were clearly present at the interface, with a

high concentration of chromium in the aluminum layer. This observation is likely a

consequence of elemental diffusion from the interface towards the aluminum layer.

The presence of the third layer, steel beneath the aluminum layer, might also have

contributed to this observation.

INTRODUCTION

The manufacturing of advanced materials with desirable

mechanical properties is important for solving some of today’s

challenges, such as excessive carbon dioxide emissions. Steel

and aluminum-based alloys are among the most common metals

used worldwide for automotive, aviation, and marine

applications. While steel offers the advantages of high strength

and low cost, aluminum alloys are lightweight and are corrosion

resistant. One way to reduce carbon dioxide emissions is by

using lightweight materials in automotive components. Therefore,

joining steel to aluminum in these structures can improve

mechanical properties while reducing fuel consumption. Thus,

research is underway to understand how the manufacturing of

metal composites can be improved.1 The manufacturing process

used during the joining process has a major influence on the

strength of the final joint2 and the performance of the final

processed material. During the bonding process it is possible to

form intermetallic layers with mechanical properties different

from those of the base materials. For instance, the intermetallic

layer thickness, and type of phases present, can influence the

strength of the final joint.3 The chemical composition of the base

materials has been found to influence the formation of

intermetallic phases along the joint interface.4,5 However, the

exact mechanism of phase formation and the influence of each

element are not well understood. Hence, there is a need to

develop profiling techniques that monitor the changes in the

concentration of alloying elements near the interface to

understand elemental diffusion.


Several analytical techniques are available for the depth

profiling of materials, including but not limited to secondary ion

mass spectrometry (SIMS), laser ablation-inductively coupled

plasma mass spectrometry (LA-ICPMS), glow discharge optical

emission spectroscopy (GDOES), and glow discharge mass

spectrometry (GDMS). In this study, we focus on GDMS-related

depth profiling and present the features and limitations of various

types of GDMS instruments. A popular instrument for depth

profiling is a GDMS instrument with a pulsed-radio frequency

source coupled to a time-of-flight (TOF) mass analyzer. Some of

the particularly useful features of this instrument include

acquisition of full spectra at a high speed, time-gated detection,6

separation of various glow discharge regions,7 reduction of

interference and good signal to noise ratio,8 direct analysis of

non-conductive materials,9 and the possibility of analysis of

thermosensitive samples.10 Furthermore, the use of the pulsed

mode increases the instantaneous power while reducing the heat

generation during the sputtering process, thereby increasing the

signal intensity.11 This instrument type was successfully used for

the profiling of ion implants in silicon substrates12 and for the

profiling of layered materials (Nb/Al13, Nb1/Al1-x–Cox14 and

Si/Co15) of various thicknesses. Furthermore, applications of this

instrument type in solar cell research for the profiling of silicon

thin films16 and cadmium telluride (CdTe) solar cells17 have been

investigated. Another notable feature of this instrument is the

ability to generate molecular ions, which allows for the

characterization of polymer-based composite materials.18 Plasma

profiling time-of-flight mass spectrometry (PP-TOFMS) is one

of these instrument types introduced by the HORIBA Jobin-Yvon

(France) company in 2014. This instrument allows the

characterization of flat-shaped samples in either continuous or

pulsed radiofrequency (RF) mode. Likewise, the Lumas 30

(Lumex Ltd., Russia) instrument introduced in 2007 uses hollow

cathode geometry for sample characterization in pulsed direct

current mode using a TOF analyzer. In 2014, Bodnar et al.

reported the depth profiling measurement of fluorine in a

fluorine-doped potassium titanyl phosphate crystal material.19

Another recent study from the same group reported depth

profiling of a conductive metal coating on a silicon substrate as

well as semiconductive and multi-layer nonconductive coatings

on glass substrates.20

TOF instruments have a limit of detection of concentration in

µg/g, which is two to three orders of magnitude lower than

sector-field instruments. Hence, the sector-field instruments are

more suitable when detecting low levels of impurities is

important, for instance in solar cell research. Sector-field

instruments, such as the Element GD (Thermo Electron now

Thermo Fischer Scientific, Germany, 2005) and VG 9000 (VG

Elemental, UK, production discontinued), are currently the most

common GDMS instruments in research institutes, universities

and contract laboratories. These instruments are often referred to

as “fast-flow” and “slow-flow” instruments respectively, which

are named after the discharge gas flow rates. It is worth

mentioning some notable research works that use these

instruments.

Using fast-flow GDMS, Su et al. demonstrated the depth

profiling of major and trace elements in nickel-based

superalloys.21 Di Sabatino et al. estimated the limits of detection

of impurities in silicon used for solar cells using matrix-specific

relative sensitivity factors (RSFs).22,23 The profiling of impurities

in solar cell silicon at the ng/g level was carried out as well.24

Likewise, other notable study from the same group is

measurement of copper diffusion in silicon substrate.25

For quality control of the final silicon wafers, the impurity

content of processed ingots is routinely assessed. In such studies,

the distribution of dopants is investigated in entire silicon ingots,

which can be of several meters in height. As the depth profiling

of such large ingots is not possible, the ingots are systematically

sampled at various locations of the ingot where bulk analysis of

each slice is performed. The bulk measurements of dopants of

several samples obtained from different locations were combined,

resembling a depth profile.26-28

Notable work has been carried out using the VG 9000 for the

characterization of coated and layered materials, such as

platinum-aluminide coatings on a nickel base29 and

hafnium-doped aluminide coatings.30 As VG 9000 production

was discontinued in 2005, there is a need to understand the

depth-profiling capabilities of similar slow-flow instruments that

are currently available, such as the Astrum GDMS (Nu

Instrument, UK) and the AutoConcept GD 90 (Mass

Spectrometry Instruments, UK). Despite their introduction in

2010 and 2008, respectively, there is limited information

available in the literature demonstrating the depth-profiling

capabilities of these two instruments. In this work, nickel and

chromium present in the steel-aluminum bilayer were profiled

using an Astrum GDMS instrument. This work aims to improve

the understanding of the diffusion of trace elements that are

involved in the formation of intermetallic layers.

QUANTIFICATION OF IMPURITIES

For this work, the determination of the concentration of

chromium and nickel present in the bilayer material was

performed using a certified aluminum reference material. At

present, it is not possible to correct for differences in the

sputtering rates of the steel and aluminum matrices. Therefore, a

compromise was made by assuming that a steel layer was present

on the top of the aluminum layer. RSFs were applied to the depth

profiles using several certified aluminum reference materials to

calibrate the data. Eq. 1 was used to determine the concentration

of impurity elements.

CXˈ/Al =IXi

IAli×

AAli

AXi× RSFX/Al (1)


where CXˈ/Al is the mass fraction of impurity element/isotope Xʹ

present in the aluminum matrix, IXi and IAli are the intensities of

the impurity analyte and aluminum, respectively, AXi and AAli are

the natural isotope abundances of the impurity analyte and

aluminum, respectively, and RSFX/Al represents the relative

sensitivity factor of a specific analyte, X, present in an aluminum

matrix. RSFs are estimated mathematically as the inverse of the

slope of the calibration curve IBRX/Al versus mass fraction CX/Al

(Eq. 2). The ion beam ratio IBRX/Al was generated by measuring

the materials.

RSFX/Al =𝐶X/Al

IBRX/Al (2)

MATERIALS AND METHODS

Sample preparation. The steel-aluminum joints were prepared

by a cold roll bonding procedure using 99.8 wt% AA1080 grade

aluminum and 355 MC E grade steel (with matrix composition

of 0.07 wt% C, 0.01 wt% Si, 0.62 wt% Mn, 0.05 wt% Al, 0.03

wt% Cr and 0.03 wt% Ni) as base materials. The sample

materials were cut to the desired dimensions of 120 mm × 15

mm. The initial thicknesses of the aluminum and steel before

rolling were 0.4 mm and 1 mm, respectively. Prior to this, the

steel sample was rolled at room temperature from 3 mm to 1 mm,

followed by annealing at 750 °C for 4 h in a furnace under an

argon atmosphere to soften the steel and prevent oxidation. The

total thickness reduction of the rolled composite material was in

the range of 60%–65%. It should be noted that the steel layer was

grinded. A detailed procedure for the sample production can be

found in literature3.

The sample materials were cleaned with acetone and the

surface was prepared by manual brushing with a 0.3 mm

steel-wire brush followed by blowing clean with compressed air.

The purpose of the brushing step was to generate a rough surface

to promote bonding. The specimens were stacked together in the

sequence steel-aluminum-steel and fastened with aluminum

rivets at each end to prevent lateral movement during rolling. To

further promote bonding, the stacked material was preheated in a

furnace for 10 min at 185°C until the desired rolling temperature

of 150°C was reached. After preheating, the samples were

removed from the furnace and rolled using a high rolling mill

with a roll diameter of 205 mm and a rolling speed of

approximately 10 rpm. After rolling, the material was

immediately submerged in water.

The rolled steel-aluminum-steel composite material was cut by

SiC cutoff blade to produce samples with dimensions of 30 mm

× 25 mm to fit the GDMS flat sample holder. One set of samples

was heated at 400 °C for 30 min. The composite material was

manually grinded to reduce the thickness of the steel layer using

a series of silicon carbide papers to obtain a mirror finish. Finally,

the specimen was cleaned with ethanol and dried in air.

GDMS method. Before analyzing the samples, the instrument

(Astrum, Nu Instruments, UK) was tuned and calibrated for

different masses with tantalum: 12C+, (40Ar)2+, 36Ar+, (40Ar)2+,

(40Ar)3+, 181Ta+, 181Ta+ 40Ar+ at discharge conditions of 2 mA and

1 kV. The 181Ta signal intensity of 1.4 x 10-9 A was observed with

a magnet scan at a resolution power of approximately 4000

(M/ΔM, 10% of peak height approach). The glow discharge cell

of the instrument was cryogenically cooled, and 99.9999% argon

was used as the discharge gas. For GDMS, the size of the orifice

of the tantalum front plate used in the flat sample holder

determines the lateral resolution. For this study, an orifice with a

diameter of 10 mm was used, which contributed to the lateral

size of crater profiles of approximately 10 mm. It is important to

mention that for GDMS-related depth profiling, the edge of the

crater is also analyzed. For 56Fe and 27Al, an integration time of

160 ms was used. These elements were detected using a Faraday

cup. For 52Cr and 60Ni, an integration time of 80 ms was used.

These elements were detected using an electron multiplier. The

most abundant isotope of nickel, 58Ni, suffers from monoatomic

interference due to 58Fe. Therefore, 60Ni was chosen as the

isotope for this study. The measurements were performed at a

resolution power of approximately 4000 (M/ΔM, 10% of the

peak height approach). For depth profiling study, the composite

and base materials were subjected to glow discharge setting of 5

mA, 0.75 kV in a constant current mode. The current and voltage

readback values were largely stable during the analysis period.

The GDMS craters were measured mechanically using a

profilometer (MarSurf M 400, Mahr GmbH, Göttingen,

Germany) immediately after the sputtering event.

Other complimentary techniques. After GDMS analysis, the

composite materials were cut in half and immobilized on a

substrate using epoxy resin. The samples were prepared by

standard metallographic procedures, involving grinding on

silicon carbide discs, and polishing on cloth with diamond paste

until a deformation-free mirror finish was obtained. The craters

were further investigated using an optical microscope (Zeiss

Axiovert, Carl Zeiss AG, Jena, Germany). Scanning electron

microscope (SEM) images were taken using a Zeiss Supra 55-VP

(Jena, Germany) and energy-dispersive X-ray spectroscopy

(EDS) standardless analysis using an EDAX Octane PRO-A

detector (Ametek, USA). Elemental analysis was carried out

using JEOL JXA-8500F electron probe microanalyzer (EPMA)

with an electron beam size of 1 µm. For quantitative

wavelength-dispersive X-ray spectroscopy (WDS) in EPMA, a

pure standard of 100 % aluminum and one steel standard

SRM663 were used. The composition of the steel standard was

95.01 wt% iron, 0.24 wt% aluminum, 1.31 wt% chromium and

0.32 wt% nickel.



Crater shape. The prerequisite for depth profiling applications is

uniform sputtering of the sample. Therefore, after completion of

the sputtering event, it is advisable to check the crater shape.

Ideally, the crater bottom should be flat with a minimum edge

effect and a crater wall that is perpendicular to the crater bottom.

The evolution of the crater profile with changes in the glow

discharge condition can be found in the literature.31 The crater

shape can also vary from one matrix specimen to another.

Therefore, as a first step we determined the discharge conditions

that lead to the optimum crater shapes for the base materials. Figs.

S1A and S1B represent the crater shape for aluminum and steel

base materials, respectively, after sputtering for 1.5 h. The

sputtering rate was 129.4 nm/min and 79 nm/min for aluminum

and steel, respectively, at discharge condition of 5 mA and 0.75

kV. The argon flow of 0.69 ml/min and 0.48 ml/min were used

for obtaining the discharge condition for aluminum and steel base

materials, respectively.

It is important to mention that the shape of the steel crater

profile is not ideal. However, as the idea was to profile the

steel-aluminum joint, a compromise had to be made.

Furthermore, any change in the glow discharge condition during

the sputtering process (with the aim of obtaining the optimum

crater) would influence the signal stability. Additionally, the

specimen analysis brought other challenges such as

matrix-specific calibration, the lack of direct access to aluminum

(stacked between two steel layers), and the slow sputtering rate of

Astrum GDMS instrument. Hence, the steel layer was manually

grinded, where the composite material was assumed to be steel

on top of the aluminum rather than the steel-aluminum bilayer.

Therefore, only one side of the composite material was

characterized.

For simplicity, the heat-treated composite material is referred

to as the “treated” sample while the non-heat-treated material is

referred to as the “untreated” sample. The crater shapes of the

untreated and treated samples are presented in Fig. 1, where the

Fig. 1 The crater profile of untreated (A) and treated (B) samples after

sputtering for 16 h 20 min and 8.5 h at glow discharge condition of 5 mA,

0.75 kV using argon flow rates of 0.85 ml/min and 0.83 ml/min

respectively.

Fig. 2 The lateral view of the untreated sample cut at the crater after

GDMS sputtering. Optical microscopy images of the crater at low

resolution (top) with a shaded area around which a high- resolution image

is taken (bottom).

Fig. 3 The lateral view of the treated sample cut at the crater after GDMS

sputtering. Optical microscopy images of the crater at low resolution (top)

with a shaded area around which a high-resolution image is taken

(bottom).

untreated sample was sputtered to a greater depth compared to

the treated sample. The total sputtering time for untreated and

treated samples were 16 h 20 min and 8 h 30 min corresponding

to sputtering rates of 143.9 nm/min and 111.3 nm/min,

respectively. The differences in sputtering depth and time are

related to inconsistency in the steel layer thickness in the samples.

The untreated sample (Fig. 2) had a thicker steel layer compared

to the treated sample (Fig. 3). The optical microscopy images

shown in Figs. 2 and 3 were taken after cutting the sample at the

crater, followed by investigation of the lateral sections of the

samples. Furthermore, the redisposition of steel and aluminum

layers outside the crater was clearer for the untreated sample

compared with the treated sample.

RSFs determination. In this work, the reported concentration of

impurities is based on the RSFs determined using certified

aluminum reference materials. Using Eq. 2, these values are

determined as the inverse of the slopes obtained from the linear

plots presented in Fig. 4 for chromium and nickel. The RSFs

were calculated at discharge conditions of 5 mA, 0.75 kV. The

RSFs values for 52Cr and 60Ni in aluminum were found to be 0.91

and 1.25, respectively.


Fig. 4 Abundance-corrected ion beam ratio curves as a function of mass fraction for chromium (A) and nickel (B) based on several

certified aluminum

reference materials.

Depth profiling. The GDMS bulk measurements of the

aluminum and steel base materials are presented in Table S1. The

same discharge conditions were used (5 mA, 0.75 kV) for the

base materials as for the composite materials to enable direct

comparison between them. As observed from the depth profile

(Fig. 5), the initial concentrations of chromium and nickel in steel

were comparable to the bulk concentrations in the steel base

material (Table S1). This observation was consistent for both

untreated and treated samples. Likewise, the bulk concentration

of nickel in the aluminum base material was consistent with its

concentration in the aluminum layer for both untreated and

treated samples. However, the concentration of chromium in the

aluminum layer was more than two orders of magnitude higher

than the bulk concentration in the aluminum base material. The

values were surprisingly high and therefore were verified by

using the complementary technique of EPMA. The EPMA

results confirmed a higher chromium concentration in the

aluminum layer (Table S2) compared to the bulk concentration in

the aluminum base material (Table S1). These EPMA

experiments were performed for both treated and untreated

samples. The average chromium concentration of 830 µg/g for

the treated sample measured by EPMA is comparable to the

approximate concentration of 900 µg/g measured from the

GDMS depth profile in the aluminum layer. A plausible

explanation for the higher chromium content in the aluminum

layer is the high diffusion coefficient of chromium in aluminum

compared to chromium in steel.32,33 Furthermore, it is worth

repeating that aluminum is stacked between two steel layers,

which may potentially increase the chromium diffusion to

aluminum. It is important to stress that the concentrations of both

chromium and nickel are slightly higher in the treated sample

than in the untreated one, which can be explained through heat

treatment of 400°C for 30 min. As the untreated sample was also

subjected to preheating before the rolling process, and diffusion

can occur as a result of exposure to elevated temperatures during

the rolling process. Therefore, this is probably why chromium

concentration in untreated sample is also high both in depth

profile (Fig. 5) and EPMA bulk measurement (Table S2) similar

to the treated sample.

Depth resolution. Depth resolution is often used to estimate the

ability of analytical techniques to measure local changes of

analytes as a function of depth. In general, for GDMS studies, the

depth (typically expressed in terms of µm or nm) between 16 %

and 84 % of analyte concentration is taken as the depth

resolution.34 The lower the depth resolution, the sharper the

transition of changes in the depth profile. In the present study,

untreated and treated samples exhibited depth resolutions of

about 29 µm and 13 µm, respectively (Fig. 5). For GDMS depth

profiling, an edge effect is non-homogenous sputtering of the

crater center as compared to the crater edge that contributes to a

reduced depth resolution. For the samples investigated in this

study, non-homogenous sputtering was greater for the untreated

sample than for the treated sample (Fig. 1), contributing to a

reduced depth resolution of the untreated sample profile. In

addition to the edge effect, the diffusion length of the analytes

and the layer thickness also affect the depth resolution. Wang et

al. demonstrated that a sample with a film thickness in the range

of 4–5 µm resulted in a depth resolution in the range of 0.5–0.7

µm.35 A slow-flow instrument (AutoConcept GD 90) was used in

that study, which is similar to the Astrum GDMS in terms of

operating parameters and sputtering rates.35 Therefore, poor

depth resolution is potentially linked to the thickness of the steel

layer for the samples used in the present study, which is

approximately 80 and 30 µm for the treated and untreated

samples, respectively (Figs. 2 and 3).

Intermetallic Layer. Enrichment of both the impurity elements

of chromium and nickel with two distinct transitions for each

element was observed at the interface for both the untreated and

treated samples. This can be attributed to the free surface energy

at the interface, which leads to the segregation of the impurity

elements. The same sample treated at a slightly higher


Fig. 5 Depth profile of iron and aluminum (A), chromium (B) and nickel (C) of untreated (left) and treated (right) steel-aluminum composite material after

subjecting to discharge condition of 5 mA, 0.75 kV using argon flow rates of 0.85 ml/min and 0.83 ml/min, respectively.

temperature (450°C for 2 h) has been shown to result in the

formation of two distinct phases within the intermetallic layer

(IML): Fe4Al13 adjacent to the aluminum layer and Fe2Al5

adjacent to the steel layer.36 However, such results were not

obtained for the samples used in this study. Since Fe4Al13 is

reported to form prior to Fe2Al5 at the interface, only one phase

might be present after the heat treatment at 400°C for 30 min, as

shown in Fig. 6. However, the IML formed was too thin to

generate EPMA results because of the 1 µm electron beam

cross-section. In this case, the two peaks (Fig. 5) might

correspond to differences in impurity concentrations of nickel

and chromium, where the smaller peak is a buildup in the steel

material, while the largest peak is the intermetallic layer.

Furthermore, the results clearly show that even a small amount of

chromium and nickel in the steel base material accumulates at the

interface, thereby influencing the IML formation and growth.

Previous studies have demonstrated the enrichment of impurities

at the interface where steel base materials with higher chromium


Fig.

6

Scanning electron microscopy images of steel-aluminum untreated (A) and treated (B) samples with dark grey and light grey parts representing the

aluminum and steel layers, respectively, with the intermetallic layer at the interface.

and nickel contents were used.5,37

Interestingly, the results from SEM and EDS indicate that

chromium-iron precipitates were found in the aluminum layer for

both treated and untreated samples, indicating diffusion of both

chromium and iron into the aluminum layer (Fig. S2 & Table S3).

Previous studies have indicated that diffusion of chromium

towards aluminum potentially prevents further migration of iron

to the aluminum layer.36

CONCLUSIONS

This study presents the first results of depth profile analysis of a

steel-aluminum bilayer using the Astrum GDMS instrument. The

slow sputtering rate of the Astrum GDMS posed challenges for

metallographic sample preparation, where grinding of the steel

layer was required and could not be reproduced. The composite

material was treated as a steel layer on top of aluminum, to allow

the use of RSFs from a certified aluminum reference material to

calibrate the data. The results of the study indicated an

enrichment of both chromium and nickel at the steel-aluminum

interface, which was higher in the heat-treated sample than in the

untreated one. There are valuable findings in this study on the

development of thin intermetallic layers caused by the short

heat-treatment time, and on the diffusion behavior of chromium.

The observation of higher chromium content in the aluminum

layer has not been reported in the literature as far as the authors’

knowledge. These results increase our knowledge of the

mechanisms that govern the formation and growth of

intermetallic phases and the influence of alloying elements. This

is helpful for optimizing the manufacturing of steel-aluminum

joints with regard to the material composition in the joints and

the optimal post-joining heat treatment.

ASSOCIATED CONTENT

Supporting Information. The Supporting Information (Figs.

S1-2, Tables S1-3) is available at www.at-spectrosc.com/as/home

AUTHOR INFORMATION


*G. Paudel


Notes


ACKNOWLEDGMENT

The authors are thankful to senior engineer Morten Peder Raanes

at the Department of Materials Science and Engineering at

NTNU for EPMA analysis. Likewise, the engineers Elin Harboe

Albertsen, Anita Storsve and Pei Na Kui from the same

department are thanked for continuous support in the laboratory.

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Zircon ZS - a Homogenous Natural Reference Material for U–Pb

Age and O–Hf Isotope Microanalyses

Xiao-Xiao Ling,a Qiu-Li Li,a,b,* Chuan Yang,c Zhu-Yin Chu,a Lian-Jun Feng,a Chao Huang,a

Liang-Liang Huang,a Hong Zhang,d Zhen-Hui Hou,e Jun-Jie Xu,a Yu Liu,a Guo-Qiang Tang,a,b Jiao Li,a and

Xian-Hua Li a,b a State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, P. R. China

b College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, P. R. China

c British Geological Survey, Keyworth, NG12 5GG, UK

d State Key Laboratory of Continental Dynamics, Northwest University, Xi’an 710069, P. R. China

e School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, P. R. China

Received: January 17, 2022; Revised: April 01, 2022; Accepted: April 01, 2022; Available online: April 06, 2022.

DOI: 10.46770/AS.2022.033

ABSTRACT: A well-formed natural zircon crystal ~10 g in weight from Sri Lanka was introduced as a reference material

for the geochemical microanalysis of U–Pb–O–Hf isotopes. For the U–Pb system, a total of 96 secondary ion mass spectrometry

(SIMS) and 174 laser-ablation inductively coupled plasma mass spectrometry analyses showed that zircon ZS was homogeneous

within a ~20 μm area level. According to chemical abrasion isotope dilution thermal ionization mass spectrometry, the U–Pb system

is concordant within the uncertainties, yielding a weighted mean 206Pb/238U age of 560.6 ± 1.3 Ma (2 standard deviation (SD), n =

18) and a weighted mean 207Pb/206Pb age of 561.1 ± 3.5 Ma (2SD, n = 18).

The U and Th concentrations were 570 ± 40 μg g-1 (1SD) and 132 ± 28 μg

g-1 (1SD), respectively. The homogeneity of the O isotopes was confirmed

by 261 SIMS analyses, and that of Hf isotopes was determined by 100 laser-

ablation multi-collector inductively coupled plasma mass spectrometry

(MC–ICP–MS) analyses. A weighted mean δ18O value of 13.69‰ ± 0.11‰

(2SD, n = 12) obtained by laser fluorination isotope ratio mass spectrometry

(IRMS) and a weighted mean 176Hf/177Hf value of 0.281668 ± 0.000010

(2SD, n = 7) by solution MC–ICP–MS are recommended as the best

reference values for zircon ZS.

INTRODUCTION

Zircon (ZrSiO4) is commonly found in various types of

intermediate-acid igneous rocks, metamorphic rocks, and

sedimentary rocks.1-5 The in-situ U–Pb age, Hf–O isotopes, and

geochemical composition of zircon can be obtained through the

use of microbeam analysis techniques such as secondary ion mass

spectrometry (SIMS) and laser-ablation inductively coupled

plasma mass spectrometry (LA–ICP–MS), which have a broad

range of applications in isotope geochronology and geochemical

tracer research.6-10 The matrix effect is one of the main concerns

that need to be considered during SIMS and LA–ICP–MS

microanalyses. Matrix-matched reference materials are key to

overcoming the matrix effect and obtaining accurate results. More

than 20 zircon age standards have been reported,11 with several

zircons such as SA01,12 Tanz,13 and SA02 serving as good

standards.14 However, qualified zircon reference materials are still

rare because they need to meet the following criteria: (1)

uniformity in radiogenic 207Pb/206Pb, 206Pb/238U, and 207Pb/235U; (2)

isotopic concordance; (3) a negligible concentration of initial Pb;

and (4) sufficient radiogenic Pb concentration.15 The first SIMS

zircon standard SL3, which has a high U content, can introduce

matrix effects during the SIMS calibration procedure.16 The low

radiogenic Pb makes it difficult for the widely used zircon standard


Fig. 1 Images of the zircon ZS that was collected from Sri Lanka. (a)

Photograph of the original zircon ZS crystal. (b) Microphotograph of a

portion of small zircon ZS fragments that were obtained after crushing. The

selected polished zircon ZS fragments that were embedded in epoxy are

presented in the reflection (c–e), back-scattered electron (BSE) (f–h), and

cathodoluminescence (CL) images (i–k).

9150017, 18 to monitor 207Pb/206Pb fractionations in SIMS tests. An

ideal zircon reference material should possess a relatively high U

and radiogenic Pb content while having negligible

metamictization damage. Zircon standard M257 meets the above

criteria, but its supply is limited.19 Here, we report a zircon

reference material with moderate Pb content. The results show that

this ~10 g zircon ZS crystal is homogenous with respect to U–Pb

ages and Hf–O isotopes, making it a suitable reference material for

microanalysis.

EXPERIMENTAL

Samples. A zircon crystal (named as ZS) purchased from mineral

dealers was collected from Sri Lanka. However, the exact

provenance and petrological characteristics of the crystal were

unknown. The brown, semitransparent mega-crystal had the most

extended dimension of approximately 3 cm and a total weight of

~10 g (Fig. 1a). This crystal was crushed into small shards (Fig.

1b) and cleaned twice with distilled water and ethanol. The small

fragments contained a small number of cracks, but no visible

inclusions were observed in the reflection (Figs. 1c–1e), back-

scattered electron (BSE) (Figs. 1f–1h), or cathodoluminescence

(CL) images (Figs. 1i–1k).

Instruments and operating conditions. The samples were

separated into four groups. Approximately 200 fragments (150–

300 μm) were selected from each group and randomly distributed

into four epoxy mounts with zircon reference materials

GBW04409 (Penglai),20 GBW04705 (Qinghu),21,22 91500,17,18

Tanz,13 TEMORA 2,23 and Plesovice24 for analysis. The mounts

were polished to obtain flat surfaces and expose the interior of the

grains.

Raman spectra. Raman spectra of the samples were obtained

using a LabRam micro-Raman spectrometer (Horiba Jobin Yvon,

Paris, France) at the Institute of Geology and Geophysics, Chinese

Academy of Sciences (IGGCAS), Beijing, China. A He–Ne laser

(532 nm) was used for excitation. A charge-coupled device

detector with a 50× magnification working-distance objective

were used. The spectral resolution of the instrument was ~2 cm–1.

Further description can be found in Ling et al. 25

U–Pb Age

SIMS. U, Th, and Pb isotope analyses were conducted using a

CAMECA IMS-1280HR SIMS at the IGGCAS in Beijing. Four

analytical sessions were performed on each of the four mounts. A

description of the instruments used for analyses and the analytical

procedure can be found in Li et al. (2009), and only a brief

summary is provided here.26 The primary O2– ion beam spot was

approximately 20 μm 30 μm in size and had a 6–9 nA intensity.

A single electron multiplier was used in the ion-counting mode to

measure the secondary ion beam intensities using the peak

jumping mode, with each measurement consisting of seven cycles.

In the analyses, the zircon standard was interspersed with

unknown grains. Pb/U calibration and the concentrations of U and

Th were calibrated relative to the zircon standard Tanz during

sessions 1 and 4, zircon standard TEMORA 2 during session 2,

and zircon standard 91500 during session 3. A second standard,

GBW04705 (Qinghu), was also analyzed with zircon grains as an

unknown. Long-term uncertainty for the 206Pb/238U measurements

of the standard zircons was propagated to the unknowns (1 relative

standard deviation (RSD) = 1.5%).27, 28 By using the measured

non-radiogenic 204Pb signals and an average of the present-day

crustal composition, the measured compositions were corrected.29

LA–ICP–MS. Forty grains of zircon ZS were analyzed during the

first session to determine U–Pb dating with a Thermo iCAP RQ

ICP–MS coupled with a Resonetics Resolution S-155 193 nm

laser ablation system at the State Key Laboratory of Continental

Dynamics (Northwest University. Xi’an, China). The analyses

were performed at a pulse rate of 6 Hz, beam energy of 4 J/cm2,

and spot diameter of 30 μm. Zircon standard 91500 was used to

calibrate the mass discrimination and elemental fractionation.

GBW04705 (Qinghu) was used as the secondary standard.

Background and analytical signal integration, time-drift correction,

and quantitative calibration were performed for the U–Pb isotopes

using Iolite_v4.0 software.30

The second and third zircon dating analytical sessions were

performed using an ArF excimer laser system (GeoLas Pro, 193

nm wavelength) coupled with a quadrupole ICP–MS (Agilent

7700e) at the University of Science and Technology of China

(USTC). A total of 26 and 59 grains were subjected to analyses in

the second and third sessions, respectively. All analyses were

performed at a laser energy of 10 J/cm2, beam diameter of 32 μm,


and repetition rate of 10 Hz. The sample signal and background

were measured at approximately 60 and 30 s, respectively.

Additional details on the analytical techniques used in the U–Pb

analyses in this study are available in Hou et al. 31 The zircon

standard 91500 and zircon standard SA01 were used as external

standards. U/Pb ratios were processed using the macro program

LaDating@Zrn written in Excel spreadsheet software. Common

Pb was corrected using the method described by Andersen.32

The fourth LA–ICP–MS U–Pb analysis session was conducted

at Wuhan Sample Solution Analytical Technology Co., Ltd.,

Wuhan, China. Fifty spots were analyzed on 20 different zircon

ZS grains using a GeoLas HD 193 nm laser coupled with an

Agilent 7900 ICP–MS instrument. The analytical conditions

included an ablation pit of 32 µm diameter and a repetition rate of

5 Hz. Elemental fractionation and instrumental mass

discrimination were corrected by normalization to the reference

zircon GJ-1,33 which was analyzed during the analytical session

under the same conditions as the samples. For further details on

the analytical protocols and data processing methods used for the

U–Pb analyses, see Liu et al. 34

Chemical abrasion isotope dilution thermal ionization mass

spectrometry (CA–ID–TIMS). Approximately 100 small zircon

ZS shards (100–150 μm) were selected under a binocular

microscope for CA–ID–TIMS analysis at IGGCAS and the

British Geological Survey (BGS, UK). The methods used in both

laboratories were similar and are discussed in the following

paragraphs.

CA–ID–TIMS (IGGCAS). The CA–ID–TIMS procedures

implemented at IGGCAS were similar to those reported by Chu et

al.35 and Wang et al.;36 therefore, only a brief description is

provided here. The small zircon ZS fractions were pretreated using

a chemical abrasion procedure. The fragments were placed in a

quartz dish, annealed at 900 °C for 60 h in a muffle furnace, and

then cleaned twice with HNO3 and Milli-Q water at room

temperature. The shards were loaded into Teflon microcapsules

containing HF and placed in an oven at 200 °C for 12 h for

leaching. The leached zircon grains were washed in two cycles of

Milli-Q water, 6 mol L-1 HCl, and HNO3, left on a hotplate (120 °C)

for 1 h, and then immersed for 1 h in an ultrasonic bath. Sixty

thoroughly rinsed grains were transferred under a microscope

using a pipet (set to approximately 3 µL), and the grains were

loaded into 13 small 300 L Savillex PFA capsules and spiked

with an in-house mixed 205Pb-235U isotopic tracer solution (IGG-

1) (Xu et al., in resubmission). Dissolution was achieved using

∼100 L of concentrated hydrofluoric acid (HF) in Teflon

microcapsules in a Parr pressure vessel placed in a 220 °C oven

for 48 h. The dissolved zircon solution was successively dried to

salts at 80 °C on a hotplate and redissolved in Teflon

microcapsules in a Parr pressure vessel with ∼150 L of 6 mol L-

1 HCl at 180 °C for 12 h. After redrying and redissolving in ∼50

L of 3 mol L-1 HCl, Pb and U were chemically purified from the

solutions through 50 L microcolumns of anion exchange resin

and were finally dried with the addition of 10 L of 0.05 mol L-1

phosphoric acid (H3PO4). Purified Pb and U were loaded onto

single predegassed zone-refined Re filaments with 3 µL of silica

acid gel emitter solution. The Pb and U isotopic ratios were

successively measured on a Thermo Triton Plus thermal ionization

mass spectrometer (TIMS) in peak-jumping mode using a

secondary electron multiplier (SEM) at IGGCAS. The Pb isotopes

were measured as mono-atomic ions (Pb+), and uranium was

measured as oxide ions (UO2+). The SEM dead times for Pb and

U were set to 15 and 16 ns, respectively. The Pb isotope

fractionation effect was corrected by an external method using

NBS981 Pb as a reference standard (Pb fractionation coefficient:

0.16 ± 0.08%, 2σ). The U isotope fractionation effect was

corrected by an external method using U500 as the reference

standard (U fractionation coefficient: 0.058 ± 0.028%, 2σ). The

total common Pb in each zircon analysis was attributed to

laboratory Pb.

CA–ID–TIMS (BGS). Seven zircon shards were analyzed for

U and Pb isotopes using the CA–ID–TIMS method at the BGS.

The methods were similar to those of Yang et al.37 After annealing,

the zircon shards were chemically abraded in HF and HNO3 in an

oven set at 210 °C for 12 h. The zircon ZS samples were spiked

with mixed 202Pb–205Pb–233U–235U (ET2535) EARTHTIME

isotopic tracer solutions. U and Pb isotopic measurements were

performed using a Thermo Fisher Scientific Triton thermal

ionization mass spectrometer.

Both laboratories used the Tripoli and ET_Redux applications

to conduct data reduction, date calculations, and uncertainty

propagation. Corrections to the 206Pb/238U and 207Pb/206Pb ages for

the initial 230Th disequilibrium were determined assuming a Th/U

ratio of 2.8. The decay constants were obtained from Jaffey et al.38

A 238U/235U isotopic composition of 137.818 ± 0.045 was used.39

Oxygen Isotope Measurements. The homogeneity of the zircon

oxygen isotopes was checked using a CAMECA IMS-1280 SIMS

in IGGCAS. The primary beam Cs+ ions were accelerated at 10

kV and focused using a Gaussian mode with an intensity of 1.0–

2.0 nA. The analytical spot size was approximately 20 μm in

diameter, with a 10 µm beam diameter and 10 µm raster area. The

signals of 16O and 18O were collected simultaneously using two

Faraday cups in the multi-collection mode. The charge effect was

compensated by a normal electron gun.40

Isotope ratio mass spectrometry (IRMS). The oxygen isotopes

of the zircon ZS crystals were acquired using a laser-based

fluorination oxygen-extraction line at the Stable Isotope

Laboratory. The Chinese national reference material GBW04409

(Penglai) was used as a quality monitor. A detailed description of

the instrumentation and analytical procedures can be found in


Fig. 2 Raman spectra of the zircon samples. The main zircon Raman bands

were found at around ~356, ~439, and ~1008 cm−1.

Feng et al. 41 The Isoplot 3.75 software package was used for data

processing and calculations.42

Hf isotope homogeneity and determination. A total of 100 in-

situ Lu–Hf isotope measurements were carried out in two sessions

on two mounts on a Neptune Plus MC–ICP–MS (Thermo Fisher

Scientific, Germany) coupled with a Geolas Pro 193 nm excimer

laser ablation system (Coherent, USA) at IGGCAS. The analytical

spot was 44 μm in diameter. Laser had a frequency of 4 Hz and a

beam energy density of 6 J/cm2, and the ablation time was 26 s.

Zircon standards GBW04409 (Penglai), Mud Tank,43 SA01, and

SA02 were used to monitor the instrumental situation. For more

experimental details and data reduction procedures, see Wu et al.

(2006).44 Seven aliquots of the zircon ZS powders were analyzed

by means of solution MC–ICP–MS. All chemical preparations and

Hf isotope analyses by ICP–MS of zircon ZS were conducted at

Wuhan Sample Solution Analytical Technology Co., Ltd., Wuhan,

China. The procedures used to separate Hf were similar to those

reported by Zhang and Hu.45 The BCR-2 and AGV-2 rock powder

reference materials were analyzed during the same session, and the 176Hf/177Hf ratios were determined to be 0.282859 ± 0.000008

(2SD) and 0.282973 ± 0.000008 (2SD), respectively, which were

consistent with the values reported in previous studies.45

Trace element concentration. The trace elements in the zircon

ZS samples were determined using LA–ICP–MS at Wuhan

Sample Solution Analytical Technology Co., Ltd. For more details

regarding the experimental and data reduction procedures, see Hu

et al. and Liu et al.46, 47 An Agilent 7900 ICP–MS equipped with a

GeoLas HD 193 nm laser ablation system was used. External

calibration was performed relative to NIST610 standard glass.48

ARM-1 glass49 and SA01 zircon were used for quality control

monitoring.


Structural study using Raman spectroscopy. We measured the

Raman spectra of six well-characterized zircon standards: 91500,

TEMORA 2, Plesovice, GBW04705 (Qinghu), GBW04409

(Penglai), and ZS. Representative Raman spectra obtained from

the analyzed samples are shown in Fig. 2. The spectra of all zircon

samples are similar to those of crystalline zircons that have

experienced few alpha decay events.50 All samples exhibited

characteristic peaks at approximately ~356, ~439, and ~1008 cm−1,

which are reference peaks from those of synthetic zircon. These

peaks were interpreted to represent the internal vibrations of the

SiO4 tetrahedra.51 The most intense peaks observed for

GBW04409 (Penglai), 91500, ZS, TEMORA 2, GBW04705

(Qinghu), and Plesovice zircons were visible at 1008, 1006, 1004,

1004, 1004, and 1001 cm−1, respectively. The slight shift from the

standard 1008 cm−1 was attributed to radiogenic damage.52 The

approximate radiation dose that has been absorbed by a zircon

material can be evaluated based on the U–Th concentration and

age.53 The degree of lattice damage can be described as

“displacements-per-atom (dpa)”. The Ddpa (dose in dpa) of a zircon

ZS grain can be calculated based on the recommended TIMS age

and U and Th concentrations. With increasing Pb accumulation,

the Ddpa of zircon became more significant, and the Raman spectra

of the zircons changed gradually (Fig. 2). Considering that the U

(480–670 μg g-1) and Th (90–190 μg g-1) contents are variable

(results from the trace elements section in this study), the zircon

ZS undergoes radiation damage over a dose range of 9.6–13.6 ×

1014 α-decay events/mg (0.046–0.065 Ddpa). This relatively low

Ddpa value demonstrates that zircon ZS experiences no disturbance

during the oxygen isotope test and would not cause a “high-U

matrix effect” during SIMS age dating.54

U–Pb Age of zircon ZS. The isotopic U–Pb ratios and ages of the

zircon ZS shards obtained using SIMS are listed in Table S1. A

summary of the zircon U–Pb measurements is presented in Table

1 and displayed in Fig. 3. The uncertainties of the individual zircon

analyses are reported at the 1σ level in the data tables. The four

SIMS analytical sessions included 12, 12, 16, and 56 analyses in

sessions 1, 2, 3, and 4, respectively. Each point shows a concordant

U–Pb age within the uncertainties. The Concordia U–Pb ages

determined from each session were 559 ± 5 Ma (2SE (standard

error), n = 12, Fig. 3a), 559 ± 5 Ma (2SE, n = 12, Fig. 3b), 560 ±

5 Ma (2SE, n = 16, Fig. 3c), and 561 ± 3 Ma (2SE, n = 56, Fig.

3d), respectively. The average ages of 206Pb/238U and 207Pb/206Pb

were 560 ± 2 Ma (2SE, n = 96, Figs. 3i and 3j) and 562 ± 4 Ma

(2SE, n = 95, with one spot with a relatively high common Pb

discarded (Figs. 3k and 3l), respectively. The data for individual

LA–ICP–MS U–Pb analyses are listed in Table S2. The internal

error determined during LA–ICP–MS zircon U–Pb dating was

approximately 1%. The respective LA–ICP–MS Concordia ages

calculated from the four sessions were as follows: 564 ± 4 Ma

(2SE, n = 40, Fig. 3e), 557 ± 5 Ma (2SE, n = 26, Fig. 3f), 563 ± 3

Ma (2SE, n = 59, Fig. 3g), and 554 ± 2 Ma (2SE, n = 49, with one

spot with a small inclusion being discarded, Fig. 3h). The


Table 1 Summary of the U–Pb dating methods for zircon ZS from different laboratories

Analytical Methods Lab and Sessions 207Pb/206Pb age 206Pb/238U age

Standards n Weighted Mean 1SD, Ma RSD, % Weighted Mean 1SD, Ma RSD, %

SIMS

IGGCAS-session1 561 27 5 559 2 0.3 Tanz 12

IGGCAS-session2 558 11 2 559 4 0.8 TEMORA 2 12

IGGCAS-session3 565 16 3 560 3 0.6 zircon 91500 16

IGGCAS-session4 562 13 2 561 4 0.7 Tanz 56

LA–ICP–MS

NWU-session1 560 35 6 564 7 1.2 zircon 91500 40

USTC-session2 576 34 6 557 6 1.1 SA01 26

USTC-session3 574 35 6 563 6 1.1 SA01 59

WHSS-session4 572 40 7 554 3 0.6 GJ-1 49

CA–ID–TIMS IGGCAS-session1 561.1 2.11 0.38 560.1 0.59 0.11 / 11

BGS-session2 561.1 1.03 0.18 560.7 0.53 0.09 / 7

IGGCAS: Institute of Geology and Geophysics, Chinese Academy of Sciences, China; NWU: State Key Laboratory of Continental Dynamics (Northwest

University); USTC: Chinese Academy of Sciences (CAS) Center for Excellence in Comparative Planetology at the University of Science and Technology of

China; WHSS: Wuhan Sample Solution Analytical Technology Co. Ltd., China; BGS: British Geological Survey, UK.

Fig. 3 The U–Pb ages of zircon ZS. (a–d) The Concordia diagrams show SIMS U–Pb dating results and (e–h) the LA–ICP–MS U–Pb dating results. The

average (i) (k) and histograms (j) (l) show the 206Pb/238U age and the 207Pb/206Pb age results obtained during SIMS. The average (m) (o) and histograms (n) (p)

show the 206Pb/238U age and the 207Pb/206Pb age results obtained during LA–ICP–MS. All error symbols are given at 2σ.

systematic uncertainties of LA–ICP–MS zircon U–Pb dating

should be considered.55 Then, systematic uncertainties of 2% (2σ)

for the 206Pb/238U and 0.55% (2σ) for the 207Pb/206Pb

measurements were propagated to the samples. The average age


Table 2. U–Pb TIMS Zircon Data

Sample Pb (pg) a Th/U b

Isotopic Ratios

ρd

Dates (Ma)

206Pb/204Pb c 207Pb/206Pb 207Pb/235U 206Pb/238U 207Pb/206Pb 207Pb/235U 206Pb/238U

Ratios ±2σ % Ratios ±2σ % Ratios ±2σ % Ratios ±2σ (abs) Ratios ±2σ (abs) Ratios ±2σ (abs)

1 -Z1 4.3 0.22 15655 0.0590 0.12 0.7367 0.25 0.0907 0.18 0.89 564.4 2.7 560.5 1.1 559.5 1.0

1 -Z2 5.2 0.23 10760 0.0588 0.30 0.7373 0.53 0.0910 0.33 0.84 558.9 6.7 560.8 2.3 561.3 1.8

1 -Z3 4.5 0.23 22274 0.0588 0.18 0.7357 0.29 0.0908 0.19 0.78 558.8 4.0 559.9 1.3 560.2 1.0

1 -Z4 3.5 0.24 21181 0.0588 0.17 0.7353 0.29 0.0907 0.23 0.81 558.4 3.7 559.6 1.3 560.0 1.2

1 -Z5 9.8 0.21 8293 0.0588 0.18 0.7360 0.35 0.0908 0.26 0.85 559.1 4.1 560.0 1.5 560.3 1.4

1 -Z6 8.3 0.21 6344 0.0589 0.28 0.7372 0.35 0.0909 0.21 0.58 560.8 6.2 560.7 1.5 560.7 1.1

1 -Z7 9.4 0.24 14104 0.0588 0.20 0.7348 0.38 0.0906 0.29 0.86 560.5 4.3 559.4 1.7 559.1 1.6

1 -Z8 5.9 0.22 18751 0.0588 0.19 0.7357 0.36 0.0908 0.24 0.87 559.3 4.2 559.9 1.5 560.0 1.3

1 -Z9 3.4 0.28 10079 0.0588 0.14 0.7364 0.22 0.0908 0.13 0.79 559.5 3.1 560.3 0.9 560.5 0.7

1 -Z10 4.6 0.23 17446 0.0588 0.11 0.7363 0.20 0.0908 0.12 0.87 559.3 2.4 560.2 0.9 560.5 0.6

1 -Z11 7.8 0.20 4573 0.0589 0.17 0.7372 0.24 0.0908 0.13 0.69 562.6 3.8 560.8 1.0 560.3 0.7

1 -Z12 4.3 0.23 21394 0.0589 0.10 0.7350 0.24 0.0906 0.18 0.91 561 2.3 559.5 1.0 559.1 1.0

1 -Z13 8.3 0.23 23707 0.0589 0.10 0.7357 0.22 0.0906 0.16 0.89 563.9 2.4 559.9 1.0 558.9 0.9

2-Z1 0.7 0.22 164007 0.0589 0.03 0.7377 0.10 0.0909 0.08 0.90 561.0 1.0 561.1 0.4 561.1 0.4

2-Z2 0.5 0.20 31065 0.0588 0.13 0.7355 0.17 0.0907 0.08 0.63 559.3 2.9 559.8 0.7 559.9 0.4

2-Z3 0.4 0.20 21972 0.0589 0.09 0.7362 0.15 0.0907 0.07 0.88 561.2 2.0 560.2 0.6 559.9 0.4

2-Z4 0.4 0.21 8464 0.0589 0.09 0.7380 0.12 0.0909 0.05 0.55 562.6 2.2 561.3 0.5 560.9 0.2

2-Z5 0.3 0.20 8299 0.0589 0.10 0.7370 0.12 0.0908 0.04 0.47 561.0 2.3 560.6 0.5 560.5 0.2

2-Z6 0.3 0.21 4642 0.0589 0.14 0.7378 0.16 0.0909 0.05 0.47 561.3 3.1 561.1 0.7 561.1 0.3

2-Z7 0.3 0.21 3005 0.0588 0.18 0.7373 0.19 0.0909 0.04 0.35 560.1 4.0 560.8 0.8 561.0 0.2

a Total mass of common Pb. All common Pb assumed to be laboratory blank. Blank isotopic composition at IGG: 206Pb/204Pb = 17.78 ± 0.50, 207Pb/204Pb = 15.31 ± 0.34, 208Pb/204Pb = 38.51 ± 0.57. b Th contents calculated from radiogenic 208Pb and 230Th-corrected 206Pb/238U date of the sample, assuming concordance between U–Pb and Th–Pb systems. c Measured ratio corrected for fractionation and spike contribution only. d Error correlation between 207Pb/235U and 206Pb/238U.

Table 3. Summary of the oxygen isotopes for zircon ZS

Methods Sample δ18O (‰, VSMOW)

n Standards Mean 2SD

SIMS

ZS-session1 13.78 0.23 30

GBW04409 Penglai

ZS-session2 13.81 0.30 20

ZS-session3 13.86 0.36 22

ZS-session4 13.76 0.24 30

ZS-session5 13.86 0.34 159

IRMS ZS 13.69 0.11 12


Fig. 4 Concordia diagram of the zircon ZS grains obtained using CA–ID–TIMS. (a) The green symbols represent the IGGCAS results, and (a) the blue

symbols represent the BGS results.

Fig. 5 Mean value and frequency distributions of δ18O values of the zircon ZS: (a), (b), (c), (d), and (e) are the δ18O SIMS values obtained for zircon ZS

during sessions 1, 2, 3, 4, and 5, respectively. (f) shows the δ18O RIMS values for zircon ZS.


of 206Pb/238U was 558 ± 2 Ma (2SE, n = 174; Figs. 3m and 3n).

The average age of 207Pb/206Pb was determined to be 564 ± 7 Ma

(2SE, n = 174; Figs. 3o and 3p). Both SIMS and LA–ICP–MS data

indicated that zircon ZS has a uniform U–Pb age.

Twenty CA–ID–TIMS analyses were performed on the zircon

ZS. The complete TIMS U–Pb isotopic ratios are listed in Table 2

and the U–Pb results are presented in Fig. 4. All errors that were

observed during the TIMS analyses are quoted in the text, tables,

and error ellipses in the Concordia diagram and given at 2σ.

Thirteen analyses were conducted at IGGCAS, and two of these

analyses with significantly higher uncertainty for the 206Pb/238U

ages (2σ > 0.25%) were rejected. The other eleven analyses

yielded a weighted mean 206Pb/238U date of 560.1 ± 0.4 Ma (2SE,

n = 11) and a weighted mean 207Pb/206Pb date of 561.1± 1.5 Ma

(2SE, n = 11). The analytical precision of the weighted mean 206Pb/238U ages for these standard zircons was approximately 0.1%

(2 RSE). Seven analyses carried out at BGS yielded a weighted

mean 206Pb/238U date of 560.7 ± 0.4 Ma (2SE, n = 7) and a

weighted mean 207Pb/206Pb date of 561.1 ± 0.7 Ma (2SE, n = 7).

By combining all the above analyses, the recommended 206Pb/238U

age of the zircon ZS and the 207Pb/206Pb age were obtained as 560.6

± 1.3 Ma (2SD, n = 18) and 561.1 ± 3.5 Ma (2SD, n = 18),

respectively.

Oxygen Isotopes Using SIMS and IRMS. Oxygen isotopic

measurements were performed in five sessions to evaluate micron-

level homogeneity. The raw data from the five sessions are shown

in Table S3, and a summary of the SIMS data is listed in Table 3.

The GBW04409 zircon (Penglai) was analyzed to monitor the

instrument conditions. The analytical precision 2SD of the five

sessions obtained for the GBW04409 zircon were 0.14‰, 0.23‰,

0.27‰, 0.33‰, and 0.41‰, respectively, indicating that the

instrument was stable throughout all five sessions. The first to

fourth sessions were conducted in four mounts to determine inter-

grain variations in the oxygen isotopes as individual

measurements of 30, 20, 22, and 30 ZS fragments. The fifth

session consisted of 159 measurements conducted on 50 grains.

Two to four spots were performed on each shard to examine

intragrain variability. The measurements from all five sessions

form Gaussian distributions with mean δ18O (‰) values of 13.78

± 0.23‰ (2SD, n = 30; Fig. 5a), 13.81 ± 0.30‰ (2SD, n = 20; Fig.

5b), 13.86 ± 0.36‰ (2SD, n = 22; Fig. 5c), 13.76 ± 0.24‰ (2SD,

n = 30; Fig. 5d), and 13.86 ± 0.34‰ (2SD, n = 159; Fig. 5e),

respectively. Homogeneity can be proven by comparing the SDs

Table 4. Summary of the Hf isotopes for zircon ZS

Methods Sample 176Hf/177Hf 2SD n Standards

LA–MC–

ICPMS

ZS-

Session1 0.281674 0.000047 60

SA01 ZS-

Session2 0.281678 0.000054 40

Solution MC–

ICPMS ZS

0.281668 0.000010 7 /

Fig. 6 The Hf isotopic results of zircon ZS. (a) Average and (b) histogram

of 176Hf⁄177Hf measurements for 100 zircon ZS spots determined by LA–

MC–ICP–MS. (c) Average of 176Hf⁄177Hf measurements determined by

solution MC–ICP–MS. Error bars are 2σ.

of the measured results with those of well-demonstrated standards.

Normally, the 2SD of SIMS oxygen analysis for zircon standards

is routinely 0.3‰–0.5‰.56 SIMS oxygen analysis can achieve

excellent repeatability (2SD < 0.3‰), but only under ideal

measurement conditions.57, 58 The results indicate that the intra- or

intergrain variations observed in the oxygen isotopes are

sufficiently small to show the homogeneity of the zircon ZS. The

oxygen isotope compositions obtained using IRMS are

summarized in Table 3. The recommended value for zircon ZS

was 13.69 ± 0.11‰ (2SD, n = 12; Fig. 5f) based on 12 analyses.

The δ18O value determined by SIMS was consistent with that

determined by IRMS within uncertainty.

MC–ICP–MS Hf isotope data. The complete isotopic ratios of

Lu–Hf are listed in Table S4. A summary of these analyses is

provided in Table 4. The mean Lu–Hf isotopic results for SA01,

Mud tank, GBW04409 (Penglai), and SA02 zircon reference

materials under the same conditions were determined to be

0.282282 ± 0.000047 (2SD), 0.282502 ± 0.000042 (2SD),

0.282928 ± 0.000049 (2SD), and 0.282294 ± 0.000061 (2SD),

respectively, which are consistent with the recommended results

within uncertainties. Sixty individual Hf isotope analyses during

session 1 obtained 176Hf⁄177Hf values ranging from 0.281620 to

0.281753. Forty zircon ZS shards analyzed in session 2 showed 176Hf⁄177Hf values ranging from 0.281618 to 0.281732. The 100

Lu–Hf isotope measurements formed a Gaussian distribution with

a grand mean of 0.281676 ± 0.000050 (2SD, n = 100; Figs. 6a and

6b). Seven aliquots of zircon ZS were obtained by solving the

MC–ICP–MS measurements, and the yield of 176Hf/177Hf values

ranged between 0.281663 and 0.281675. All seven 176Hf/177Hf

datasets had a weighted mean of 0.281668 ± 0.000010 (2SD, n =

7; Fig. 6c), which was identical to the mean of the LA–MC–ICP–

MS results within uncertainties.

According to the laser ablation and solution measurements

conducted in this study, the zircon ZS mega crystal has relatively

homogeneous Hf isotopic compositions. The mean value of 176Hf/177Hf = 0.281668 ± 0.000010 (2SD) determined by solution

MC–ICP–MS measurements is the best recommended value for

zircon ZS.


Table 5 Trace element composition of zircon ZS

Element

ZS-Session1

n = 22

ZS-Session2

n = 50

Mean 1SD Mean 1SD

Li 1.9 0.5 / /

Ti 3.80 0.70 3.51 0.71 Y 104 78 134 86

Nb 0.29 0.07 0.30 0.08

La 0.002 0.003 0.008 0.018 Ce 1.67 0.17 1.70 0.22

Pr 0.04 0.05 0.06 0.06

Nd 0.64 0.66 0.96 0.84 Sm 1.0 1.1 1.3 1.1

Eu 0.21 0.20 0.29 0.25

Gd 3.2 2.9 4.4 3.4 Tb 0.91 0.79 1.23 0.92

Dy 9.6 7.5 12.9 8.9

Ho 3.3 2.5 4.4 2.9 Er 15 10 19 12

Tm 2.9 2.0 3.8 2.3

Yb 26 17 34 20 Lu 4.7 3.0 6.3 3.6

Hf 8454 126 8584 173

Ta 0.08 0.03 0.07 0.03 Pb 50 2 55 4

Th 127 24 134 30

U 551 23 577 44 Th/U 0.23 0.04 0.23 0.04

∑REE/μg g-1 173 / 224 /

δEu 0.12 / 0.12 / Ce/Ce* 168 / 75 /

Fig. 7 Chondrite-normalized REE patterns of the LA–ICP–MS data of 18

zircon ZS grains.

Chemical Composition. The individual LA–ICP–MS chemical

results for the zircon ZS determined in this study are listed in Table

S5. Two to three spots were performed on eight ZS grains in the

first session, and 50 spots were performed on 20 different grains

of zircon ZS in the second session. The trace element

concentration data (Table 5) showed small variations between the

different shards in the zircon ZS crystals. Zircon rare earth

elements (REEs) abundances are normalized to chondrite

meteorite values and plotted in Fig. 7.59 Heavy rare earth elements

(HREEs) are enriched relative to light rare earth elements

(LREEs), with negative Eu (δEu = ~0.12, Table 5) and significant

positive Ce anomalies (Ce/Ce* varies from 75 to 168). All

analyses resulted in the following values: U = 570 ± 40 μg g-1

(1SD), Th = 132 ± 28 μg g-1 (1SD), Hf = 8544 ± 171 μg g-1 (1SD),

and a Th/U ratio of 0.23 ± 0.04 (1SD). The total REE content

varied between 43–186 μg g-1.

CONCLUSIONS

Raman spectroscopy, SIMS, IRMS, ICP–MS, and TIMS were

used to evaluate a 10 g natural zircon ZS crystal. The Raman

results showed that zircon ZS was well crystallized without

metamict. The analytical results via SIMS and LA–ICP–MS

suggest that the samples are homogeneous in terms of U–Pb–O–

Hf isotope composition at the ~20 µm scale, indicating that the

crystal is a qualified reference material for SIMS U–Pb–O isotopic

analysis and for LA–ICP–MS Hf isotope measurement. As

determined by CA–ID–TIMS, the recommended 206Pb/238U

values and 207Pb/206Pb age for zircon ZS are 560.6 ± 1.3 Ma (2SD)

and 561.1 ± 3.5 Ma (2SD), respectively. The weighted mean δ18O

value of 13.69 ± 0.11‰ (2SD) determined by laser fluorination

IRMS and the mean 176Hf⁄177Hf value of 0.281668 ± 0.000010

(2SD) determined by solution MC–ICP–MS are the best reference

values for zircon ZS.

All zircon materials are available from the corresponding author

if required.

ASSOCIATED CONTENT



AUTHOR INFORMATION

Qiu-Li Li is a full professor of

Geochemistry at the Institute of Geology

and Geophysics, Chinese Academy of

Sciences (IGGCAS). He received his B.S.

degree in Geology from Northwest

University (NWU, Xi’an city) in 1998,

and completed Ph.D in Geochemistry

from University of Science and

Technology of China. He worked as

postdoctoral research fellow at the

IGGCAS (2003-2007), associate

professor from 2008 to 2012 and full professor since 2013 at the IGGCAS.

From 2017, he started to work as director of ion probe laboratory at the

IGGCAS. From 2022, he acts as chairman of Microbeam Analysis


Professional Committee of Chinese society for mineralogy petrology and

geochemistry. His research focused on micro area isotope geochemistry.

Based on the ion probe platform, he has developed a variety of in-situ U-

Th-Pb dating methods for many accessary minerals, which provides

effective dating means for solving geological chronological problems such

as ultra-alkaline rocks, carbonates, clastic sedimentary rocks, medium and

low temperature metamorphic rocks, low-temperature deposits and

extraterrestrial samples; Through the analysis of small beam spots of

zirconium containing minerals, he accurately determined that the Chang'e-

5 basalt was formed 2 billion years ago. He has been working as member

of editorial board for Atomic Spectroscopy. Qiu-Li Li published more than

50 SCI papers of the first or corresponding author in journals such as Nature,

Nature Communications and EPSL, and other more than 180 cooperative

papers.


*Q.-L. Li


Notes


ACKNOWLEDGMENTS

The authors gratefully thank the National Key Research and

Development Plan (2018YFA0702600) and the National Natural

Science Foundation of China (41773044, 41773047, and

42173037) for financially supporting this work. We thank Anton

Azaro for providing the zircon materials. We also appreciate

technical assistance from Hongxia Ma, Lanzhen Guo, Hongwei Li,

Zheng Liu, Lu Chen, and Wenquan Fan. Lastly, the authors

gratefully acknowledge the detailed reviews from the editors and

the reviewers.

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https://doi.org/10.1016/0009-2541(94)00140-4


Cadmium Isotope Analysis of Environmental Reference Materials

via Microwave Digestion–Resin Purification–Double-Spike MC-

ICP-MS

Qiang Dong,a,b,c Cailing Xiao,d Wenhan Cheng,e Huimin Yu,e Jianbo Shi,b Yongguang Yin,a,b,c,f,*

Yong Liang,d,* and Yong Cai a,b,g a Laboratory of Environmental Nanotechnology and Health Effect, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing

100085, P.R. China

b State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,

Beijing 100085, P.R. China

c University of Chinese Academy of Sciences (UCAS), Beijing 100049, P.R. China

d Institute of Environment and Health, Jianghan University, Wuhan 430056, P.R. China

e CAS Key Laboratory of Crust–Mantle Materials and Environments, School of Earth and Space Sciences, University of Science and Technology of China,

Hefei 230026, P.R. China

f School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, P.R. China

g Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA

Received: November 13, 2021; Revised: January 27, 2022; Accepted: January 27, 2022; Available online: February 07, 2022.

DOI: 10.46770/AS.2021.1109

ABSTRACT: Cadmium isotope fractionation is a promising indicator for tracing the source, transport, and transformation of

Cd in the environment; therefore, a high-precision method for the Cd isotope analysis of environmental samples is urgently required.

In this study, eight environmental reference materials (NIST 2711a, GSS-1, GSS-4, GSS-5, GSD-11, GSD-12, GSD-30, and BCR-

679) with different matrices were digested under microwave irradiation and purified via anion exchange. Thereafter, their Cd isotope

ratios were analyzed using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) with double-spike

correction. The samples digested under microwave irradiation exhibited high Cd recovery (> 96%). One step of anion-exchange-

based purification can remove most interfering elements without any detectable loss of Cd. If the purified solution contained Zn/Cd >

0.04, Zr/Cd > 0.01, Mo/Cd > 0.2, Pd/Cd > 4 × 10−5, In/Cd > 0.02, or Sn/Cd > 0.1, a secondary step using the same purification

procedure would be necessary. The measured δ114/110Cd values of

reference materials (from −0.558 to 0.550‰) were in adequate

agreement with those of previous studies, suggesting that this method

can be used to analyze the Cd isotope ratios in soil, sediment, and plant

samples. In addition, the large variation in the Cd isotope ratios of these

reference materials implies that the Cd isotope ratio is promising for

identifying pollution sources and the biogeochemical cycle of Cd.

INTRODUCTION

Cadmium (Cd) is a heavy metal physiologically toxic to plants,

animals, and humans. Cd in the environment is derived from both

natural and anthropogenic sources.1 The major natural sources of

Cd include volcanic eruptions and rock weathering.2 With

industrialization, large amounts of Cd have been released into the

environment via anthropogenic activities, such as the production

of Ni-Cd batteries, waste incineration, coal combustion, and

fertilizer application.3 Soil suffers from severe Cd pollution

because it is the main contaminant sink.4 When entering the soil,

Cd can be adsorbed or immobilized by different soil components,

internalized by plant roots, and eventually translocated to different

plant tissues.5, 6 The complexities of the pollution sources and

biogeochemical cycles of Cd in soil significantly restrict the

solution to environmental Cd pollution.


Cd has eight stable isotopes with mass numbers in the range of

106–116. Cd isotope fractionation has been shown to occur during

the evaporation/condensation and biological cycling of Cd.7, 8

Recent studies have reported a significantly large degree of Cd

isotope fractionation among the products and waste produced

during coal combustion, metal smelting/refining, and metal

coating,9-12 which implies that isotope fractionation can be used to

identify Cd pollution sources.13 Researchers have identified the

main Cd sources and their contribution to Cd pollution by

determining the Cd isotope ratios of various sources and

contaminated receptors (e.g., soil and sediments).14-17 Cd isotope

fractionation also occurs during its adsorption-desorption between

the solid and liquid phases18-20 and translocation to different plant

tissues (e.g., roots, shoots, and grains).21, 22 These observations

have demonstrated that Cd isotope analysis is a promising tool for

tracing the sources of Cd in contaminated sites and can also be

used as an indicator for probing the behavior of Cd in the

environment.

In early research, Cd isotope analysis was typically accomplished

using thermal ionization mass spectroscopy.23 With improvements

in mass spectroscopy, the use of multi-collector inductively

coupled plasma mass spectrometry (MC-ICP-MS) for metal

isotope analysis became practical.24 Since MC-ICP-MS can easily

overcome the first ionization potential of Cd via plasma ionization,

it is currently the dominant technique for Cd isotope analysis.23 To

accurately determine Cd isotope ratios, possible interfering

substances must be removed from samples, which requires the

samples to be completely digested, purified, and recovered. For

environmental sample digestion, the most widely used protocol is

to sequentially add sample and mineral acids (e.g., HNO3 and HF)

to high-pressure bombs or perfluoroalkoxy (PFA) vials. Digestion

is then performed using an oven or a hot plate for tens of hours.25-

28 Although these methods can completely digest samples, they are

time-consuming and exhibit poor organic matter removal

performance due to the incomplete oxidation of carbon.

Unfortunately, most environmental samples, such as agricultural

soils and plants, contain large amounts of organic matter that can

significantly affect the analytical accuracy of Cd isotope ratios.11

Due to the increasing use of Cd isotope fractionation in

environmental research, establishing an effective and efficient

digestion method for environmental samples has become

necessary. In addition, most soil samples contain interfering

elements (e.g., Zn, Zr, Mo, In, and Sn) in amounts that are 1–2

orders of magnitude higher than that of Cd. Thus, it also becomes

necessary to evaluate whether current purification processes can

effectively remove the matrix and interfering elements in a single

operation, and if not, whether the content of interfering elements

in the Cd sample solution can meet the requirements for Cd

isotope analysis. Furthermore, if the Cd sample solution does not

meet the test requirements, the performance of the secondary

purification procedure needs to be evaluated.

Therefore, eight environmental reference materials, including

soil, sediments, and plants, were tested to evaluate the use of

microwave digestion and anion-exchange-based chemical

purification procedures using double-spike MC-ICP-MS for Cd

isotope analysis. In addition, the effects of isobaric (Pd, In, and Sn)

and polyatomic (Zn, Zr, and Mo) interferences on Cd isotope

analysis were evaluated and discussed.

EXPERIMENTAL

Reagents, standard solutions, and samples. TraceMetalTM grade

HNO3 and hydrochloric acid (HCl) were procured from Thermo

Fisher Scientific (Waltham, MA). Analytical-grade HF and

hydrogen peroxide (H2O2) were procured from Beijing Chemicals

(Beijing, China). HNO3 and HCl were distilled twice using a

Savillex™ DST-1000 sub-boiling acid system. The 111Cd-113Cd

double-spike solution used in this study was prepared using the

enriched 111Cd (purity of 96.00%) and 113Cd isotopes (purity of

94.90%) from Isotopes for Science, Medicine, and Industry (San

Francisco, CA). Briefly, the enriched 111Cd and 113Cd isotopes

were individually dissolved in 2 mol L−1 HNO3, and suitable

volumes of the individual isotope solutions were weighed and

mixed to obtain a double-spike solution with a 111Cd-to-113Cd ratio

≈ 1, which could then provide high precision for the double-spike

method.25, 29 Finally, after the double-spike solution was stabilized

for 12 h, it was diluted to 10 µg g−1. An AG-MP-1M ion-exchange

resin (100–200 dry mesh size, Bio-Rad Laboratories AB, Solna,

Sweden) was used for sample purification. Milli-Q water (18.2

MΩ cm−1) was used in all experimental procedures.

Four mono-elemental Cd solutions were used to validate the

method: (1) an NIST SRM3108 Cd solution, a common reference

material for Cd isotope studies (Lot 130116);30 (2) AAS-Cd, a

commercial ICP standard solution (Lot 1797541);25 (3) Thermo-

Cd, a commercial ICP standard solution (Lot 9158147) (Alfa

Aesar, Thermo Fisher Scientific, Leicestershire, United Kingdom);

and (4) GSB-Cd, a mono-elemental Cd standard solution (GSB

04-1721-2004) (Iron and Steel Research Institute, Beijing, China).

Other mono-elemental (Mg, Ca, Mn, Cu, Zn, Zr, Mo, Pd, In, Sn,

and Pb) standard solutions used in this study were also procured

from the Iron and Steel Research Institute (Beijing, China).

Eight environmental reference materials with certified Cd

concentrations and isotopic compositions were used to evaluate

treatment processes, including four soil reference materials (NIST

2711a, GSS-1, GSS-4, and GSS-5), three stream sediment

reference materials (GSD-11, GSD-12, and GSD-30), and one

plant reference material (BCR-679).

Sample digestion. Samples were digested using microwaves. For


Table 1 Microwave digestion procedure

Temperature

(°C)

Ramping time

(min)

Holding time

(min)

Power

(W)

120 10 10 1800

150 15 20 1800

190 15 30 1800

Table 2 Chemical purification procedure for Cd separation

Reagent Volume (mL) Aim

2 mol L−1 HCl 10 Balance resin

Sample solution 2 Load sample

2 mol L−1 HCl 10 Elute matrix element

0.3 mol L−1 HCl 30 Elute Zr, Pb, In, Mo

0.06 mol L−1 HCl 20 Elute Zn and Sn

0.012 mol L−1 HCl 6 Elute Sn

0.0012 mol L−1 HCl 20 Collect Cd

Table 3 Instrumental parameters for Cd isotope measurement

MC-ICP-MS conditions Settings

RF power, W 1200–1300

Cooling Ar, L min−1 16

Auxiliary Ar, L min−1 ~0.90 (optimized daily)

Nebulizer Ar, L min−1 ~0.95 (optimized daily)

Sample introduction Aridus II

Cone Ni Jet cone, H-skimmer cone

Spray chamber temperature, °C 110

Membrane temperature, °C 160

Number of cycles

Integration time, s

one block with 30 cycles

4.194

Typical 114Cd sensitivity, V per

mg kg−1

130

soil or sediment samples, 100 ± 0.1 mg of the sample was placed

in a 60 mL Teflon tube, and HCl-HNO3-HF mixtures (10 mL, 6:

2: 2, v/v) were added into the tube. For plant samples, 250 ± 0.1 mg

of the plant sample was placed in a 60 mL Teflon tube, and HNO3-

H2O2-HF mixtures (8.5 mL, 6: 2: 0.5, v/v) were added into the tube.

Following the microwave digestion procedure (Table 1), the

Teflon tubes were put on a hot plate at 140 °C to reduce the acid

concentration until the solution volume reached ~0.5 mL, which

was done to prevent the possible loss of Cd. The residue was

transferred into a pre-cleaned centrifuge tube, and 2% (w/w)

HNO3 was added until the volume reached 15 mL. Thereafter,

0.5 mL of the digested solution was diluted, and the Cd

concentration was measured using inductively coupled plasma

mass spectrometry (ICP-MS). The rest of the digested solution

was transferred into a pre-cleaned PFA screw-cap beaker (15 mL)

and a double-spike (111Cd/113Cd ≈ 0.98, 110Cd/114Cd ≈ 0.097, 111Cd/114Cd ≈ 17.66, and 113Cd/114Cd ≈ 18.08) solution was added

to achieve a double-spike-to-sample ratio of approximately 0.5

(the ratio was determined using the double-spike software toolbox

developed by Rudge et al.).29 The PFA screw-cap beaker was then

placed on a hot plate at 145 °C until the solution was evaporated

to near dryness, and the residue was then dissolved in 2 mL of 2 M

HCl for chemical purification.

Chemical purification. The one-step chemical purification

procedure using the AG-MP-1M anionic exchange resin is widely

used to remove matrix elements and purify Cd.10, 31-33 The

chemical purification procedure used herein was modified from

the procedures reported by Wen et al.32 and Wei et al.34 The steps

in the purification procedure are listed in Table 2. Briefly, a micro-

column was filled with 3 mL pre-cleaned resin (10% (w/w) HNO3

and Milli-Q water), and the resin column was then sequentially

cleaned with Milli-Q water (10 mL) and 2% (w/w) HNO3 (10 mL).

The sample in 2 M HCl (2 mL) was loaded onto a column

equilibrated with 2 M HCl (10 mL). Thereafter, matrix elements

were eluted by 2 M HCl (10 mL), 0.3 M HCl (30 mL), 0.06 M

HCl (20 mL), and 0.012 M HCl (6 mL). The Cd fraction was then

collected with 0.0012 M HCl (20 mL) in a 25 mL PFA beaker,

after which it was evaporated to dryness on a hot plate at 145 °C.

During heating, 0.2 mL of concentrated HNO3 was added into the

beaker to remove possible organic matter from the resin.34, 35 The

residue remaining in the beaker after evaporation was then

dissolved in 2 mL of 2% HNO3 (w/w). To evaluate the extent of

purification, 0.5 mL of this solution was then taken and the

concentration of Cd and interfering elements measured. The entire

procedural blank of Cd was below 0.1 ng, which was negligible.

To assess the purification procedure, an artificial mixed solution

(Cd: Mg: Ca: Mn: Cu: Zn: Zr: Mo: Pd: In: Sn: Pb = 1: 200: 300:

100: 100: 10: 10: 2.5: 1: 2: 2.5: 5) was purified using the same

procedure. The composition and proportion of the artificial mixed

solution were designed to simulate the soil composition.25 The

concentrations of Mg, Ca, Cu, Mn, and Pb in the eluent were

measured using inductively coupled plasma-optical emission

spectrometry (ICP-OES), and the concentrations of Zn, Sn, In, Pd,

Zr, Mo, and Cd were measured using ICP-MS.

Measurement using MC-ICP-MS. Cd isotope ratios were

measured using MC-ICP-MS (Neptune Plus, Thermo Fisher

Scientific, Bremen, Germany) at Jianghan University (Wuhan,

China). Instrumental parameters are presented in Table 3. The ion

currents of 110Cd, 111Cd, 113Cd, and 114Cd were collected using L2,

L1, C, and H1 Faraday cups, respectively. To investigate and

correct the isobaric interference of 110Pd on 110Cd, 113In on 113Cd,

and 114Sn on 114Cd, the ion currents of 105Pd, 115In, and 118Sn were

simultaneously collected using L4, H2, and H4 Faraday cups,

respectively. For MC-ICP-MS measurements, an Aridus II

membrane desolvation system was used for sample introduction.

Parameters (gas flow, torch position, lens settings, and zoom optics)

were optimized to obtain a 114Cd (100 μg kg−1) signal over 13 V.

Before each measurement, the sample introduction system was

rinsed with 5% HNO3 (w/w) for 1 min and 2% HNO3 (w/w) for 2

min.

Data correction. MC-ICP-MS data were corrected in three steps.

First, the instrumental mass bias was corrected using the

generalized power law36, 37:

ƒ = In(R / r) / In(m2 / m1) (1)


Table 4 Cd concentration and isotopic compositions of the environmental reference materials used in this study and references

where ƒ is the mass fractionation factor of the instrument; R and r

correspond to the measured and true Cd isotope ratios,

respectively; and m1 and m2 are the masses of the two Cd isotopes.

Before each sample measurement sequence, a pure NIST 3108

solution was tested, and the isotope ratio of 114Cd to 110Cd was used

to calculate the mass fractionation factor ƒ. The ƒ cannot be

ignored, otherwise, it will produce percent-level bias in the

measured isotopic composition from the true isotopic composition

of sample.38

Second, the isobaric interferences of 110Pd on 110Cd, 113In on 113Cd, and 114Sn on 114Cd were corrected as follows: (1) using the

ion current of 105Pd to calculate the ion current of 110Pd; (2) using

the ion current of 118Sn to calculate the ion current of 115Sn, and

then using the ion current of 115In (corrected ion current of 115Sn)

to calculate the ion current of 113In; and (3) using the ion current of

118Sn to calculate the ion current of 114Sn. Thereafter, the measured

ion currents of 110Cd, 113Cd, and 114Cd were corrected using the

calculated ion currents of 110Pd, 113In, and 114Sn, respectively, to

obtain the true ion currents of 110Cd, 113Cd, and 114Cd. The

interferences of 110Pd, 113In, and 114Sn were calculated as follows25:

110Pdm = 105Pdm × (110Pd / 105Pd)n × (110 / 105) f (2)

113Inm = (115Inm – 115Snm) × (113In / 115In)n × (113 / 115) f (3)

115Snm = 118Snm × (115Sn / 118Sn)n × (115 / 118) f (4)

114Snm = 118Snm × (114Sn / 118Sn)n × (114 / 118) f (5)

where m and n correspond to the measured signal and the

corresponding isotope natural abundance ratio, respectively; and ƒ

is the mass fractionation factor of the instrument. Considering

similar mass number of Pd, In, and Sn to that of Cd, the same ƒ

was used here.25

Third, the three measured isotope ratios, 111Cd-to-110Cd, 113Cd-

to-110Cd, and 114Cd-to-110Cd, were inserted into three simultaneous

nonlinear equations that can be iteratively solved to obtain the true

isotopic composition of the sample after infinitely excluding the

double-spike composition based on a mathematical algorithm.29, 39

Cd isotopic compositions were reported in δ-notation relative to

the NIST SRM3108 Cd solution, which is defined as follows:

δ = ((114Cd / 110Cd)sample

(114Cd / 110Cd)SRM 3108 − 1) × 1000 (6)


Efficiency of the microwave digestion procedure. The reference

and measured Cd concentrations of the eight reference materials

are listed in Table 4. According to these results, microwave

digestion completely digested the samples and did not cause a

significant loss of Cd, which is indicated by the Cd recoveries of

99.5%, 98.7%, 98.0%, 99.7%, 99.1%, 96.4%, 98.9%, and 101%

for NIST 2711a, GSS-1, GSS-4, GSS-5, GSD-11, GSD-12, GSD-

30, and BCR-679, respectively. Compared to the time required for

hot plate digestion or high-pressure bombs, significantly lesser

Reference

materials

Zn or Sn/Cd

(Before purification)

Zn or Sn/Cd

(After purification)

Reference value

(mg kg−1)

Measured value

(mg kg−1)

δ114/110Cd (‰)

in this study

δ114/110Cd (‰)

in references

Soil

NIST

2711a

Zn/Cd > 7

Sn/Cd > 3

Zn/Cd < 0.01

Sn/Cd < 0.005 54.1 ± 0.5 53.8 ± 0.6 0.550 ± 0.046

0.532 ± 0.03826

0.57 ± 0.0727

0.58 ± 0.0827

0.551± 0.05125

0.57 ± 0.0443

0.62 ± 0.0328

Soil

GSS-1

Zn/Cd > 158

Sn/Cd > 1

Zn/Cd < 0.01

Sn/Cd < 0.1 4.3 ± 0.4 4.23 ± 0.08 0.099 ± 0.054

0.10 ± 0.2310

0.11 ± 0.06 27

0.098 ± 0.02726

Soil

GSS-4

Zn/Cd > 600

Sn/Cd > 16

Zn/Cd ≈ 0.01

Sn/Cd ≈ 1 0.35 ± 0.06 0.34 ± 0.01 −0.303 ± 0.044

−0.308 ± 0.01626

−0.23 ± 0.0928

Soil

GSS-5

Zn/Cd >1000

Sn/Cd > 40

Zn/Cd < 0.2

Sn/Cd < 10 0.45 ± 0.06 0.45 ± 0.01 −0.558 ± 0.046

−0.694 ± 0.01026

−0.54 ± 0.0628

Sediment

GSD-11

Zn/Cd > 160

Sn/Cd > 160

Zn/Cd < 0.05

Sn/Cd < 0.05 2.3 ± 0.2 2.28 ± 0.05 −0.320 ± 0.067

−0.274 ± 0.03726

−0.305 ± 0.05443

−0.34 ± 0.0628

Sediment

GSD-12

Zn/Cd > 120

Sn/Cd > 13

Zn/Cd < 0.04

Sn/Cd < 0.001 4.0 ± 0.4 3.86 ± 0.01 −0.041 ± 0.030

−0.071 ± 0.06026

−0.020 ± 0.09527

0.290 ± 0.03014

0.00 ± 0.1327

−0.10 ± 0.0628

Sediment

GSD-30

Zn/Cd > 21

Sn/Cd > 0.7

Zn/Cd ≈ 0

Sn/Cd ≈ 0 4.3 ± 0.3 4.25 ± 0.04 0.289 ± 0.042 0.29 ± 0.0443

Plant

BCR-679 Zn/Cd > 48 Zn/Cd ≈ 0 1.66 ± 0.07 1.68 ± 0.06 0.203 ± 0.012 0.22 ± 0.0244


Fig. 1

Elution curves of the artificial mixed standard solution (Cd: Mg: Ca:

Mn:

Cu:

Zn:

Zr:

Mo:

Pd:

In:

Sn:

Pb = 1:

200:

300:

100:

100:

10:

10:

2.5:

1:

2:

2.5:

5).

time (~3 h) was required for microwave digestion, including

sample weighing, reagent addition, digestion, and cooling.

Although microwave digestion consumed a large volume of acid

mixtures (10 mL), and the acid mixtures must be evaporated after

digestion (approximately 2 h), the overall time requirement was

still significantly lower than that of other digestion methods. In

addition, microwave digestion effectively destroyed the organic

matrix in the samples,40,41 as supported by the changes in the color

of the digestion solution (transparent or pale yellow) and the

subsequent chemical purification performance. Microwave

digestion simultaneously digested 40 samples (including blanks),

and the digestion performance was highly parallel. Therefore,

microwave irradiation can be efficiently used to digest

environmental samples for the subsequent Cd isotope analysis.

Performance of the chemical purification procedure. Cd

isotope analysis is marred by a variety of interferences that must

be removed, including the matrix effect, isobaric interference, and

polyatomic interference. First, interference removal was attempted

using an anion-exchange-based resin purification procedure with

an artificial mixed standard solution. As shown in Fig. 1, the

matrix elements (Mg, Ca, Mn, and Cu) were almost completely

leached from the column in the first stage (2 M HCl), and the

interfering elements (Zn, Zr, and Mo), which can form polyatomic

molecules, were completely leached out of the column before the

fourth stage (0.012 M HCl). Pd was immobilized on the column

and was not detected in any eluents, while In was eluted out of the

column in the second stage (0.3 M HCl). Most Sn was eluted out

of the column in the third (0.06 M HCl) and fourth stages

(0.012 M HCl), and a small fraction of Sn was observed together

with Cd in the last stage (0.0012 M HCl). Elution curves were in

adequate agreement with those of other studies.10, 33, 42

In addition, the incomplete recovery of Cd during chemical

purification may produce significant isotope fractionation.10, 24

However, previous studies have indicated that Cd isotopes are not

significantly fractionated when the recovery after chemical

purification is higher than 95%.10, 31 The recovery of Cd in the

procedure used in this study, monitored using a mixed standard

solution, was >98%, which guarantees data quality. The above

results suggest that this chemical purification procedure can

efficiently separate the matrix and interference elements from Cd

without causing any loss of Cd.

To further verify the performance of the purification procedure,

the digestion solutions of the eight environmental reference

materials were purified after evaporation and re-dissolution. The

Cd recoveries for all eight samples after purification exceeded

98%, as determined using ICP-MS analysis. In addition,

interference elements, including Zr, Mo, Pd, and In, were not

detected after purification, but small amounts of Zn and Sn

remained in some analyte solutions (Table 4). This may be related

to the matrix composition of the sample and the relative content of

the interference elements in the sample. For example, the matrices

of NIST 2711a, GSD-12, GSD-30, and BCR-679 are relatively

simple (Table 4), and the concentration ratios of Zn to Cd and Sn

to Cd in the analyte solution after purification were extremely low.

However, for GSS-1, GSS-4, GSS-5, and GSD-11, the

concentration ratios of Zn to Cd and Sn to Cd after purification

were higher.

These results demonstrate that the above procedure can

effectively purify the samples. However, in some samples with

high concentrations of interference elements, the interference

elements were not completely removed. Thus, it is necessary to

evaluate whether the concentration of the interference elements in

the analyte solution after purification can cause bias during MC-

ICP-MS measurements, and whether secondary chemical

purification is required for subsequent MC-ICP-MS analysis.

Evaluation of polyatomic and isobaric interferences. During

instrumental detection, the non-target elements in analyte

solutions can cause spectral interference during Cd isotope

measurements, including polyatomic and isobaric interferences.

Polyatomic interferences include 94Zr16O+, 94Mo16O+, 70Zn40Ar+

on 110Cd, 95Mo16O+ on 111Cd, 97Mo16O+ on 113Cd, and 98Mo16O+ on 114Cd.36 Isobaric interferences include 110Pd on 110Cd, 113In on 113Cd, and 114Sn on 114Cd.36 Therefore, the thresholds for these

interferences must be evaluated. Cd isotopes were measured in a

series of NIST SRM3108 solutions (100 μg L−1) doped with

different concentrations of Zn, Zr, Mo, Pd, In, and Sn. As shown

in Fig. 2, Zn/Cd > 0.04, Zr/Cd > 0.01, and Mo/Cd > 0.2 in the

analyte solution result in significant Cd isotopic deviation. Isobaric

interferences can theoretically be corrected using mathematical

calculations, but their correction is difficult when the concentration

ratios of these elements to Cd exceed a certain range.25 In this study,

isotopic deviations could not be corrected when the ratios of Pd to

Cd > 4 × 10-5, In to Cd > 0.02, and Sn to Cd > 0.1 (Fig. 3). These


Fig.2

Doping experiments for evaluating the effects of polyatomic

interferences on Cd isotope measurements.

Fig. 3 Doping experiments for evaluating the effects of isobaric

interferences on Cd isotope measurements.

observations differ from those of some previous studies.10, 25, 26 The

threshold ratios of polyatomic interference elements as reported

elsewhere ranged from 0.01 to 0.1 for Zn, 0.001 to 0.005 for Zr,

and 0.1 to 1 for Mo.10, 25, 26 In this study, the results for Zn, Zr, and

Mo adhered to those ranges. In addition, the threshold ratios of

isobaric interference elements varied among the studies.

Insufficient information is available about the threshold ratio of Pd

because of the low levels in environmental and geological samples.

However, our results for Pd were similar to those reported by Liu

et al.25 For In, previously reported threshold ratios ranged from

10−3 to 0.02,25, 28 and our result for In was similar to that reported

by Peng et al.28 For Sn, different levels (0.02 to 0.5) of Sn-to-Cd

ratios were reported by different laboratories, 26-28 and our result

for Sn fell within that range. However, the interference trends of

In and Sn in this study were different from those reported

previously.25-27 For In, the test range far exceeded that reported by

Liu et al.,25 and δ114/110Cd values decreased when the ratio of In to

Cd increased from 0.02 to 0.05 and increased when the ratio of In

to Cd exceeded 0.05. This may be due to the fact that a high 113In

signal, produced from high In content, can disrupt the data

correction of Cd.28 For Sn, δ114/110Cd values decreased when the

ratio of Sn to Cd increased, but the decrease was small. As

mentioned earlier, the threshold ratios of Sn were different among

different studies.26-28 In addition, Liu et al.25 reported that the

interference of Sn can be corrected. Therefore, the threshold ratios

of interference may be related to different instruments and

experimental conditions that may produce different mass

discrimination,26, 28 as well as the difference in the trend of

interference.

As mentioned above, when the concentration ratio of any

interfering element to Cd after the first purification exceeds its

corresponding threshold, a second chemical purification

procedure is required. According to the interfering elements in the

solution after the first purification, the reference materials GSS-4,

GSS-5, GSD-11, and GSD-12 needed to be purified for a second

time. After the secondary chemical purification procedure, the

concentrations of the interfering elements in the purified solution

decreased to their corresponding threshold without a loss of Cd,

and the Cd isotopic ratios were consistent with those reported in

previous studies (Table 4).

Another point that should be emphasized is the selection of

laboratory equipment. A heating plate with an anti-rust coating

was initially used in this laboratory, and no abnormalities were

observed during the initial period of use. However, over time,

rusting occurred around the plate, leading to a significant increase

in the Zn concentration in the analyte solution. Therefore, a

graphite-coated heating plate was used to avoid Zn contamination.

Precision, accuracy, and Cd isotope ratios of environmental

reference materials. To evaluate the precision and accuracy of

this method, three types of samples were measured: (1) four pure

Cd solutions; (2) a synthetic solution containing matrix elements

in three pure Cd solutions (NIST SRM3108, Thermo-Cd, and

GSB-Cd); and (3) eight environmental reference materials.

The long-term precision of NIST 3108 was ±0.05‰ (2SD, n =

28). The δ114/110Cd value of AAS-Cd in this study (−0.699 ±

0.048‰, 2SD, n = 21) was in adequate agreement with that

reported by Liu et al.25 The average δ114/110Cd values for Thermo-


Fig. 4 Long-term measurement results of standard solutions relative to NIST SRM 3108.

Cd and GSB-Cd (two commercial mono-elemental standard

solutions) were −1.208 ± 0.049 (2SD, n = 20) and 4.441 ± 0.053‰

(2SD, n = 22), respectively (Fig. 4). To verify that the purification

process did not change the true Cd isotope ratios of the samples,

synthetic solutions (with double-spike) were purified and analyzed.

Their δ114/110Cd values were −0.028 ± 0.056 (NIST SRM3108,

2SD, n = 3), −1.218 ± 0.043 (Thermo-Cd, 2SD, n = 3), and 4.437

± 0.063‰ (GSB-Cd, 2SD, n = 3), which is in adequate agreement

with those of pure solutions. These results demonstrate that the

chemical purification procedure did not change the true Cd isotope

ratios of the samples. In addition, the Cd isotope ratios of the eight

environmental reference materials were measured. Samples were

digested under microwave irradiation, a double-spike solution was

added before purification, and the ratios were then measured using

MC-ICP-MS. δ114/110Cd values were 0.550 ± 0.046 ‰ (2SD, n =

7) for NIST 2711a, 0.099 ± 0.054‰ (2SD, n = 3) for GSS-1,

−0.303 ± 0.044‰ (2SD, n = 3) for GSS-4, −0.558 ± 0.046‰ (2SD,

n = 7) for GSS-5, −0.320 ± 0.067‰ (2SD, n = 5) for GSD-11,

−0.041 ± 0.030‰ for GSD-12 (2SD, n = 5), 0.289 ± 0.042‰ (2SD,

n = 5) for GSD-30, and 0.203 ± 0.012‰ (2SD, n = 3) for BCR-

679, in adequate agreement with those reported in previous studies

(Table 4).

CONCLUSIONS

In this study, microwave digestion was conducted to prepare

environmental samples for high-precision Cd isotope analysis.

Compared with the digestion efficiency of traditional digestion

methods (hot plate or high-pressure bombs), that achieved herein

was significantly higher. Typically, the chemical purification

procedure using an anion exchange resin effectively removed

matrix elements without the loss of Cd. However, a secondary

purification step was required for the analyte solutions with high

concentrations of interference elements (Zn/Cd > 0.04, Zr/Cd >

0.01, Mo/Cd > 0.2, Pd/Cd > 4 × 10−5, In/Cd > 0.02, or Sn/Cd >

0.1). The δ114/110Cd values of the eight environmental reference

materials with different matrices varied from −0.558 to 0.550‰,

with Cd concentrations from 0.45 to 54.1 mg kg−1, which was in

adequate agreement with that reported in previous studies. The

developed microwave digestion–resin purification–double-spike

MC-ICP-MS method is useful for the Cd isotope analysis of

environmental matrices and can improve the understanding of the

source, transportation, and transformation of Cd in the

environment.


AUTHOR INFORMATION

Yongguang Yin is a professor of

environmental sciences at Research Center

for Eco-Environmental Sciences (RCEES),

Chinese Academy of Sciences (CAS). He

received his B.S. and M.S. degree in Applied

Chemistry and Analytical Chemistry from

Chongqing University in 2002 and 2005, and

completed his Ph.D. in Environmental

Sciences from RCEES in 2008. He has been

working as member of editorial board for

Atomic Spectroscopy. His research interests focus on speciation,

environmental transformation, and regional/global cycle of toxic metals.

He has published over 140 scientific papers in peer-reviewed journals.

Yong Liang is a professor in School of

Environment and Health, Jianghan

University. He received his Ph.D. degree in

Environmental Science from Institute of

Hydrobiology, Chinese Academy of

Sciences. By using analytic chemistry,

toxicology and computational methods, he

has conducted research on environmental

chemistry and ecotoxicology. His recent

research mainly focuses on bioaccumulation,

transport, transformation, and toxicity of

typical persistent toxic substances, including heavy metals, perfluoroalkyl

substances, and flame retardant. He has published more than 120 scientific

papers in peer-reviewed journals.


*Y. G. Yin


* Y. Liang


Notes


ACKNOWLEDGMENTS

This work was supported by the National Natural Science

Foundation of Shandong (ZR2020ZD20), National Natural

Science Foundation of China (21976193, and 21777178), and

National Key R&D Program of China (2018YFC1800400). Y. Yin

acknowledges the supports from National Young Top-Notch

Talents (W03070030) and Youth Innovation Promotion

Association of the Chinese Academy of Sciences (Y202011).

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Coal Proximate Analysis Based on Synergistic Use of LIBS and

NIRS

Shunchun Yao,a,b,* Huaiqing Qin,a,b Shuixiu Xu,a,b Ziyu Yu,a,b Zhimin Lu,a,b and Zhe Wangc,* a School of Electric Power, South China University of Technology, Guangzhou 510640, P. R. China

b Guangdong Province Engineering Research Center of High Efficient and Low Pollution Energy Conversion, Guangzhou 510640, P. R. China

c State Key Lab of Power Systems, International Joint Laboratory on Low Carbon Clean Energy Innovation, Department of Energy and Power Engineering,

Tsinghua University, Beijing 100084, P. R. China

Received: April 13, 2022; Revised: April 28, 2022; Accepted: April 28, 2022; Available online: April 28, 2022.

DOI: 10.46770/AS.2022.099

ABSTRACT: Rapid and accurate analysis of the coal properties is essential for its clean and efficient utilization. However,

the complexity of the physical-chemical characteristics of coal complicates accurate quantification of its properties using rapid

spectroscopic technologies. Herein, a synergistic method is proposed to achieve optimum

results for coal proximate analysis using spectra obtained via laser-induced breakdown

spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS). Firstly, the ash

content was analyzed by LIBS, and the moisture content was analyzed by NIRS. The

calorific value and volatile content were then analyzed using the fusion data obtained from

LIBS and NIRS, combined with the ash and moisture results. Finally, the fixed carbon

content was calculated by subtracting the mass percentage fractions of ash, moisture, and

volatile matter from 100%. The results indicated that the proposed method achieved good

quantitative performance for coal proximate analysis. The root mean square errors of

prediction for ash, moisture, calorific value, volatile matter, and fixed carbon were 0.743%,

0.304%, 0.187 MJ/kg, 0.662% and 0.972%, respectively. Thus, we believe that the

proposed method based on the synergy of LIBS and NIRS opens avenues for efficient

prospective analysis of coal properties.

INTRODUCTION

Coal is the primary source of energy worldwide. According to the

BP Statistical review of World Energy 2021, the global coal

consumption in 2020 is 151.42 EJ.1 Such a large volume of coal

consumption contributes to high carbon emissions. However, to

achieve the goals stated in the Paris Agreement, carbon emissions

must be reduced.2 The clean and efficient utilization of coal,

assisted by intelligent burner operation, is vital for reducing carbon

emissions, and therefore, online measurements of multiple coal

properties are essential. Currently, some rapid analysis techniques

for coal properties, such as prompt gamma neutron activation

analysis (PGNAA)3 and X-ray fluorescence (XRF)4 analysis, have

been put into practice. However, PGNAA machine is bulky and

needs to be strictly regulated because its neutron source has

potential health and environmental hazards.5 XRF is not capable

of detecting light elements, such as C and H.6 These disadvantages

limit the wider application of PGNAA and XRF in coal property

analysis.

Laser-induced breakdown spectroscopy (LIBS) is a promising

technique for coal analysis7–9 and demonstrates advantages such

as effortless sample preparation, real-time detection, and

capability to detect all elements. Gaft et al.10 first applied LIBS to

online analysis of ash content and the results were in good

agreement with those from an existing online PGNAA machine.

Ctvrtnickova et al.11,12 used LIBS to measure ash in coal, and they

established calibration curves for the elements contained in ash,

demonstrating excellent correlation between element


concentration and spectral intensity. Zhang et al.13 used principal

component analysis (PCA) to extract the principal components

from LIBS data as the inputs for a support vector regression (SVR)

model to analyze coal properties. Yuan et al.14 proposed a non-

linearized multivariate dominant factor based partial square least

(PLS) model to correlate LIBS data with coal properties. Hou et

al.15 proposed a hybrid quantification model to analyze coal

properties, which integrated spectrum standardization, the

dominant factor PLS, and a database system to improve sample-

to-sample reproducibility. Song et al.16 used an ensemble variable

selection method to filter LIBS data for a PLS model and proposed

a synergistic regression to describe the linear and nonlinear

relationship between LIBS data and coal properties.17 Yan et al.18–

20 employed a kernel extreme learning machine combined with

different variable selection methods to filter LIBS data to optimize

the measurement of coal properties. Yao et al.21 used characteristic

lines in LIBS to analyze coal properties based on an artificial

neural network (ANN) optimized by a genetic algorithm, and

reported that the analysis result is better than that obtained from a

neutron activation coal online analyzer. Zhang et al.22 used the

wavelet threshold de-noising method to identify available

variables in LIBS to optimize the analysis of coal properties based

on SVR. Lu et al.23 proposed the PLS method combined with ash

classification to determine coal properties and discovered that the

method improved the accuracy of analysis. Zhang et al.24

compared the performance of the PLS method, SVR and ANN in

LIBS analysis of coal properties and discovered that the ANN

demonstrates the best prediction performance. Although several

efforts have been made to improve coal analysis using LIBS,

matrix effects and the relatively high measurement uncertainty still

limit its wide application.25,26

Near-infrared reflectance spectroscopy (NIRS) is another

available technique for coal analysis; it detects organic functional

groups in substances according to molecular vibrations.27 NIRS

has previously been used to analyze the coal properties and

demonstrates several advantages, such as no sample preparation

requirements, fast response, and simple operation. Bona et al.28–30

used near-infrared spectroscopy to analyze coal properties based

on PLS method coupled with cluster analysis. The results

indicated that the parameters related to organic matter exhibited a

low prediction error. Kim et al.31 selected several near-infrared

spectral bands to predict the properties of coal on a conveyor belt,

and the prediction error met the requirement of ASTM/ISO

standards. Begum et al.32,33 chose five maximum absorption bands

distributed in the visible-near-infrared region to analyze coal

properties and compared the performance of PLS, random forest,

and extreme gradient boosting regression methods. Wang et al.34

proposed a deep synergy adaptive-moving window partial least

square-genetic algorithm to correlate NIRS data with coal

properties. Dong et al.35 proposed a deep belief network combined

with a derivative function and regularization two-layer extreme

learning machine (DF-RTELM) model to determine coal

Table 1 Summary of coal property analysis by LIBS and NIRS

Calibration

method Results

LIBS

PCA-SVR13

Ash content: R2 = 0.96, RMSEP = 1.82%, ARE = 5.48%; Volatile

matter: R2 = 0.95, RMSEP = 1.22% ARE = 4.42%; Calorific value:

R2 = 0.91, RMSEP = 0.85 MJ/kg, ARE = 3.68%

Non-linearized

multivariate

dominant factor

PLS14

Moisture content: R2 = 0.97, RMSEP = 0.87%, ARE = 26.2%; Ash

content: R2 = 0.93, RMSEP = 3.49%, ARE = 12%; Volatile matter: R

2

= 0.97, RMSEP = 1.41%, ARE = 5.47%; Calorific value: R2 = 0.97,

RMSEP = 1.33 MJ/kg, ARE = 2.71%

Standardized

spectra combined

with Dominant

factor based PLS36

Ash content: AAE = 1.661%, mRSD = 1.649%; Volatile matter: AAE

= 0.664%, mRSD = 1.274%; Heat value: AAE = 0.856 MJ/kg, mRSD

= 0.317%

PLS16

Volatile matter: Rp2 = 0.9303, RMSEP = 0.8534%; Ash content: Rp

2 =

0.96, RMSEP = 1.7618%; Calorific value: Rp2 = 0.9034, RMSEP =

0.8436 MJ/kg

Synergic

regression17

Heat value: Rp2 = 0.959 RMSEP = 0.373 MJ/kg, MAE = 0.299 MJ/kg;

Volatile content: Rp2 = 0.938 RMSEP = 0.709%, MAE = 0.590%; As

content: Rp2 = 0.918 RMSEP = 1.112%, MAE = 0.855%

PSO-KELM18

Ash content: Rp = 0.9926, RMSEP = 2.0480%; Volatile matter: Rp =

0.9945, RMSEP = 1.0874%; Calorific value: Rp = 0.9872, RMSEP =

0.6999 MJ/kg

V-WSP-PSO-

KELM20

Calorific value: Rp

2 = 0.9894, RMSEP = 0.3534 MJ/kg

ANN21

Ash content: R2 = 0.9871, AAE = 0.82%, RMSEP = 1.09%, ASD =

1.64%; Volatile matter: R2 = 0.9755, AAE = 0.85%, RMSEP = 0.99%,

ASD = 0.92%; Fixed carbon: R2 = 0.9890, AAE = 0.96%, RMSEP =

1.32%, ASD = 1.08%; Calorific value: R2 = 0.9870, AAE = 0.48

MJ/kg, RMSEP = 0.64 MJ/kg, ASD = 0.86 MJ/kg

WTD-SVR22

Calorific value: R2 = 0.99, AAE = 0.47 MJ/kg, ARE = 1.82 MJ/kg,

RMSEP = 0.80 MJ/kg; Ash content: R2 = 0.99, AAE = 0.55%, ARE

= 2.99%, RMSEP = 0.60%

SVM-PLS23

Ash content: R2 = 0.9972, RMSEP = 0.9065%; Volatile matter: R

2 =

0.9959, RMSEP = 0.7719%; Calorific value: R2 = 0.9969, RMSEP =

0.4093 MJ/kg

ANN24

Ash content: R2 = 0.9968, AAE = 0.69%, RMSEP = 1.086%, ARSD

= 2.15%; Volatile matter: R2 = 0.9986, AAE = 0.87%, RMSEP =

1.128%, ARSD = 1.39%; Calorific value: R2 = 0.9999, AAE = 0.87%,

RMSEP = 1.128%, ARSD = 1.39%

NIRS

PLS29

Moisture content: RMSEP = 3.22%; Ash: RMSEP = 5.23%; Volatile

matter: RMSEP = 3.70%; Fixed carbon: RMSEP = 5.13%; Heating

value: RMSEP = 557.04 kcal/kg

MLR31

Moisture content: RMSEP = 1.02%; Volatile matter: RMSEP =

1.20%; Ash: RMSEP = 1.74%; Fixed carbon: RMSEP = 1.97%

MLR33

Calorific value: R2 = 0.92, RMSEP = 1.64 MJ/kg

DSA-MWPLS-

GA34

Moisture content: RMSEP = 1.172%; Ash: RMSEP = 1.851%;

Volatile matter: RMSEP = 0.50%; Heat value: RMSEP = 0.964 MJ/kg

DBN-DF-

RTELM35

Moisture content: Rp = 0.96, RMSEP = 1.67%; Ash content: Rp = 0.95,

RMSEP = 3.58%; Volatile matter: Rp = 0.97, RMSEP = 3.54%; Fixed

carbon: Rp = 0.99, RMSEP = 3.34%

*R2 denotes determination coefficient, RMSEP denotes Root Mean Square Error of Prediction, ARE

denotes Average Relative Error, MAE and AAE denote Mean/Average Absolute Error, ASD denotes

Average Standard Deviation, mRSD and ARSD denote Mean/Average Relative Standard Deviation.

properties based on visible-infrared spectral data and discovered

that the model outperformed other models such as convolutional

neural network extreme learn machine and PLS-based models.

However, the performance of NIRS is impaired by its lack of the

sensitivity and absence of direct correlation with inorganic

components. Coal property analyses using LIBS and NIR were

partially summarized in Table 1.


Evidently, the combination of LIBS and NIRS should achieve

much better results than either of them individually because LIBS

provides elemental information, while NIRS provides molecular

information, and the properties of coal are related to both its

elemental and molecular composition. Garcia et al.37 used the

integration of LIBS with NIRS to reveal the relationship between

the heavy metals content and mineralogical concentration in

perlite. Sánchez-Esteva et al.38 used a combination of LIBS and

visible near-infrared spectroscopy to improve soil phosphorus

determination based on the PLS method. Oliveira et al.39

discovered that the fusion of LIBS and NIRS data demonstrates

better predictive accuracy than individual LIBS and NIRS data for

determination of micro- and macro elements in vegetable samples.

Bricklemyer et al.40 revealed that the combination of visible NIRS

and LIBS data provides the best prediction of total carbon in soil.

In a previous work,41 we have proven that the combination of

LIBS and NIRS improves the analysis of multiple coal property

indices. The results of that study obtained the RMSEP for calorific

value, volatile matter, ash content, and moisture content as 0.192

MJ/kg, 0.672%, 0.774%, and 0.308%, respectively.

As can be inferred the above literature review, a modelling

method directly correlates spectral data with coal properties;

therefore, it may suffer from low accuracy when determining coal

properties with complex characteristics. In contrast, methods that

are more dependent on the physical correlation between the

spectra and coal properties should yield a more accurate and robust

analytical performance. In the present study, we developed a step-

by-step method to utilize the spectral information synergistically.

METHOD

Coal properties with different characteristics were determined

using various information. Ash is the residue obtained from

burning coal, primarily composed of the oxides of elements such

as Si, Al, Fe, Ca, Mg and K42, which contribute to high spectral

emissions in LIBS. Moisture, in regards to coal proximate analysis,

refers to the free water content in coal,43 and NIRS is sensitive to

moisture.44 The calorific value is the total energy released from the

complete combustion of a unit mass of coal under specified

conditions.45 The evaporation of moisture absorbs some of the heat

released during combustion. The minerals in ash are decomposed

by endothermic reactions, which also absorbs some amount of

heat.46 Volatile matter is a gaseous mixture of thermal

decomposition products formed when coal is heated at high

temperature under air-free conditions.45 Volatile matter

components originate from the decomposition of organic matter as

well as from minerals,47 such as the water of hydration and sulfur

compounds. Moreover, the metal cations in ash reduce the total

volatile yield because they promote re-deposition of the primary

volatile material.48 Thus, calorific value and volatile matter are not

Fig. 1 Step-by-step method for utilization of the spectral information to

improve coal property analysis.

only related to both the elemental and organic molecular

compositions, but are also dependent on ash content and moisture

content to a certain extent. Accordingly, we proposed a step-by-

step method to synergistically utilize the spectral information to

improve the analysis of these properties of coal, as shown in Fig.1.

First, the ash content was analyzed using LIBS and moisture

content was analyzed using NIRS. Then, the ash and moisture

results were combined with the fusion data obtained from LIBS

and NIRS to deduce the calorific value and volatile matter content.

Finally, the fixed carbon content was calculated according to its

formula definition by subtracting the previously determined

percentage mass fractions of ash, moisture, and volatile matter

from 100%. The novelty of this work is to propose a step-by-step

method to synergistically utilize the spectral information to

determine coal properties with different characteristics.

Owing to high dimensionality and collinearity present in LIBS

and NIRS data, quantitative analysis models of ash and moisture

content were established based on PLS49 method. The relationship

between calorific value, volatile matter, moisture content, and ash

content is complex. The contribution of moisture to the calorific

value and volatile content is affected by the form of water and

other factors.50 In reference to the heat of metal oxides formation,

ash contributes positively to the calorific value, while the presence

of oxidized metals results in the absorption of energy.46 Ash is

mainly composed of oxidized metals, so it contributes negatively

to calorific value. For volatile matter, inorganic constituents not

only promote the conversion of heavier hydrocarbon species to

light gases, but also promote the polymerization of tars to form

non-volatile species.51 Meanwhile, LIBS data, NIRS data and the

ash and moisture content all have significant differences in terms

of order of magnitude. Therefore, three approaches were proposed

to establish quantitative analysis models for calorific value and


volatile matter. The three approaches are described as follows:

(1) The generalized spectral variables (GS) approach initially uses

decimal scaling52 to normalize the LIBS and NIRS data and

the ash and moisture results. Then, LIBS and NIRS data and

the ash and moisture results are connected end-to-end to form

GS. Finally, the GS are used to determine the calorific value

and volatile matter based on the PLS method.

(2) The multiple linear regression coupled with PLS (MLR_PLS)

technique directly uses the ash and moisture results to

preliminarily deduce the calorific value and volatile matter

based on a multiple linear regression (MLR)49 method. Then,

the residuals between the preliminary analysis value from the

MLR and the certified value are corrected using the LIBS and

NIRS data based on PLS method. The result represents the

sum of the preliminary analysis value and corrected residuals.

(3) Non-linear multivariate regression coupled with PLS (non-

linear MR_PLS) methodology directly used the determined

ash and moisture results and their quadratic term to

preliminarily determine the calorific value and volatile matter

based on the MLR method, which is called non-linear

multivariate regression.53 Then, the residuals between the

preliminary analysis value obtained from the non-linear

multivariate regression and the certified value are corrected

using the LIBS and NIRS data based on PLS. The result is the

sum of the preliminary analysis value and the corrected

residuals.

These quantitative analysis models were evaluated according to

R2, RMSEP, AAE and mRSD. The mRSD denotes the mean value

of the relative standard deviation (RSD) of all samples in the

validation set, and the RSD value is calculated based on 4 repeated

measurements. In the PLS regression model, the input variables

were selected according to the PLS regression coefficient method

described in the literature.54

EXPERIMENTAL

Coal samples. The coal samples used in this study were 60 coal

samples collected from different coal-fired power plants located in

China. The coal samples were ground and sieved to particle sizes

of less than 0.2 mm. The certified calorific value, volatile matter,

ash content, moisture content, and fixed carbon content were

analyzed by standard off-line methods55,56 on an air-dried basis.

Table 2 lists the statistical results of the certified values of coal

samples. Among the chosen samples, 12 coal samples were

randomly selected for the validation set. The remaining 48 coal

samples were assigned to the calibration set.

Table 2 Statistical results of the certified value of coal properties

Coal property Minimum Maximum Mean S.D.

Calibration

set

Ash content (%) 7.30 43.04 23.54 10.58

Moisture content (%) 0.43 10.58 3.38 3.08

Calorific value (MJ/kg) 17.87 27.85 23.65 2.29

Volatile matter (%) 7.91 41.37 22.92 11.03

Fixed carbon (%) 42.76 66.63 50.17 6.72

Validation

set

Ash content (%) 9.32 41.84 25.46 11.16

Moisture content (%) 0.64 9.72 3.56 3.50

Calorific value (MJ/kg) 18.38 25.92 22.89 2.22

Volatile matter (%) 9.44 37.39 22.32 10.52

Fixed carbon (%) 43.85 61.67 48.67 6.19

LIBS spectra collection. In present work, the LIBS and the NIRS

setup are independent instruments and LIBS and NIRS spectra

were collected independently. A detailed description of the LIBS

experimental setup is provided in the literature57. Briefly, the

excitation source was a 1064 nm Q-switch Nd: YAG pulse laser

(Quantel Brilliant Easy, USA). The laser pulse duration was 5 ns,

and the repetition frequency was 2 Hz. The laser energy was

optimized to 55 mJ per pulse. The laser was focused using a lens

with a focal length of 50 mm to ablate the sample and generate

plasma. The focal point was located 2 mm below the sample

surface. Plasma emission was coupled to an optical fiber and then

transmitted to a four-channel spectrometer (Avantes, Netherlands).

The spectrometer covered a wavelength range of 178 to 827 nm,

with a nominal resolution of 0.08-0.1 nm and fixed gate-width of

1.05 ms. The delay time of the spectrometer acquisition signal was

set as 1 μs. During the experiment, approximate 3 g of powdered

coal from each sample was pressed into a 25 mm diameter pellet

using a tablet machine (Pike Technologies Crushir). The sample

pellets were exposed to ambient air and placed on an automatic

three-dimensional mobile platform. For each sample, 320 spectra

were collected from 16 locations on the pellet’s surface.

NIRS spectra collection. NIRS spectra were collected using an

MPA Type Fourier transform NIR spectrometer (Bruker,

Germany). The instrument was controlled using the software

package OPUS7.5 from Bruker and NIRS spectra were collected

in diffuse reflectance mode. The background spectrum was

collected using air as the blank sample to correct the coal spectra.

Each powdered coal sample was held in a diffuse reflectance

sample cup and, the surface was flattened using a spatula. Each

spectrum was recorded by co-adding 64 scans over the range of

12,500 to 4000 cm-1 at a spectral resolution of 4 cm-1 and 4 NIRS

spectra were collected from each coal sample.


Typical LIBS and NIRS spectrum of coal collected in this study is

presented in Fig. 2. LIBS spectra were preprocessed using the

channel normalization method to reduce the fluctuation of spectral


Fig. 2 Typical spectrum of coal: (a) LIBS, (b) NIRS.

Fig. 3 Ash content analyzed using LIBS.

Fig. 4 Moisture content analyzed using NIRS

Table 3 Statistical results of the certified value of coal properties60-64

Empirical equation Coal origin GCV range

(MJ/kg)

GCV = -0.11M+0.33VM+0.35FC-0.003A * India 12.75-28.37

GCV = -37.77-0.647M+0.089VM-0.387A* United States 4.82-34.85

GCV = 35.391-0.47M-0.028VM-0.364A * Afghanistan 3.66-32.65

GCV = -0.266M+0.293VM+0.371FC-0.004A * United States 4.82-34.85

GCV = -0.053M+0.296VM+0.353FC-0.002A * China 12.71-26.53

GCV =76.56-1.30 (VM+A)+7.03×10-3(VM+A2 ** Turkish 15.63-30.37

*Received basis; **Dry basis; Where GCV, M, VM, FC and A denote the gross calorific value,

moisture content, volatile matter, fixed carbon, and ash content, respectively.

data.58 NIRS spectra were preprocessed using standard normal

variate analysis to reduce baseline variations and particle-size

scattering effects.59 Subsequently, 320 LIBS spectra of each coal

sample were equally divided into four sets, and the spectra in each

set were averaged. Thus, four LIBS averaged spectra and four

NIRS spectra representing each sample were used to establish

quantitative analysis models of the coal properties. LIBS and

NIRS spectra were normalized using decimal scaling for data

fusion.

Ash and moisture content. The ash content was analyzed using

LIBS, and the results are presented in Fig. 3. The error bars

represent the standard deviation calculated from four repeated

measurements. The R2, RMSEP, AAE, and mRSD are 0.990,

0.743%, 0.628%, and 2.005%, respectively. The moisture content

was analyzed using NIRS and the result is presented in Fig. 4. The

R2, RMSEP, AAE, and mRSD are 0.998, 0.304%, 0.241%, and

15.191%, respectively. These results indicate that the ash and

moisture content can be accurately determined by LIBS and NIRS

owing to their simple characteristics. Ash consists of inorganic

oxides, the elements of which correspond to obvious characteristic

lines in the LIBS spectrum. NIRS lacks sensitivity to inorganic

substances but can detect vibrations of bond O-H, which are

related to moisture.

Correlation between coal properties. The empirical equation

correlating the calorific value with proximate analysis was first

proposed by Goutal in early 20th century. Subsequently, several

empirical equations that consider ash and moisture as important

components for calculating the calorific value of coal on a dry

basis or received basis, have been developed, as listed in Table 3.

It can be observed from the reported empirical equation that ash

and moisture contribute negatively to the calorific value.

Volatile matter is related to the decomposition of organic

substances and associated mineral matter.47 To quantify the

correlation between the calorific value, volatile matter, and

determined ash and moisture results, a distance correlation (DC)

analysis was conducted. The DC method can disclose both linear

and nonlinear relationships between the independent variable (X)

and dependent variable (Y).65 As presented in Table 4, the distance

correlation coefficient for the calorific value with ash or moisture


Table 4 Results of distance correlation analysis

Ash content Moisture content

Calorific value Correlation 0.797 0.523

Sig. <0.05 <0.05

Volatile matter Correlation 0.905 0.912

Sig. <0.05 <0.05

Fig. 5 The X loading weights of factor 1 for the calibration models based

on the GS approach: (a) calorific value (b) volatile matter.

exceeds 0.523. The distance correlation coefficient is greater than

0.880 for the volatile matter, ash, and moisture. Besides,

significance test was conducted according to the correlation

coefficient and t test66 at the 95% confidence level. Therefore,

calorific value and volatile matter strongly correlate with the ash

and moisture content. The ash and moisture results were

considered for the determination of calorific value and volatile

matter.

Calorific value and volatile content. As discussed above,

calorific value and volatile matter have complex characteristics,

which are related to both elemental and molecular composition

and correlate with ash content and moisture content. Thus, the

calorific value and volatile matter were analyzed by the

combination of NIRS and LIBS data with ash and moisture results

using GS, MLR-PLS and non-linear MR-PLS approaches. For

comparison, the conventional approach based on PLS with only

the LIBS and NIRS data was also used to determine the calorific

value and volatile matter. The X loading weights for the calibration

models based on the GS approach are presented in Fig. 5 for

calorific value and volatile matter. The loading weight of ash and

moisture data is greater than that of most variables, which indicates

that the ash and moisture data play an important role in the

determination of calorific value and volatile matter.

The analysis results of the calorific value and volatile matter

obtained from different approaches are shown in Figs. 6 and 7. The

results obtained from the GS, MLR_PLS and non-linear MR_PLS

approaches are better than those from conventional approach.

Moreover, the non-linear MR_PLS approach exhibits the best

performance. When compared with the conventional approach, for

the non-linear MR_PLS, the RMSEP, AAE and mRSD decrease

from 0.536 MJ/kg to 0.187 MJ/kg, 0.449 MJ/kg to 0.154 MJ/kg

and 0.937% to 0.262%, respectively, for the calorific value, and

from 1.125% to 0.662%, 0.940% to 0.506% and 3.192% to

1.045%, respectively, for volatile matter. In the non-linear

MR_PLS method, non-linear multivariate regression manages the

linear and non-linear relationships between the calorific value,

volatile matter, ash, and moisture. Furthermore, the residuals

between the preliminary analysis value obtained from the non-

linear multivariate regression and the certified value are corrected

by LIBS and NIRS data, which provides abundant elemental and

molecular information related to the calorific value and volatile

content.41

Fixed carbon content. The fixed carbon content was indirectly

determined by subtracting the resultant summation of ash,

moisture and volatile content from 100%.45 To obtain all

proximate analysis indices, the fixed carbon content was

calculated using its definition formula with the results of ash and

moisture and the result of volatile matter predicted by the non-

linear MR_PLS method. The calculation results for fixed carbon

content are shown in Fig. 8.

CONCLUSIONS

A step-by-step method to synergistically utilize spectral

information was proposed to improve the analysis of various

properties of coal with different characteristics. The ash content

was determined using LIBS, and the moisture content was

determined using NIRS. The RMSEP value of ash and moisture

content are 0.743% and 0.304%, respectively. Subsequently, the

ash and moisture results were combined with the fusion data of

LIBS and NIRS to determine calorific value and volatile matter,

and this approach outperformed the conventional approach that


Fig. 6 Analysis results for calorific value obtained from different approaches: (a) conventional approach, (b) GS, (c) MLR_PLS, and (d) non-linear MR_PLS.

Fig. 7 Analysis results for volatile matter obtained from different approaches: (a) conventional approaches, (b) GS, (c) MLR_PLS, and (d) non-linear

MR_PLS.


Fig. 8 Calculated results for fixed carbon content.

used only the fusion data of LIBS and NIRS. The non-linear

MR_PLS approach exhibited the best performance. Compared

with the conventional approach, for the non-linear MR_PLS

method, the RMSEP, AAE and mRSD for calorific value

decreased from 0.536 to 0.187 MJ/kg, 0.449 to 0.154 MJ/kg and

0.937% to 0.262%, respectively, and the corresponding values for

volatile matter decreased from 1.125% to 0.662%, 0.940% to

0.506%, and 3.192% to 1.045%, respectively. Following the

analysis of ash, moisture, and volatile matter content, the fixed

carbon content was determined using its definition formula. These

results demonstrate that the proposed method can improve the

accuracy and precision of coal analysis.

AUTHOR INFORMATION

Shunchun Yao received his Bachelor's in

2006 from the Soochow University, China,

and PhD in 2011 from the school of Electric

power, South China University of

Technology (SCUT). He is a research

professor of power engineering and

engineering thermos-physics at the school of

electric power, SCUT. His major research

interests are laser-induced breakdown

spectroscopy (LIBS), tunable diode laser

absorption spectroscopy (TDLAS), and their application to intelligent

sensing and on-line monitoring for clean and efficient utilization of energy.

He has been working as associate director in Guangdong Province

Engineering Research Center of High Efficient and Low Pollution Energy

Conversion and LIBS young scientist of Chinese Society for Optical

Engineering. Shunchun Yao is author or co-author of over 90 articles

published in peer-reviewed scientific journals, with an h-index of 20

(Scopus). His work is supported by Guangdong Natural Science Funds for

Distinguished Young Scholar.

Zhe Wang received his bachelor and

master’s degree in 1998 and 2001,

respectively, from Tsinghua University, and

Ph.D in 2007 from the Pennsylvania State

University. He is a professor of the

Department of Energy and Power

Engineering, Tsinghua University. His

major research interests are laser-induced

breakdown spectroscopy (LIBS)

quantification and its applications. He has

been been the general secretary and executive associate director of the

professional committee of laser-induced breakdown spectroscopy of

Chinese Optical Society since 2016. Zhe Wang is author of over 100

articles published in peer-reviewed scientific journals, with an h-index of

34 (Web of Science). He was awarded the international contribution

award of the Asian Symposium on LIBS.


*S. C. Yao


*Z. Wang


Notes


ACKNOWLEDGMENTS

This work was supported by the National Natural Science Fund of

China (51676073), Guangdong Natural Science Funds for

Distinguished Young Scholar (2021B1515020071), The Fok

Ying-Tong Education Foundation for Young Teachers in the

Higher Education Institutions of China (171047), Foundation of

State Key Laboratory of High-efficiency Utilization of Coal and

Green Chemical Engineering (2022-K72), Technological

Innovation and Entrepreneurship Team Project of Foshan

(1920001000052), Natural Science Foundation of Guangdong,

China (2022A151501074), the Guangdong Province Key

Laboratory of Efficient and Clean Energy Utilization

(2013A061401005).

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High-precision Fe Isotope Analysis on MC-ICPMS Using a 57Fe-58Fe Double Spike Technique

Xin Li, Yongsheng He,* Shan Ke, Ai-Ying Sun, Yin-Chu Zhang, Yang Wang, Ru-Yi Yang, and Pei-Jie Wang

State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing 100083, P.R. China

Received: March 11, 2022; Revised: April 15, 2022; Accepted: April 15, 2022; Available online: April 16, 2022.

DOI: 10.46770/AS.2022.062

ABSTRACT: Herein we report procedures based on multi-collector inductively coupled plasma mass spectrometry (MC-

ICPMS) for high-precision Fe isotopic analysis using a 57Fe-58Fe double spike technique. Iron purification was achieved using AG1-

X8 in HCl media following previously or newly established procedures. In the new procedure, smaller columns with 4 mm diameter

were used, containing 0.4 mL AG1-X8, thus greatly reducing the operation time and the amount of acid and resin consumed

compared to the previously established method using 1 mL resin. Potential trace Ni interference on 58Fe was suppressed by increasing

the total Fe ion intensity to ≥ 120 V. Measurements of GSB Fe solutions doped with mono-elements demonstrated that a mass bias

correction by the 57Fe-58Fe double spike was robust if Ca/Fe ≤ 1.0, Al/Fe ≤ 1.0, Cu/Fe ≤ 1.0, Co/Fe ≤ 0.1, Ni/Fe ≤ 10-4, and Cr/Fe ≤

10-4. Monitoring of pure Fe standard solutions, viz. IRMM-014 and NIST3126a, and geological reference materials, viz. JP-1,

BHVO-2, W-2a, GSP-2, and COQ-1, over nine months

yielded δ56Fe (relative to IRMM-014) values of 0.003 ±

0.013‰ (2 SD, N = 20), 0.368 ± 0.011‰ (2 SD, N = 30),

0.019 ± 0.018‰ (2 SD, N = 15), 0.109 ± 0.017‰ (2 SD, N =

30), 0.049 ± 0.018‰ (2 SD, N = 17), 0.155 ± 0.018‰ (2 SD,

N = 14), and -0.066 ± 0.022‰ (2 SD, N = 20), respectively,

consistent with the recommended values within quoted errors.

Based on repeated analyses of the standards, the long-term

precision of our double spike method is better than 0.02‰ for

δ56Fe on average, proving its ability to distinguish small

isotope fractionation among high-temperature samples.

INTRODUCTION

Iron (Fe) has long been of great interest to geochemists because of

its abundance, role in biological activities, and sensitivity to redox

changes.1-3 It has four stable isotopes, 54Fe, 56Fe, 57Fe, and 58Fe,

with relative abundances of 5.84, 91.76, 2.12, and 0.28%,

respectively. The mass difference among them (up to 6.9%

between 54Fe and 58Fe) intrinsically drives isotope fractionation,

which is a potential tool for tracing a variety of geological,

biological, and environmental processes, such as partial melting,4,5

magma differentiation,6-11 mantle metasomatism,12-14 and redox

exchange between the deep Earth and surficial spheres,15-17 redox

change of paleo-oceans,18,19 and microbe and human

metabolism.20-22

Compared with low-temperature archives, Fe isotope

fractionation among the high-temperature geological samples is

relatively restricted. For example, the majority of the pristine

mantle peridotites, island arc basalts and boninites, mid-ocean

ridge basalts, and ocean island basalts display a limited δ56Fe

variation of 0.10 0.15‰.1 Hence, high-precision isotopic

analyses are necessary to resolve and decode Fe isotope

fractionation in high-temperature systems. However, there are

analytical challenges in obtaining high-precision and accurate Fe

isotopic data, arising mainly from isobaric interferences and the

correction of instrumental mass bias. Polyatomic ion interferences

(e.g., 40Ar16O+ on 56Fe+) are routinely discriminated by (pseudo-)

high mass resolution on new generations of MC-ICPMS (e.g., Nu

Plasma 1700, Nu Plasma, and Neptune plus).23-26 Instrumental


mass bias can be corrected using standard sample bracketing

(SSB),23-25 element doping,8, 25-28 and double spike techniques.29-31

The SSB method is used widely and assumes mass bias during

sample measurements to be the same as that of bracketing standard

measurements. Validation of this assumption requires a well-

controlled laboratory environment (e.g., temperature, humidity,

and air exhaust rate), instrument stability, and a highly matched

matrix (1 and the references therein).The element doping method

assumes that the isotopes of the doped element exhibit an isotope

fractionation behavior similar to those isotopes of the element of

interest and helps to overcome mass bias fluctuations.1, 32

Nevertheless, the doped element and the element of interest (e.g.,

Ni and Fe) could exhibit different isotope fractionation behaviors

during measurements.31 In an optimized case, a long-term

precision (2 SD) for 56Fe/54Fe better than 0.03‰ can be routinely

achieved, provided that each sample is measured nine times by

MC-ICPMS.23, 25, 31

Theoretically, the double spike technique can be used to

accurately correct instrumental mass bias and yield high-precision

isotopic data.33 Millet et al.30 reported a procedure to measure Fe

isotope ratios using a 57Fe-58Fe double spike and demonstrated that

the external reproducibility could be as good as 0.02‰ for δ56Fe.

Nevertheless, isobaric interferences (e.g., trace 58Ni+ on 58Fe+ and

polyatomic interferences) may hinder accurate analyses.29,30 In

particular, 58Ni is an abundant isotope (~68.08% 34), whereas 58Fe

is a trace isotope (~0.28% 35). The improper correction of trace 58Ni+ interference using 60Ni+ routinely monitored on a Faraday

cup may cause significant uncertainty in the deduced 56Fe/54Fe

using the double spike method (namely, the trace Ni correction

problem).29 Finlayson et al.29 illustrated that the precision of δ56Fe

can be improved three-fold from ±0.145 to ± 0.052‰ when 60Ni+

is monitored on an ion counter instead of a Faraday cup, allowing

improved correction of 58Ni+ interference.

In this study, we developed an improved purification procedure

to efficiently separate Fe from the sample matrix and present high-

precision Fe isotope ratio measurements utilizing a 57Fe-58Fe

double spike. The on-peak-zero fluctuations (partially contributed

by tails of polyatomic ions) and trace Ni correction problems were

suppressed by increasing the signal-to-noise ratio. The robustness

of this method was demonstrated by repeated measurements of

solutions (GSB Fe, IRMM-014, and NIST3126a) and geological

standards (JP-1, BHVO-2, W-2a, GSP-2, and COQ-1), indicating

a 2 SD reproducibility and accuracy for δ56Fe better than 0.020‰.

Fe isotopic analyses of such high precision can help distinguish

limited Fe isotope fractionation during high-temperature processes.

THEORETICAL OPTIMIZATION

Iron isotopic analyses using a 57Fe-58Fe double spike on MC-

ICPMS have been previously attempted in Zhu et al.31 In this early

study, δ56Fe was measured at low signals (i.e., a 56Fe signal within

15 5 V), yielding a long-term precision of 0.10‰ for single time

measurements and showing no superiority in precision or accuracy

compared to that of the SSB method using element doping.31 The

merits of the double spike technique in accurate mass bias

correction may have been counteracted by the trace Ni correction

problem stemming from the introduction of the trace isotope 58Fe

into data deduction.29 The trace Ni correction problem was

previously overcome by monitoring 60Ni on an ion counter.

However, this required the measurements to be in dynamic

mode.29 Herein, we evaluate the trace Ni correction problem via

Monte Carlo calculations for measurements using Faraday cups

only. Theoretical runs were simulated for a spiked standard with

δ56Fe = 0, an optimized 57Fe-58Fe double spike proportion31,33

(referred to as q hereafter) of 0.457 and a mass bias (referred to as

β hereafter) of -1.5. Internal errors from counting statistics and

Johnson noise of the measured ratios during data acquisition were

calculated based on equations listed by Lehn et al.36 using an

integration time of 4.194 s; 1011 Ω resistors were adopted for all

Faraday cups, except that a 1010 Ω resistor was used for 56Fe when

its signal was > 40 V. To account for the trace Ni correction

problem, trace Ni (60Ni/58Fe = 10-7) was introduced. Despite being

theoretically negligible, it could cause a problem during

measurements. The non-linear behavior of Faraday cups

connected with the 1011 Ω resistors has been previously observed

for signals below 1 mV on the Nu instrument29, and the hardware

manual for Neptune plus suggests that 1011 Ω resistors may not

accurately measure signals < 0.1 mV. A limit (e.g., 0.1 mV, 0.2 mV,

and 0.3 mV) was set, and for a signal below this limit (i.e., 60Ni in

this study), the relative error from Johnson noise (σjn) contributed

by this signal was calculated by dividing the value of the limit with

that of the signal, i.e., [limit]/[signal]. Compared to the theoretical

value of 0.02 mV for 4.194 s of integration on 1011 Ω resistors,36

this assumption experientially enlarged the Johnson noise of the

trace 60Ni signal to examine the non-linear and inaccurate behavior

during data acquisition. Measured ratios were randomly obtained

for 40 cycles by setting normally distributed errors around

theoretical values, and then the mean δ56Fe and two standard errors

(2 SE) were deduced using equations listed in He et al.37 The

calculation was performed and repeated 1000 times for the sum of

the signals ranging from 2 to 200 V with an increment of 2 V. The

results are illustrated in Fig. 1 and show considerable extra errors

from the correction of trace Ni interference being propagated to

the deduced mean δ56Fe, particularly at a low sum of signals. For

example, for a sum of signals of 10 V, the internal error for δ56Fe

is 0.099‰, 0.162‰, and 0.235‰ when the trace signal limit is set

to 0.1 mV, 0.2 mV, and 0.3 mV, respectively, compared to the case

(~0.064‰) without Ni correction. Internal errors for δ56Fe with or

without the trace Ni correction problem converge at a high sum of

signals, and the trace Ni correction problem can be suppressed by

an increased signal-to-noise ratio or with a sum of signals >120 V.


Fig. 1 Theoretical and practical internal errors (two standard errors of the

mean, 2 SE) change with the sum of signals (V). The theoretical lines are

based on Monte Carlo calculations for a double spiked standard (δ56Fe = 0)

using the 57Fe-58Fe double spike adopted in our lab having an optimized q

(~0.457) and a mass bias of -1.5. Integration errors of ratios were calculated

using the equation developed by Lehn et al.36

with consideration of the trace

Ni correction problem (for details refer to the main text). The limit (0.1 mV,

0.2 mV, and 0.3 mV) for the trace signals are marked on the curves. The

practical internal errors are illustrated by the bracketing GSB standard

solutions measured during this study, with the red and hollow diamonds

representing the results on the two different Neptune Plus used.

Fig. 2 Elution curves of Fe and matrix elements obtained for BHVO-2

using 4-mm diameter columns filled with 0.4 mL AG1-X8 resin. “Matrix”

is the concentration weighted mean of K, Na, Mg, Al, Ca, Ti, and Mn.

Table 1. New purification procedure for geological samples using AG1-

X8 resin

Separation stage Reagents Volume (mL)

AG1-X8 In MQ H2O 0.4

Clean resin MQ H2O 1

Clean resin 0.4N HCl 2

Condition 6N HCl 1

Load sample 6N HCl 0.1

Elute matrix 6N HCl 0.5

Elute matrix 6N HCl 0.5

Elute matrix 6N HCl 1

Elute matrix 6N HCl 2

Collect Fe 0.4N HCl 0.5

Collect Fe 0.4N HCl 0.5

Collect Fe 0.4N HCl 1

Collect Fe 0.4N HCl 2

EXPERIMENTAL METHODS

Reference materials and the 57Fe-58Fe double spike. Three pure

Fe standard solutions (GSB Fe, IRMM-014, and NIST3126a)

were used to monitor the reproducibility of isotopic analyses using

mass spectrometry. GSB Fe, an in-house standard prepared by

mixing mono-element solutions from the China Iron and Steel

Research Institute (CISRI), has a δ56Fe of 0.729 0.005‰ (2 SE)

relative to IRMM-014.23 GSB Fe that passed through the column

was used as a bracketing standard and doped with mono-element

solutions from CISRI for the doping experiments. Five well-

characterized rock standards from the United States Geological

Survey (USGS) and the Geological Survey of Japan (GSJ) were

processed and analyzed to evaluate the reproducibility and

accuracy of the procedure. A 57Fe-58Fe double spike was adopted

and prepared using single spikes from the Oak Ridge National

Laboratory (USA), according to the optimized proportion

calculated by Rudge et al.33 The double spike composition was

previously calibrated,30 with 54Fe/56Fe = 0.026621 ± 0.000005, 57Fe/56Fe = 3.8452 ± 0.0005, and 58Fe/56Fe = 3.9807 ± 0.0005.

Sample digestion and chemical purification. Sample powders

containing ≥ 250 μg of Fe were dissolved in a mixture of

concentrated HF-HNO3 (2:1, v/v). After the optical check,

precipitate-free solutions were evaporated to dryness at 160 °C and

refluxed in aqua regia (HCl:HNO3 = 3:1, v/v) and trans aqua regia

(HNO3:HCl = 1:2, v/v) to remove fluorides and completely

oxidize Fe2+. The resultant solutions were dried at 90 °C and

refluxed with concentrated HCl. The final samples were dissolved

in 0.5 mL of 6 N HCl prior to column chromatography. Fe was

purified using AG1-X8 resin (200-400 mesh chloride form, Bio-

Rad, Hercules, CA, USA) (Table 1). The resin was pre-cleaned by

sequentially rinsing with 6 N HCl, 1 N HNO3, 0.4 N HCl, and

Milli-Q H2O. At the beginning of this study, the previously

established procedure23 was adopted using 10 mL columns from

Bio-Rad, USA, having a diameter of ca. 8 mm, filled with 1 mL

AG1-X8 resin. Most of the matrix elements were eluted with 8 mL

6 N HCl, and Fe was collected using 9 mL of 0.4 N HCl. The

recovery of Fe was estimated to be ~99.95 ± 0.13% (2 SD, n = 5)

after a single pass.23 Chromatography was routinely repeated a

second time to completely remove interference elements, such as

Cr and Ni.

To reduce the amount of acid and resin consumption and

increase efficiency, smaller columns from Booming Technology

Limited, China (http://www.boomingtec.com), having a diameter

of 4 mm, packed with 0.4 mL AG1-X8 resin, were also tested. The

elution behavior of the new column was evaluated using BHVO-

2, and the results are shown in Fig. 2. Most major (e.g., K, Ca, Na,

Mg, Al, Ti, and Mn) and trace (e.g., Cr and Ni) matrix elements

could be quantitatively (> 99.9%) washed out in the first 4 mL of

6 N HCl, except for Cu and Co, which could be efficiently eluted

without losing Fe by using more 6 N HCl. Since most high-

temperature samples (except for Cu- and Co-bearing minerals or

http://www.boomingtec.com/


Table 2. Comparison of different purification processes

References Resin and volume Column diameter Wash acid volume Collect Fe volume Time

This study 0.4 mL AG1-X8 4 mm 4 mL 6N HCl 4 mL 0.4N HCl ~4 h

He et al. (2015) 23 1 mL AG1-X8 8 mm 8 mL 6N HCl 9 mL 0.4N HCl ~8 h

Millet et al. (2012) 30 0.5 mL AG1-X4 / 6 mL 6N HCl+0.001% H2O2 4 mL 0.05N HCl /

Chen et al. (2017) 26 2 mL AG MP-1M / 46 mL 8.2N HCl+0.01% H2O2 18 mL 2N HCl+0.01% H2O2 /

Gong et al. (2020) 28 1 mL AG MP-1M 8 mm 5 mL9N HCl+5 mL 6N HCl+0.5 mL

1N HCl 1.5 mL 1N HCl /

Fig. 3 Mass bias correction by the 57Fe–58Fe double spike method illustrated

by two representative sessions on April 11, 2021 and September 04, 2021.

Deduced 56Fe (open symbols) show no drifting with time, despite a

fluctuation in mass bias (as filled symbols expressed as values calculated

using β relative to the first measurement of each session) is noted.

ore deposits) have low Cu/Fe and Co/Fe ratios (e.g., as low as

0.0015 and 5×10-4 for BHVO-2), we used 4 mL of 6 N HCl to

elute the matrices for efficiency. Iron was then collected by 4 mL

of 0.4 N HCl. As demonstrated by BHVO-2 after a single pass, the

yield of Fe was approximately 99.67 ± 1.02% (2 SD, n = 4), and

all major isobaric element/Fe ratios were reduced to insignificant

levels (e.g., Mg/Fe < 3.0×10-5, Al/Fe < 1.5×10-5, Ca/Fe < 4.3×10-

4, Cr/Fe < 1.8×10-5, Mn/Fe < 3.0×10-5, Co/Fe < 5×10-7, and Ni/Fe

< 3.7×10-7). Compared with previous schemes (Table 2), this

column procedure consumed less resin and acid, making it more

efficient to operate (i.e., 8 h for the previous procedure23 compared

to 4 h for the new procedure). The same column procedure was

repeated twice to purify Fe further. The collected Fe was

evaporated to dryness, acidified with concentrated HNO3 to

reduce possible organic materials from resins,31 then dissolved in

0.15 N HNO3 prior to isotopic analyses.

The whole procedure blank, including digestion, column

chromatography, and measurement, was less than 15 ng and

considered negligible (< 2‰) compared to the ≥10 μg of sample

Fe processed.

Mass spectrometry. Iron isotopic analyses were conducted on a

Thermo Fisher Neptune Plus MC-ICPMS at the Isotope

Geochemistry Lab at the China University of Geosciences,

Beijing (CUGB). The samples were introduced into the plasma

through a PFA self-aspiration micro-nebulizer with an uptake rate

of 50 μL/min and a Scott double-pass quartz glass spray chamber,

which used a Cetac ASX-110 autosampler. Iron isotopes were

measured at the lower mass shoulders in high-resolution modes,

with 53Cr, (54Cr + 54Fe), 56Fe, 57Fe, (58Fe + 58Ni), and 60Ni statically

collected by Faraday cups, L3, L1, C, H1, H2, and H4, respectively. 53Cr and 60Ni were monitored to correct the isobaric interferences

of 54Cr and 58Ni using experiential ratios previously obtained for

100 ppb GSB Cr and Ni solutions (54Cr/53Cr = 0.2571 and 60Ni/58Ni = 0.408761).23 The sample and standard solutions were

spiked in optimized q ~0.46 prior to either the column chemistry

or isotopic analyses, with bulk concentrations of 8–12 ppm to

obtain a total Fe signal >120 V. To account for the >70 V 56Fe

signal, a 1010 Ω resistor amplifier was used for the central Faraday

cup, and 1011 Ω resistors were used for the other cups. Each

analysis consisted of an idle time of 3 s and 40 cycles of 4.194 s

integration time. Each analysis sequence consisted of a blank of

0.15 N HNO3 for the on-peak-zero correction and several blocks

of sample and standard solutions, which were routinely repeated

four times. GSB Fe was used as the bracketing standard for every

five samples. The data were deduced offline using an Excel macro

program.31 A burst of residual argide-based interferences30 was not

observed, as indicated by the absence of a jump in the deduced

δ56Fe of bracketing GSB Fe in each session (Fig. 3). Nevertheless,

a monotone drift in the deduced δ56Fe value of bracketing GSB Fe

was observed when the high-resolution split was aged. In this case,

the split was replaced before the validated measurements. The Fe

isotopic compositions were reported in δ notation, (56Fe (‰) =

[(56Fe/54Fe)Sample/(56Fe/54Fe)Standard - 1]1000), relative to IRMM-

014 unless specified otherwise, and were converted by the

following equation23: 56FeIRMM-014 = 56FeGSB Fe + 56FeIRMM-014

(GSB Fe) + 56FeGSB Fe 56FeIRMM-014 (GSB Fe) / 1000. The

isotopic data were reported as the mean values of four repeated

analyses, and the uncertainties were given as two standard errors

of the mean (2 SE), with an error of 56FeIRMM-014 (GSB Fe) being

considered ((2 SE)2IRMM-014 = (2 SE)2

GSB Fe + 0.0052).23


Internal and external errors arising from data acquisition. As

shown in Section 2, the internal errors (2 SE) during the data

collection by MC-ICPMS are predicted in Fig. 1 after Lehn et al.36

while considering the trace Ni correction problem. Most of the


Fig. 4

Results for pure Fe solution standards (a) IRMM-014 and (b)

NIST3126a measured over nine months. The horizontal solid lines and gray

bars represent the mean values and two standard deviations, respectively,

calculated for the results of multiple analyses (large black diamonds)

consisting of four single-time measurements (gray small diamonds) each.

Recommended values (RV) for IRMM-014 and NIST3126a are from He

et al.23

and Zhu

et al.31, respectively. For raw data, refer to Table S1.

Fig. 5 Measured 56Fe for a suite of solutions with known isotope

compositions that were prepared by mixing IRMM-014 and GSB Fe.

measurements in this study were done using Fe solutions ≥ 8 ppm

to maintain a sum of signals ≥ 120 V, while a few measurements

were performed at a lower sum of signals (~87 V), either of which

yielded internal errors of δ56Fe mostly below the expected values

with a limit for the trace signal of ~0.3 mV (Fig. 1). As exemplified

by bracketing GSB standards, internal errors of δ56Fe were 0.018

± 0.009‰ (2 SD, n = 209) and 0.029 ± 0.013‰ (2 SD, n = 44) for

measurements having the sum of signals within 145.5 ± 30.6 V

and 86.9 ± 5.2 V, respectively. To monitor the external precision

for single-time measurements on MC-ICPMS, IRMM-014 and

NIST3126a solutions were measured against GSB Fe via different

analytical sessions over nine months, and ~0.030‰ for δ56Fe (2

SD) (Fig. 4) was obtained. The long-term precision could be

further improved via multiple analyses by a factor of n, where n

is the number of measurements on each sample.8,23 Herein, we

routinely measured each sample four times using MC-ICPMS,

and the external precision was expected to be approximately

0.015‰, confirmed by the results of IRMM-014 and NIST3126a.

The long-term mean δ56Fe of IRMM-014 relative to GSB Fe was

-0.726 0.013‰ (2 SD, N = 20), whereas that of NIST3126a

relative to IRMM-014 was 0.368 0.011‰ (2 SD, N = 30) (Fig.

4), agreeing well with the long-term values previously obtained by

the SSB and Ni-doping methods in the same lab (-0.729 0.005‰

(2 SE);23 0.365 0.005‰ (2 SE)31). To further validate the

accuracy and ability of this method to distinguish minor Fe isotope

fractionation, a suite of Fe solutions with known isotopic

compositions was prepared by mixing IRMM-014 with GSB Fe.

Measurements of these solutions yielded a shift of -0.007 0.013‰

(2 SD, N = 6) between the measured and expected δ56Fe (Fig. 5).

These results demonstrate that by increasing the signal-to-noise

ratios, the trace Ni correction problem could be suppressed, and

δ56Fe could be routinely measured using MC-ICPMS with an

external precision and accuracy better than 0.02‰, provided that

each sample was measured four times.

Evaluation of isobaric interference and matrix effect. The

presence of matrix elements in the sample solutions may have

caused bias during Fe isotopic analyses on MC-ICPMS owing to

isobaric interference and matrix effects.23,25,30 Herein, we

evaluated the effect of Cu, Co, Ca, Al, Cr, and Ni on Fe isotopic

analyses using a 57Fe-58Fe double spike by measuring the GSB Fe

doped with mono-element solutions (Fig. 6). Cu and Co could not

be completely removed by chromatography using either a

previously established method or the one described herein.23 The

matrix effect of Cu and Co with [Cu or Co]/Fe ratios up to 1:1 was

previously demonstrated to be negligible for the SSB method.23

Thus, it was not surprising to find no bias in the deduced δ56Fe

values for Cu (Fig. 6a). Nevertheless, the doped solution with

Co/Fe = 1:1 yielded a biased high δ56FeGSB up to 0.074 0.018‰

(2 SE), although this effect was negligible when Co/Fe ≤ 0.1 (Fig.

6b). Herein, we attributed the bias caused by doped Co to 59Co1H+

as an isobaric interference on 60Ni+, indicated by a higher 60Ni/54Fe


Fig. 6 Doping experiments to evaluate the matrix effect on 56Fe determination. The horizontal solid lines and gray bars represent the expected value banded

by the long-term reproducibility (0 ± 0.02‰). For raw data, refer to Table S2.

of ~0.000410 for the Co/Fe = 1:1 solution, compared to that of the

bracketing GSB Fe (~0.000024). Over-correction of 58Ni+, caused

by 59Co1H+ with a productivity of 5 ppm, could explain the

observed erroneously high δ56FeGSB when Co/Fe = 1. The matrix

effect of Co was not expected to cause any significant bias on the

deduced δ56Fe for most high-temperature samples wherein it is at

trace levels. However, special caution was suggested to

sufficiently remove Co from Co-abundant samples (e.g., Co-rich

crust) when Fe isotopic analyses were conducted using the double

spike method. Calcium and Al possibly generated polyatomic ion

interferences (40Ca14N+ and 27Al2+ on 54Fe+, 40Ca16O+ on 56Fe+, and

40Ca16O1H+ on 57Fe+) which could not be resolved by double spike

data deduction. Millet et al.30 observed using a Nu Plasma MC-

ICPMS that the presence of 20% Ca and ≥5% Al could lead to an

erroneously low deduced δ56Fe. Herein, we observed no bias in

the deduced δ56Fe values for the doping experiments with Ca and

Al (Fig. 6c and 6d). This indicates that either the productivity of

polyatomic ion interferences was low or that these interferences

were discriminated by the high mass resolution mode on our

instruments. However, even in the highest mass resolution mode,

isobaric 54Cr+ and 58Ni+ could not be resolved from Fe, which was

routinely monitored by 53Cr+ and 60Ni+ and corrected using


Table 3. Average iron isotopic compositions of geological RMs measured

during Jan. 2021 and Sept. 2021

RMs References 56FeIRMM-

014 2 SD 2 SE# N

IRMM-014,

IRMM This study 0.002 0.013 0.014 20

NIST3126a, NIST

This study 0.367 0.011 0.014 30

Zhu et al. (2018) 31 0.365 0.047 45

JP-1, Peridotite,

GSJ

This study 0.019 0.018 0.013 15

Zhu et al. (2018) 31 0.018 0.022 9

He et al. (2015) 23 0.006 0.011 3

BHVO-2, Basalt,

USGS

This study 0.109 0.017 0.014 30

Chen et al. (2017) 26 0.098 0.045 7

He et al. (2015) 23 0.112 0.021 27

Craddock and

Dauphas (2011) 20 0.114 0.035 12

Millet et al. (2012)

30 0.128 0.019 5

W-2a, Diabase,

USGS

This study 0.049 0.018 0.015 17

He et al. (2015) 23 0.053 0.025 8

Craddock and

Dauphas (2011) $, 20 0.056 0.051 6

GSP-2,

Granodiorite,

USGS

This study 0.155 0.018 0.017 14

Chen et al. (2017) 26 0.170 0.048 5

He et al. (2015) 23 0.154 0.012 11

Craddock and

Dauphas (2011) 20 0.159 0.038 6

Millet et al. (2012)

$, 30 0.153 0.023 1

COQ-1, Carbon,

USGS

This study -0.066 0.022 0.014 20

He et al. (2015) 23 -0.065 0.028 6

Craddock and

Dauphas (2011) 20 -0.115 0.020 3

Notes: #: 2 SE is the mean of internal errors (95% CI) on 56Fe for

individual measurements. $ denotes the usage of old batch of standards

(i.e., W-2 and GSP-1) in the literature.

empirical ratios 54Cr/53Cr ~ 0.2571 and 60Ni/58Ni ~ 0.4088 23.

Doping experiments validated this empirical correction method

when both Cr/Fe and Ni/Fe were < 1×10-4 (Fig. 6e and 6f). It is

noted that Cr and Ni can be effectively removed by our

purification procedure, e.g., eluted Fe from BHVO-2 having Cr/Fe

< 1.8×10-5 and Ni/Fe < 3.7×10-7, after single chromatographic run,

while this was routinely duplicated. Therefore, the isobaric

interference of 54Cr+ and 58Ni+ was negligible for the purified

samples in this study.

Whole procedure precision and accuracy. Five well-

characterized geological reference materials (JP-1, BHVO-2, W-

2a, GSP-2, and COQ-1) were repeatedly measured over nine

months, and the corresponding results are reported in Table S3 and

summarized in Table 3. Two types of column procedures were

used independently, and the sample loading varied from 10 μg to

1000 μg during this study. As shown in Fig. 7, no systematic bias

was observed when either the sample loading or chromatography

was adopted (Table S3). Runs with spiking prior to

chromatography and isotopic analyses also yielded consistently

deduced δ56Fe values, indicating insignificant artifacts from the

chromatography. Accordingly, all the raw data were considered

equally. JP-1, BHVO-2, W-2a, GSP-2, and COQ-1 yielded

average 56Fe values of 0.019 ± 0.018‰ (2 SD, N = 15), 0.109 ±

0.017‰ (2 SD, N = 30), 0.049 ± 0.018‰ (2 SD, N = 17), 0.155 ±

0.018‰ (2 SD, N = 14), and -0.066 ± 0.022‰ (2 SD, N = 20),

respectively, in agreement with the results previously reported

within the quoted uncertainties6,20,30,31 (Table 3). These results

demonstrated an external 2 SD precision and accuracy, on average,

of better than 0.02‰ for 56Fe.

Merits and pitfalls of the double spike versus those of other

techniques. To date, high-precision Fe isotopic analyses can be

routinely achieved by either SSB,23,25 SSB with element doping,31

or SSB with double spiking (30, and this study). SSB plus double

spiking is the only technique that can routinely measure Fe

isotopic data with a long-term 2 SD precision of 0.02‰ (30, and

this study), while the best precision reported for SSB with or

without element doping is approximately 0.03‰.23,25,31 Multiple

analyses are generally required for both SSB (with or without

element doping) (nine times in the cases of23,25,31) and double spike

techniques (four times in the current case), indicating an

improvement in precision at the cost of efficiency. Since the

double spike technique can accurately correct the mass bias

without drift in the deduced δ56Fe, bracketing standards are

measured for every five samples in the current study, instead of

every sample, as in the case of SSB,23,25 or every two or three

samples in the case of SSB with element doping.31 Accordingly,

the procedures reported herein also double or triple the

measurement efficiency compared to that for SSB with or without

element doping. Millet et al.30 reported a precision of 0.02‰ for

single-time measurement that consists of three blocks, each of

which contains 120 s background measurements, peak centering,

and 40 5 s long integrations. In addition, to account for the

occasional burst of argide interferences, each measurement on

unknown samples was bracketing by standard measurements.30

Therefore, the overall measurement efficiency of this study is

comparable to that reported by Millet et al.30

When compared with SSB and SSB with element doping

techniques, special care should be taken about isobaric

interferences30 and the trace isotope, 58Fe, which is introduced into

the data deduction of the double spike technique (29 and this study).

Incomplete discrimination of Fe from argide interferences, which

may burst during measurements, can cause substantial artifacts.30

Improper correction of trace isobaric Ni or significant Co

contamination may cause problems in the precision and accuracy

of Fe isotope analyses (29; and this study). In addition, the double

spike data deduction assumes that Fe isotope fractionation in

natural samples follows a certain fractionation law (e.g., the


Fig. 7

Iron isotopic compositions of five geological reference materials (JP-1, BHVO-2, W-2a, GSP-2 and COQ-1). RV from He et al.,23

calculated using

data compiled from 10 independent laboratories, are shown for a comparison. For raw data, refer to Table S3.

exponential fractionation law),33,38 meaning that any information

about either equilibrium versus kinetic isotope fractionation39 or

mass-independent fractionation40 will be overlooked. Lastly, this

study adopted measurements of high ion intensities to suppress

isobaric interferences, with the 56Fe signal usually >70 V. Damage

to the Faraday cups was not observed during this study, but its

relevant risk cannot be ruled out.

CONCLUSION

Herein, we report an improved procedure for Fe purification and

high-precision isotopic analyses using MC-ICPMS with a 57Fe-

58Fe double spike. In our new column procedure, less resin and

acid were consumed compared to previously reported methods,

thus providing a direct, efficient, and rapid method for Fe

purification. The trace Ni correction problem was suppressed by

increasing the total Fe signal to ≥ 120 V to maintain a high signal-

to-noise ratio. Based on the monitoring of pure Fe standard

solutions (IRMM-014 and NIST3126a) and geological reference

materials (JP-1, BHVO-2, W-2a, GSP-2, and COQ-1) over nine

months, δ56Fe could be routinely measured with a long-term

precision of better than 0.02‰, provided the measurements were

performed in quadruplicate. High-precision iron isotopic analyses

will promote a better understanding of minor but important isotope

fractionation among high-temperature geological samples.4,15


ASSOCIATED CONTENT



AUTHOR INFORMATION

Yong-Sheng He is a Professor at the

Institute of Earth Sciences, China

University of Geosciences, Beijing

(CUGB), leading a group focusing on Fe,

Ca, and Mg isotope geochemistry. He

received his B.S. (2005) and Ph.D. (2011)

degrees in geochemistry from the

University of Science and Technology of

China. After completing his Ph.D., he

came to CUGB for post-doctoral research

and joined the Isotope Geochemistry Lab

as a faculty in 2013. His main interest was petrogenesis of adakitic rocks

and their implication on evolution of orogenic crust. He currently focuses

on methodology developments on metal stable isotope geochemistry and

its application in tracing key geological and planetary processes, e.g., deep

carbon and oxygen cycles, changes in paleo-environment, and the

formation and evolution of the Moon. He has published over 50 peer-

reviewed scientific papers in ISI-indexed journals.


* Y. S. He


Notes


ACKNOWLEDGMENTS

The efficient editorial efforts by Prof. Wei Guo and constructive

comments from two anonymous reviewers are greatly appreciated.

We thank Dr. Shitou Wu and Dr. Hao Wang for their helps in

ICPMS measurements on eluates. This work is supported by the

National Key R&D Program of China (2019YFA0708403), the

National Natural Science Foundation of China (42122019), the

Fundamental Research Funds for the Central Universities (3-7-5-

2019-07), the 111 Project of the Ministry of Science and

Technology, China (BP0719021) and State Key Lab of Geological

Processes and Mineral Resources.

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Review of In-situ Online LIBS Detection in the Atmospheric

Environment

Qihang Zhang and Yuzhu Liu*

Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Jiangsu Collaborative Innovation Center on Atmospheric Environment and

Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China

Received: June 21, 2021; Revised: August 24, 2021; Accepted: August 25, 2021; Available online: September 11, 2021.

DOI: 10.46770/AS.2021.609

ABSTRACT: The aim of this review is to provide a brief introduction to recent research advances in in-situ online detection

of atmospheric pollutants based on laser-induced breakdown spectroscopy (LIBS) under atmospheric environments. Atmospheric

pollution has drawn much public attention, and there is increasing demand for

rapid and accurate evaluation of atmospheric environments. LIBS has the

advantages of in-situ online detection, simultaneous multi-element analysis, and

noncontact measurement, making it a highly competitive analytical technique in

the field of environmental monitoring. In terms of the different target samples,

some typical research cases, including atmospheric particulate matter,

atmospheric pollution sources, halogens in VOCs, atmospheric sulfur, and stable

isotope abundance, are presented to illustrate the current development and

problems of LIBS detection in this field.

INTRODUCTION

In recent decades, there has been ever-increasing demand for the

rapid detection of atmospheric pollutants, including volatile

organic compounds (VOCs), heavy metals, NOx, and other

hazardous substances, as the global atmospheric environment has

worsened owing to natural changes and anthropogenic activities in

many countries, and thus there has been ever-increasing demand

for the rapid detection of these atmospheric pollutants.1 The

presence of these atmospheric pollutants has caused serious

atmospheric environmental problems such as haze, increased size

of the ozone hole, and photochemical smog, and it has become a

serious threat to the global ecological environment and human

health,2,3 a problem that is gaining the attention of world

governments. To solve these environmental problems or reduce

their negative effects on the environment and human health, it is

essential to first determine the composition of the atmosphere in a

targeted area and then take specific measures after that. However,

the types and concentrations of the components in the atmosphere

vary from hour to hour with air flow. Hence, detection must be

accomplished in a very short time to ensure the validity of further

analysis and the timeliness of the measurement results, which is a

great challenge for current detection methods.

Laser-induced breakdown spectroscopy (LIBS) is a well-

known atomic emission spectroscopy (AES) technique. In LIBS

measurements, the target sample is ablated to a high-temperature

plasma by a high-intensity pulse laser, and then the atomic, ionic,

and molecular spectra containing the compositional information

emitted from the plasma are qualitatively and quantitatively

analyzed to determine all the components and their concentrations

in the sample.4 LIBS has attracted significant attention since it was

first reported at the X Colloquium Spectroscopicum Internationale

by Brech and Cross in 1962.5 Shortly thereafter, Runge et al.

established a calibration curve for quantitative analysis of target

elements based on the intensity of the characteristic line and the

plasma radiation mechanism,6 demonstrating that LIBS could be

a useful tool for spectrochemical analysis. Over the past several

decades, much scientific work covering a wide range of subjects

has been carried out by research groups worldwide,7 and the basic

theory of LIBS has gradually matured. Now, it has a wide variety

of applications in many fields, such as in material sorting,8-12 coal

analysis,13-16 food testing,17-19 medical testing,20-22 environmental

monitoring,23-26 ocean exploration,27-30 and space exploration.31-33


Unlike current chemical analytical methods, LIBS is a novel

optical analytical method with many distinguishing features, such

as rapid and precise response, high sensitivity, small sample loss,

and simultaneous multi-element analysis, and it is pollution-

free.34,35 These unique advantages make the LIBS technique an

effective method for compositional analysis. Moreover, detection

based on the LIBS technique requires no sample preparation, and

it can be performed on gaseous, liquid, and solid samples under

atmospheric pressure,36-38 which gives LIBS detection a great

advantage over the current analytical techniques in terms of

environmental detection. The elemental information of the sample

can be determined according to the obtained emission spectra in a

short time, providing real-time detection. Therefore, it is

appropriate and promising to apply the LIBS technique to the in-

situ online detection of atmospheric environmental pollutants.

However, compared with its application for the detection of liquid

and solid samples, there are few publications on the detection of

gaseous samples, especially in-situ detection under atmospheric

pressure, because the low density of gas samples makes it difficult

to observe the characteristic lines of the target element in the

emission spectra. With the development of laser techniques and

the theory of light-matter interaction, some novel LIBS techniques,

such as femtosecond LIBS and dual-pulse LIBS,39-42 have been

applied to improve the intensity and stability of the spectral signal.

More and more studies are currently being carried out to improve

the performance of the LIBS detection of gas, and the LIBS

technique is playing an increasingly important role in the detection

of atmospheric environmental pollution.

Research advances in LIBS applications in the field of

environmental monitoring have been widely reported in many

other reviews.43-46 This paper mainly introduces the development

of atmospheric environment detection based on the LIBS

technique in recent years. To adequately demonstrate the scientific

research development of LIBS detection in an atmospheric

environment, several typical cases are discussed to illustrate the

current development and future trends in this field.

EXPERIMENTAL

A diagram of a typical LIBS system used for gas detection is

shown in Fig. 1. The pulse laser is the core device of this system,

and it plays a key role in the ablation of the target sample. As

shown in Fig. 1, a nanosecond Nd:YAG laser system is used as

the excitation laser, which is operated at a fundamental wavelength

of 1064 nm. Three dielectric lenses and a plano-convex lens are

used to focus the laser pulse onto the target sample. An air pump

is used to control the flow direction of the gas sample and ensure

that the focus point is filled with the gas sample. The emission

spectra emitted from the plasma are recorded using a spectrometer.

Moreover, because bremsstrahlung caused by the collision of

electrons in the plasma interferes with the atomic and molecular

Fig. 1 Schematic diagram of the LIBS experimental apparatus for detection

of gaseous sample.

emission, which contains the compositional information of the

target sample, a digital delay is set between the spectrometer and

laser system to control the integral delay time of each LIBS

measurement and avoid the effect of background noise.

CASE STUDIES

Some studies have been conducted on the detection of

atmospheric environments based on LIBS detection, most of

which can be found in other reviews.47,48 In this review, some

typical cases carried out in the last few years are carefully analyzed

to illustrate the current development and problems in this field.

Atmospheric particulate matter analysis. Air quality is closely

related to health. However, air quality has steadily declined

because of the use of fossil fuels, and thus the air in many cities

has reached a serious level of pollution. The main pollutant in most

cities is floating particulates, which is the main reason for the

formation of haze in the air. A variety of heavy metal elements

exist in these atmospheric particulates, such as lead, mercury,

chromium, arsenic, and cadmium.49 Once these heavy metal

element particulates enter the human body through breathing, they

do great harm to bodily health and can lead to diseases of the

respiratory system, cardiovascular system, reproductive system,

etc.50 More seriously, these heavy metal pollutants are mostly

nondegradable, and they could exist in the bodies of animals and

plants for a long time, traveling up the food chain and causing

more serious damage to the human body. Hence, the detection of

heavy metal elements in atmospheric particulates based on the

LIBS technique is worth studying.

It is difficult to directly perform LIBS measurements of

particulate matter (PM) without any pretreatment because of the

low density of PM samples in air. Many different preconcentration

methods have been used to increase the hit efficiency of the laser

pulse. The filter-based technique is a simple but useful tool to

focus PM onto a membrane or substrate. Kwak et al. developed a

LIBS experimental system with a particle collection substrate

stage that could automatically move in a specific time interval.51

The detection results of the PM in every time interval were


averaged to obtain time-resolved compositional information about

the PM. Zhang et al. carried out research on the qualitative and

quantitative detection of heavy metal elements in PM samples.52

The PM was collected using an air pump and focused onto a quartz

fiber filter (QFF) membrane. The LIBS signal was greatly

improved after this preconcentration. The recorded spectra

showed clear spectral lines of Na, Al, Si, Cu, Mg, and Fe in the

emission spectra. In addition, the authors established a calibration

curve of Pb using the internal standard method. However, the

filter-based technique required a lot of time to extract the PM from

the air, and thus the LIBS measurement could not be performed in

real time or accomplished in a short time. Chances are that some

pollutants that only existed for a short time were not observed in

the spectra after the preconcentration process. In addition, the

detection accuracy of the LIBS technique is greatly influenced by

the matrix effect caused by the use of a substrate or filter

membrane. To solve these problems, some new methods have

been proposed to rapidly extract PM. Diwakar et al. designed an

electrostatic aerosol collection system to collect particles for LIBS

measurements.53 The aerosol particles were charged using a

corona needle to which a 5-kV positive potential was applied and

then collected by the flat tip of a microneedle electrode. The

particle capture efficiency was as high as 99% over a wide range

of particle sizes, 30–600 nm, using this aerosol collection system.

The elements Cd, Cr, Cu, Mn, Na, and Ti were simultaneously

measured using LIBS, and a calibration curve was constructed by

plotting the peak-to-base (P/B) ratio as a function of mass

deposited on the collection needle tip. The limits of detection

(LODs) of these elements were calculated to be 5.03, 0.035, 0.138,

0.155, 0.018 and 0.44 ng, respectively, at a flow rate of 1.5 L min-1

with sampling times of 5 min. Similarly, Park et al. developed an

aerosol-focusing LIBS system with sheath air focusing to

determine the elemental composition of fine and ultrafine metal

aerosol particles.54 Maeng et al. established a novel LIBS system

with timed ablation and applied it to the determination of the

elemental composition of individual airborne particles.55 Saari et

al. studied the identification of fungal spores and bacteria particles

in biological aerosols using an electrodynamic balance-assisted

LIB electrodynamic balance (EDB) chamber.56 The above works

demonstrate that preconcentration methods can improve the

detection efficiency of the LIBS technique. However, even

combined with these methods, the LIBS technique cannot realize

the in-situ detection of PM.

Some researchers have attempted to improve conventional

LIBS systems to enhance the detection capability of particles in

specific environments. Xiong et al. studied the in-situ detection of

TiO2 nanoparticle aerosols based on low-intensity phase-selective

LIBS (PS-LIBS).57 In novel, low-intensity LIBS measurements,

the laser fluence as the excitation source is between the breakdown

thresholds of the gas and particle phases. Hence, only the solid

phase nanoparticles would break down, forming plasmas, without

any surrounding gas-phase breakdown. PS-LIBS emissions were

also enhanced with secondary resonant excitation by matching the

excitation laser wavelength with an atomic transition line in the

formed plasma. The enhancement factor of this method can be up

to 220 times with advantageously selected lines. Heikkilä et al.

studied single-particle elemental analysis of airborne aerosols by

combining EDB trapping with the LIBS technique.58 A corona-

based aerosol charger, double-ring EDB trap were imposed in the

LIBS setup to increase the detection efficiency and reduce the

LOD.

Atmospheric pollution sources. Clean air is a prerequisite not

only for human life, but also for all life on the planet. However, a

large number of emission sources of various environmental

pollutants have seriously damaged the atmospheric environment

and cause both local pollution and wide-area pollution.59-61 It is

essential to understand the pollution source and the properties of

the pollutants before taking measures. Studying the rapid online

detection of these atmospheric pollutants is meaningful for

determining emission sources and helpful for choosing the

corresponding degradation methods according to the sources of

pollution.

Coal is the major fuel used for producing electricity in many

countries, and the emission of coal ash produced during the

combustion of coal in the plant causes severe haze and acid rain.62

Zhu et al. therefore carried out a study on the quantitative analysis

of Fe and the detection of multiple elements in coal ash via the

LIBS technique.63 Taking Al as the reference element, a calibration

curve of Fe concentration was established by plotting the relative

intensity of Fe lines versus the Fe concentration in the coal ash

based on the internal standard method. Then, the Fe concentration

of unknown coal ash could be determined according to the

calibration curve, and thus the sorting of coal ash could be rapidly

realized. In addition, X-ray fluorescence spectrometry (XRF) was

used to test the accuracy of the quantitative analysis based on LIBS

measurements. Yin et al. studied the quantitative analysis of Pb in

coal ash and the rapid detection of various metal elements,64

nonmetal oxides, and salts based on the LIBS technique, which is

of great importance in coal utilization.

As a result of constant media attention and efforts made by

government departments, many scientists have joined the study of

wide-area pollution, and much research has been carried out.

However, local pollution, which impacts people’s daily lives, has

seldom been stressed. The online detection of local air pollution is

an important part of environmental protection and is beneficial to

the health of people who work or live indoors.

Mosquito-repelling incense is often used to reduce biting in

summer, where a large amount of smoke is emitted from the

burning incense. It is noted that most brands of incense include Mn

for the purpose of repelling mosquitoes. Although Mn is an

essential trace element in the human body, excessive intake of Mn

can cause neurological disorders in the brain and irreversible

damage to the central nervous system.65,66 Qu et al. studied the


online real-time detection of smoke by using a mosquito-repelling

incense as a representative example of local air pollution.67 Metal

elements, including Mg, Fe, Ca, and Ti, as well as some toxic

elements, such as Sr, Cr, and Cd, were simultaneously observed in

the spectra. Furthermore, there was also a characteristic spectral

line of F I (685.6 nm) in the spectra, which proved the existence of

meperfluthrin (C17H16Cl2F4O3) in the mosquito-repelling smoke.

Moreover, the real-time detection of the ambient air via the LIBS

technique and its effect on human breathing were also discussed

to enlarge the scope of the developed LIBS system in the local

environment.

Incense is widely used by people who practice Buddhism and

Taoism in some developing countries or regions. Large quantities

of inferior incense containing a variety of poisonous substances

are extensively produced and sold by manufacturers because of its

low cost. Hence, incense smoke causes local air pollution in indoor

environments, especially in temples. Long-term exposure to these

poisonous substances from burning incense can seriously damage

the skin, respiratory system, and nervous system.68-69 Yin et al.

studied the online and offline detection of incense smoke via LIBS.

70 Mass spectrometry was also used to determine and analyze the

elemental composition of incense smoke using a home-built

single-particle aerosol mass spectrometer (SPAMS). Through

measurements based on the combination of LIBS and SPAMS, the

authors not only obtained elemental information but also identified

the particle size and chemical composition of a single particle with

extremely high temporal and spatial resolution.

There are many smokers worldwide, and it is generally known

that cigarette smoking is harmful to one’s health owing to the toxic

ingredients in cigarettes. Moreover, cigarette smoking, which

contains nicotine, tar, Pb, As, and other noxious ingredients, also

has pernicious effects on nonsmokers.71 Many studies have

demonstrated that second-hand smoke can cause a wide range of

diseases, such as lung cancer and heart attacks.72-74 Nonsmokers

exposed to second-hand smoke have shown increased risk of heart

disease by 25%–30% and lung cancer risk by 20%–30%.75 Hence,

it is important to accurately evaluate the local pollution caused by

cigarette smoke in public areas. Zhang et al. carried out research

on the in-situ detection of cigarette smoke in public areas.76 By

using a strong pulse Nd:YAG laser (260 mJ/pulse) as the excitation

source, LIBS was applied to the sidestream smoke of a burning

cigarette. The characteristic lines of most metal elements,

including Mg, Ca, Sr, Na, and K, were observed in the obtained

spectra, as shown in Fig. 2. Moreover, the matrix effect of the

smoking detection was also discussed by comparing the LIBS

spectra of cigarette smoke and cigarette ash.

Pollutants in the air, especially over a wide area, mostly

originate from a variety of different sources.77-79 For example, the

haze that occurs frequently in cities is a mixture of construction

Fig. 2 The LIBS spectra of air and the smoke from a burning cigarette. (a) 240–340 nm, (b) 400–450 nm, (c) 500–600 nm, and (d) 600–780 nm (left y-axis

for smoke off, right y-axis for smoke on).76


dust, vehicle exhaust, factory emissions, and power plant fumes.80,81

Therefore, studies on the classification and source tracing of

atmospheric pollutants are necessary for detection in complex

environments. The classification of samples is mainly dependent

on the analysis of characteristic lines in the spectra, which could

reflect the compositional information of the different pollutants.

However, it is common that air pollution is caused by two or more

atmospheric pollutant sources, and it is difficult to distinguish

them by simple elemental analysis based on the LIBS technique.

However, by combining LIBS with machine learning algorithms,

it is possible to determine the pollutant sources based on their

characteristics. Typically, the spectral data of characteristic lines

are extracted from the spectra and used as a sample set to establish

a suitable classification model. Currently, the most common

algorithms used in LIBS analysis are principal component analysis

(PCA),82-85 linear discriminant analysis (LDA),86-88 support vector

machine (SVM),89-91 random forest (RF),92-94 and artificial neural

networks (ANN).95-98 For the classification of pollutants, these

methods work equally well. Lu et al. applied PCA,99 SVM, and

back-propagation artificial neural networks (BP-ANN) for real-

time in-situ detection and classification of some typical pollutants.

Classification models were established according to the principal

components extracted from the characteristic lines. The test

experiments showed that the recognition rates of the models based

on the SVM and BP-ANN algorithms were 90.00% and 93.33%,

respectively. The combination of these algorithms and LIBS

significantly improved the recognition accuracy of the

classification established based on LIBS analysis.

Isotopic detection. Nuclides of the same element with the same

proton number but different neutron numbers are called isotopes,

and many elements have more than two types of isotopes in nature.

It is noted that stable isotopes obtained from different sources are

likely to have different isotope ratios.100 Based on this principle,

there have been many studies on contamination tracing via the

isotopic technique.101-105 Currently, the detection of stable isotopes

is based on mass spectrometry. However, mass spectrometry

generally requires complicated sample pretreatment procedures,

and the measurement must be performed in a vacuum

environment. Moreover, it is difficult to accurately distinguish the

peaks of different elements or fragments in the mass spectrum if

there are more than two possibilities for the identification results.

The LIBS technique is promising as a supplement or alternative

for the detection and analysis of mass spectrometry owing to its

merits mentioned above.

Yin and Zhang et al. carried out research on the quantitative

detection of three principal Pb isotopes (206Pb,207Pb, and 208Pb) in

smoke from a burning cigarette and incense based on the

combination of LIBS and SPAMS.70,76 The concentration of Pb

and the abundance ratio of isotopes were analyzed according to the

obtained LIBS spectrum and mass spectrum, as shown in Fig. 3.

Such studies have successfully proved that the assistance of LIBS

measurements could greatly improve the identification accuracy

Fig. 3 (a) The variations of the spectral lines with the different

concentrations of Pb. (b) The mass spectrum of Pb isotope and abundance

in cigarette smoke.76

of the peaks in the mass spectrum. However, measurements based

on the combination of these two methods require too much time,

and the drawbacks, such as complicated sample pretreatment and

rigorous experimental conditions, still restrict the application of

these techniques in the field of isotopic detection.

In recent years, isotopic analysis based on the LIBS technique,

termed laser ablation molecular isotope spectrometry (LAMIS),

has drawn significant attention.106,107 In LAMIS, both the atomic

lines and molecular emission bands are recorded using a

spectrometer. The characteristic atomic lines provide elemental

information, whereas the molecular bands can be used for

compositional analysis. Here, we take the carbon-nitrogen free

radical emission band as an example to illustrate the formation of

diatomic molecules in plasma and isotopic analysis based on the

LAMIS technique. The temperature in the outer layer of the

plasma is lower than the temperature in the center, and the

molecular fragments can be directly released from the sample and

combined with the molecular fragments in ambient

atmosphere.108-110 The formation of CN radicals can be explained

by the reaction of carbon in the sample with nitrogen in the air,111

as shown in Fig. 4. Carbon isotopes have two principal mass

numbers, 12C and 13C, and the variation in the mass number of

carbon atoms leads to a change of the reduced mass of the CN

molecule. Then, the emission band of CN shifts a certain distance

because of the isotopic effect. Hence, there is a wavelength


Fig. 4 The formation mechanism of CN radical. In the outer layer of plasma,

the carbon in the sample combined with the nitrogen in the air.

difference between the emission bands of 12CN and 13CN, and it is

easy to distinguish the carbon isotopes using the LAMIS technique.

Furthermore, the intensities of the molecular bands emitted from

different molecules have a significant linear correlation with the

abundance of each isotope, which is the foundation of the

quantitative analysis of isotopes.

Zhang et al. carried out research on the online detection of

carbon isotopes and studies the atmospheric carbon cycle by

LIBS.112 The carbon element in the PM generated from burning

coal, wood, and paper samples was determined first. Then,

isotopic shifts of the CN molecular emission bands in the LIBS

spectrum of gaseous CO2 were experimentally measured, as

shown in Fig. 5. Theoretical calculations based on density

functional theory were performed to obtain the energy differences

of the isotopic shifts of the CN molecular band, as well as those of

many other molecules formed by different types of isotopes.

Recent research has shown that LIBS is a useful tool for the stable

isotope analysis of several elements.

However, only a few types of molecular bands have been

reported, and most of the free radicals have never been observed

in LIBS measurements. Moreover, the isotopic effect on the

molecular band was highly related to the mass number of the

element. The larger the mass number, the smaller the isotopic shift

of the emission band.106 Hence, it is difficult to accomplish the

isotopic analysis of the heavy metal element in atmospheric

pollutants merely by LIBS. Other techniques, such as mass

spectrometry, must be applied in combination with LIBS.

Detection of halogens in VOCs. VOCs are the major atmospheric

pollutants. VOCs are the precursors to the formation of ozone and

secondary organic aerosols, such as fine PM,113 which can cause

serious atmospheric environmental problems. In addition, some

specific VOCs that contain bromine and chlorine atoms have long

been regarded as culprits in the destruction of the ozone layer.114-

115 Therefore, the rapid direct detection of VOCs in the

atmospheric environment is of great significance for

environmental protection.

Most of the current studies on the LIBS technique have focused

Fig. 5 The isotopic shift of CN molecular emission bands. (a) The isotopic

shift is almost 0 when the vibrational quantum number Δν equals 0. (b) The

emission band of the transition B2 Σ+ (ν=1)→X2 Σ+ (ν=0)(∆ν = +1) redshifts

approximately 0.6 nm. (c) The emission band of the transition B2 Σ+ (ν=0)

→X2 Σ+ (ν=1)(∆ν = -1) blueshifts approximately 0.8 nm.112

on the qualitative and quantitative analysis of metal elements in

target samples. In contrast, there are few studies on the LIBS

analysis of nonmetallic elements, such as F, Cl, Br, I, and S.

Compared with metallic elements, the LIBS detection of

nonmetallic elements, especially halogens and sulfur, presents

particular difficulty for LIBS analysis because of the high

ionization energy of such elements and the relative weakness of

their spectral lines,116 which is unfavorable for the appearance of

the characteristic emission lines in the measurements. Furthermore,

most of the spectral lines of such elements lie in the vacuum

ultraviolet (VUV) (110–190 nm) spectral range,117 and the

requirement for the collection of emission spectra is so robust that

it hinders the development of LIBS applications in environmental

detection.


Fig. 6 Schematic of the LIBS experimental setup designed for online

detection of VOCs. The home-made sample cell, which includes a tube

with a valve, was used to store the gaseous sample, switch the gas flow

on/off, and control the direction of gas flow.125

To address this challenging issue, there has been significant effort

to enhance the ability of LIBS to detect nonmetallic elements in

the last several years. The experimental environment has a

significant effect on the signal strength and quality of the spectral

lines of halogens. Asimellis et al. performed LIBS detection under

a controlled inert gas ambient atmosphere so that F and Cl could

be detected in the upper visible and near-infrared (NIR)

wavelengths (650–850 nm).118 The signal-to-noise (S/N) ratio of

the spectral line was further enhanced under optimal He pressure

in the range of 60 mbar. Zhang et al. detected iodine in buffer gases

of N2 and air using nanosecond and picosecond breakdowns of

CH3I at reduced pressure.119 The results showed that the use of

buffer gases reduced the quenching rate of excited iodine in air. It

was demonstrated that the interference of the continuum emission

from the plasma decreased as the pressure decreased. However, it

is extremely difficult to use buffer gases or create a low-pressure

environment in open air. These methods are far from being

recognized as realistically suitable for the detection of halogen

traces in the atmosphere. These experiments can only be applied

to the detection of halogens in solid or liquid samples.

In addition to optimizing the environmental parameters,

determining the concentration of some nonmetallic elements by

analyzing the related molecular emission in the LIBS spectra is

also an effective solution in some specific cases. A recent review

summarized advances in halogen detection by molecular

emission.120 Gaft et al. studied the elemental analysis of halogens

in minerals,121-122 including fluorine, chlorine, bromine, and iodine,

using molecular emission. The authors developed corresponding

detection methods for these halogens by combining them with

alkali-earths and other elements to form molecules whose spectra

could be easily identified, and then the analytical methods were

successfully applied to real conditions. Thus, the molecular spectra

enabled detection under ambient conditions with much higher

sensitivity than that of F I and Cl I atomic lines. Similarly, Llamas

et al. studied fluorine quantification in calcium-free samples

through the analysis of CaF molecular bands.116 Tang et al.

investigated the determination of fluorine in copper ore using

LIBS assisted by the SrF molecular emission band.123 To improve

the sensitivity of detection, Nagli et al. proposed combining LIBS

with molecular laser-induced fluorescence (MLIF).124 Comparing

the ordinary LIBS method with LIBS-MLIF demonstrated that the

combination of LIBS and MLIF significantly improved the

detection sensitivity by approximately 10 times.

The LIBS detection of nonmetallic elements assisted by the

above methods is realized at the cost of speed and generalizability.

Furthermore, in terms of atmospheric VOCs, it is unfeasible to

apply such complicated treatments in an open environment. To

realize the direct detection of halogens in air, Zhang et al. designed

a novel LIBS experimental system coupled with SPAMS, as

shown in Fig. 6.125 Taking Halon 2402, Freon R11, and

iodomethane gas as target samples, the online direct detection of F,

Cl, Br, and I elements was carried out under atmospheric pressure

using an extremely strong pulse laser of 200 mJ as the excitation

source in the LIBS system. The LIBS and SPAMS spectra of the

Halon 2402 gas sample were captured, as shown in Fig. 7.

The atomic lines (Br I) and ionic lines (Br II) of bromine were

simultaneously observed in the LIBS spectrum. The different

isotopes of bromine and chlorine could be clearly distinguished at

the same time based on the SPAMS analysis. It was thus

demonstrated that the LIBS-SPAMS technique can provide

elemental and isotopic information of halogen atoms in

atmospheric VOCs. Furthermore, this method enables remote

LIBS detection to meet the actual needs of halogen detection in

most cases, especially in the atmosphere.

Detection of atmospheric sulfur. The LIBS detection of sulfur

has the same problem as halogen detection because the strong

sulfur lines in the NIST database lie within the VUV or NIR

spectral range,126 which poses great difficulty in terms of capturing

the emission spectra in open air. Because the spectral emission in

the VUV band rapidly decays in air, studies on the detection of

sulfur are carried out in a vacuum environment. Ytsma et al. used

the LIBS spectra of standard geological samples to quantify sulfur

and other main elements under a variety of atmospheric

conditions,127 including vacuum, Mars, and Earth atmospheres.

The authors established a quantification model based on

multivariate analysis; however, the experimental results showed

that there was poor linearity between the actual and predicted

concentrations, and thus the method was impractical for

determining the sulfur concentration. Kubitza et al. studied the

detection of sulfur in lunar analogs at concentrations ranging from

0.5 to 4.0 at% by conducting LIBS experiments in a high-vacuum

(10-3 Pa) environment.128 Zhang et al. proposed a new analytical

method for sulfur determination in sulfur-bearing powder samples


Fig. 7 The characteristic peaks of bromine in the LIBS-SPAMS spectra of Halon 2402. (a) The atomic lines (Br I) and ionic lines (Br II) of bromine in the

LIBS spectrum, and (b) the mass peaks of bromine at m/z 79 and 81 in the SPAMS spectrum.125

Fig. 8 The ionic lines of sulfur in the LIBS spectrum of a DMS gas sample

at a laser energy of 1000 mJ/pulse.130

using resonance Raman scattering combined with the LIBS

technique.129 Compared to the calibration curve achieved by sulfur

atomic emission, the linear coefficient (R2) and detection limit

were significantly better.

However, this method is not suitable for LIBS detection of

atmospheric sulfur because it is difficult to create a vacuum

environment for detection in air. Sulfur is widely distributed in

coal, petroleum, and other fossil fuels, and massive sulfide

pollutants are emitted into the atmosphere during the combustion

of these fuels and derivatives in factories. The detection of these

atmospheric pollutants in air is of great significance for

environmental protection. To apply the LIBS technique to the in-

situ detection of atmospheric sulfur, Zhang et al. established a

novel LIBS experimental apparatus specializing in the detection

of gas samples and applied it to the online direct detection of sulfur

in the gas phase under atmospheric pressure.130 The characteristic

ionic lines of sulfur were also clearly observed in the LIBS

spectrum of a dimethyl sulfide (DMS) gas sample, as shown in Fig.

8. In addition, a quantitative calibration model of sulfur was

established by fitting the intensity of the line and concentration of

sulfur compounds, and the LOD of sulfur by LIBS was calculated

to be 46 mg/L. The experimental results also demonstrated the

potential of LIBS detection of sulfur based on the analysis of sulfur

ionic lines in the visible range.

However, compared to the other techniques that have been

applied to the detection of sulfur, the LOD in LIBS measurements

is relatively high and insufficient for the actual environmental

quality control. The high LOD severely limits the application of

LIBS in the in-situ detection of atmospheric sulfur. Hence, it is

essential and meaningful to focus subsequent studies of this

subject on the enhancement of the LIBS signal and decreasing the

LOD of sulfur.

CONCLUSIONS

LIBS is a novel optical analytical method that has attracted


significant attention in recent years. Because of its unique

advantages in terms of rapid and precise response, noncontact

measurement, and simultaneous multi-element analysis, the LIBS

technique has been expected to play an important role in the field

of environment detection or even replace conventional analytical

methods under some circumstances since its inception. However,

compared with the detection of solids, liquids, and aerosols,

progress in atmospheric detection by LIBS was disappointingly

slow in the early years. As some advanced laser techniques,

including femtosecond pulse lasers, have become available, and

the fundamental theory of light-matter interaction has been

improved, development in this field has significantly increased in

the last few decades.

In this review, a brief introduction to the in-situ online detection

of the atmospheric environmental pollutants using the LIBS

technique is presented, and we have highlighted recent advances

in the research on the LIBS detection of atmospheric particulate

matter, atmospheric pollution sources, and stable isotope

abundance.

Over the past 30 years, the experimental instruments, methods,

and analytical algorithms have been greatly improved, and LIBS

detection now stands at its highest level of functionality. Our

current research on the in-situ online detection of atmospheric

environmental pollutants is focused on using LIBS to detect

pollutants with extremely low concentrations and the analysis of

the molecular structure of the organic matter.

AUTHOR INFORMATION

Yuzhu Liu is a full professor and vice director

of Jiangsu Key Laboratory for Optoelectronic

Detection of Atmosphere and Ocean at

Nanjing University of Information Science

and Technology (NUIST). He received his

PhD in physics from Wuhan institute of

Physics and Mathematics, Chinese Academy

of Sciences (June 2011). After completing his

Ph.D., he came to Paul Scherrer Institute in

Switzerland for post-doctoral research. In September 2014, he returned to

China and joined NUIST for professorship. His research interests include

laser-induced breakdown spectroscopy, Raman spectroscopy, ultrafast

molecular dynamics, photoelectron imaging technique, and time-of-flight

mass spectrometry. He is currently working on instrumentation

developments and investigations for the project of online in situ detection

of elements or pollutions in the atmosphere via LIBS. He has published

over 100 peer-reviewed scientific papers in ISI-indexed journals.


*Y. Z. Liu


Notes


ACKNOWLEDGMENTS

This work was supported by National Key R&D Program of

China (2017YFC0212700), National Natural Science Foundation

of China (U1932149), Natural Science Foundation of Jiangsu

Province (BK20191395), Key Research and Development

Program of Anhui Province (202104i07020009), and

Postgraduate Research & Practice Innovation Program of Jiangsu

Province (KYCX21_0989).

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Electron Probe Microanalysis in Geosciences: Analytical Procedures

and Recent Advances

Shui-Yuan Yang,a,* Shao-Yong Jiang,a,b Qian Mao,c Zhen-Yu Chen,d Can Rao,e Xiao-Li Li,f Wan-Cai Li,g

Wen-Qiang Yang,h Peng-Li He,i and Xiang Lij

a State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, P. R. China

b School of Earth Resources and Collaborative Innovation Center for Scarce and Strategic Mineral Resources, China University of Geosciences, Wuhan

430074, P. R. China

c State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, P. R. China

d Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, P. R. China

e Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, P.R.

China

f MOE Key Laboratory of Orogenic Belts and Crustal Evolution, School of Earth and Space Sciences, Peking University, Beijing 100871, P. R. China

g CAS Key Laboratory of Crust-Mantle Materials and Environments, School of Earth and Space Sciences, University of Science and Technology of China,

Hefei 230026, P. R. China

h State Key Laboratory of Continental Dynamics, Department of Geology, Northwest University, Xi'an, 710069, P. R. China

i State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, P.R. China

j State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, P. R. China

Received: October 07, 2021; Revised: December 31, 2021; Accepted: January 03, 2022; Available online: January 09, 2022.

DOI: 10.46770/AS.2021.912

ABSTRACT: Electron probe microanalysis (EPMA) is an in-situ and non-destructive analytical technique with high spatial

resolution and an increasingly important analysis tool in materials science and geosciences. This study summarizes the principles

and functions of EPMA, and the problems and difficulties, along with the recent advances in quantitative analysis of EPMA. A

routine EPMA procedure includes preparing samples, setting analytical conditions, acquiring data, and evaluating results. Caution

is required in all steps to obtain high-quality analytical results. The problems and difficulties commonly encountered in EPMA are

discussed and the corresponding measures and solutions required to resolve them are proposed. Specific analytical methods are

suggested to make accurate analysis of some

specific minerals. We also summarized the

challenges and solutions in light element

analysis, trace element analysis, EPMA U-Th-Pb

total dating, combined analysis with

wavelength- and energy-dispersive X-ray

spectroscopy, submicron spatial resolution

analysis at low accelerating voltages, iron

oxidation state analysis, and standard reference

materials.

INTRODUCTION

Electron probe microanalysis (EPMA) is a modern technique

based on the physical mechanism of electron-stimulated X-ray

emission and X-ray wavelength-dispersive spectroscopy (WDS),

which Raymond Castaing developed as part of his Doctor of

Philosophy dissertation in Paris back to 1951.1 It is one of the

most useful microanalytical methods for precise, non-destructive,

and quantitative elemental analyses of solid materials, including


metal alloys, glasses, ceramics, and minerals, at micrometer scale.

The EPMA instrument can also be used for elemental line scan

and mapping along with image acquisition, such as backscattered

electron image (BSEI), secondary electron image (SEI) and

cathodoluminescence (CL) image. Therefore, EPMA is a widely

used technique with a significant impact on research in materials

science2 and geosciences.3

Previous studies2-8 provided some overviews on the principles,

functions, analytical procedures, and instrumental and

methodological developments of EPMA in geosciences. As the

most commonly applied analytical technique in geosciences, we

focused on recent advances in quantitative EPMA in geoscience

in this study and emphasized on the following: 1) precautions

that need to be considered when preparing samples and setting

experimental conditions and evaluation of data quality and 2)

challenges and solutions in EPMA, such as the analyses of light

and trace elements, U-Th-Pbtotal dating, combined WDS and

energy dispersive spectrometer (EDS) analysis, a higher spatial

resolution, iron oxidation state analysis, and standard reference

materials, aiming to further extend its applications in

geosciences.

PRINCIPLES AND FUNCTIONS

There are several major EPMA instrument manufacturers, such

as JEOL (Japan), Shimadzu (Japan), and CAMECA (France). An

EPMA instrument comprises several components, including

electron gun, electron lenses, sample stage, EDS, WDS, BSE

detector (Fig. 1a). From the area on the sample surface

bombarded by the electron beam, various signals, such as

backscattered electrons, secondary electrons, characteristic

X-rays, CL, transmitted electrons, and Auger electrons (Fig. 1b),

are generated, detected, and processed in the EPMA to produce

information on images (such as BSEI and SEI), chemical

compositions, and structures.6 When the incident electron energy

is greater than critical excitation energy of the element, the

electron in the inner electron layer of the orbit is removed,

creating an electron vacancy. Subsequently, the outer electrons of

the excited state in the high-energy shell are transferred to the

inner low-energy shell electron layer (Fig. 1c), and the

characteristic X-rays with specific energy are emitted. The

X-rays emitted are known as continuous X-rays when the

incident electrons are decelerated under the nuclear Coulomb

field without removing an electron.

The main functions of EPMA include imaging, quantitative

analysis, qualitative analysis, line scan, and elemental mapping.6

The types of images include BSEI and SEI, the former is a

function of the mean atomic number of the materials and is

mainly used to demonstrate the compositional differences,

whereas the later (SEI) is used to display the sample surface

Fig. 1 A schematic diagram of (a) basic components of an electron probe

microanalyzer (Modified after Llovet et al.2), (b) signals geted by electron

irradiation of the sample (Modified after JEOL10), and (c) the

characteristic X-ray emission (after Goldstein et al.7).

morphologies. Qualitative analysis involves identifying and

semi-quantifying most elements (theoretically from Be to U) in

the analyzed area or line. Line scan and mapping are essential in

acquiring concentration variation of the element of interest within

a line segment and a selected area in the sample, respectively.

Furthermore, mapping provides an intuitive image of the element

distribution within the area of interest. Accurate quantitative

analysis of elements is the most essential EPMA application in

geosciences, which is discussed in the subsequent section.

QUANTITATIVE ANALYSIS

Quantitative analysis by EPMA can be accomplished through

both EDS and WDS. Although EDS is comparatively faster, it

has a less energy resolution and a worse ability to detect elements

present in low concentrations when compared to WDS.2 Therefor,

we focused on WDS in this study. According to Moseley's law,9

wavelength (λ) of the elemental characteristic X-rays is related to

its atomic number (Z),7 as shown in equation (1):

𝜆 =𝐵

(𝑍−𝐶)2 (1)

where B and C are constants. Therefore, the wavelength of a

characteristic X-ray emitted from a sample can be used to

identify the elements present in the sample.

A WDS spectrometer utilizes a diffracting crystal to diffract the

characteristic X-rays generated from the electron beam and

sample interaction, which are subsequently detected by a gas

flow or sealed proportional X-ray counter. The X-ray source in

the sample, the diffracting crystal, and the proportional counter

defines a circle known as Rowland circle with a constant


Table 1. Diffracting crystals for the common elements

Manufacturer Crystal 2d(nm) Analysis elements

K L M

JEOL

LIF 0.4027

K~Rb Cd~U

LIFL K~Br Cd~Fr

LIFH Ca~Ga Sn~Au

PET

0.8742

Al~Mn Kr~Tb Yb~U

PETL Al~Cr Kr~Sm Yb~U PETH Si~Ti Rb~Ba Hf~U

TAP

2.5757

O~P Cr~Nb La~Au

TAPL O~Si Cr~Sr La~Re

TAPH F~Al Cr~Br La~Yb

Shimadzu

LiF 0.401 Ca~Ge Sn~Hg

PET 0.874 Si~Ti Kr~Ba Lu~U

ADP 1.064 Al~Ca As~Te Dy~U

RAP 2.612 O~Al Cr~Kr

CAMECA

TAP

2.576

F~P Mn~Mo La~Hg LTAP F~P Mn~Mo La~Hg

Extend TAP/

Extend LTAP O~P Cr~Mo La~Hg

PET 0.8762

Si~Mn Sr~Tb Ta~Am LPET Si~Mn Sr~Tb Ta~Am

LiF 0.4207

Sc~Rb Te~Am

LLiF Sc~Rb Te~Am

diameter (Fig. 2a). The diffracting crystals diffract the

characteristic X-rays when they satisfy Bragg's law (Fig. 2b;

equation 2):

nλ=2d sinθ (2)

where n, λ, d, and θ correspond to diffraction order (an integer),

wavelength (Å) of the X-rays, interplanar spacing (nm) of the

diffracting crystal, and diffraction angle of the X-rays,

respectively. The characteristic X-ray wavelength depends on the

element present in the sample, while the X-ray intensity is related

to the concentration of the element. With the movement of the

diffracting crystals and the proportional counter in a Rowland

circle, the X-ray diffraction angle is adjusted within a specific

range to diffract the characteristic X-rays with certain

wavelengths onto the X-ray counter. In addition, a diffracting

crystal can only diffract one characteristic X-ray line at a time. A

Fig. 2 A schematic diagram of (a) Rowland circle and (b) Bragg’s

diffraction law (after Reed6). R: radius of the Rowland circle; L: detection

position, which is the distance between the X-ray source and the

diffracting crystal; θ: diffraction angle at which the diffracted

characteristic X-rays are in phase (after Zhao et al.3).

diffracting crystal with an appropriate interplanar spacing need to

be selected for a specific characteristic X-ray line. Common

diffracting crystals used by major instrument manufacturers are

listed in Tables 1 and 2.

Quantitative analysis is based on a positive correlation

between concentration of element and intensity of the

corresponding characteristic X-ray. Given the concentration of a

specific element in a standard, Cstd, the concentration of the same

element in an unknown sample, Cunk, can be calculated using the

formula presented in equation (3):6

Cunk =Iunk

Istd× Cstd ×

ZAFunk

ZAFstd= k × Cstd ×

ZAFunk

ZAFstd (3)

where Iunk is the net intensity of the characteristic X-rays from the

unknown sample, Istd is the net intensity of the same

characteristic X-rays from the standard, and ZAFunk and ZAFstd

are the matrix correction factors of the unknown sample and

standard, respectively. The k refers to the ratio of the unknown

intensity to the standard intensity, known as k-ratio.

SAMPLE PREPARATION

Polishing. Sample preparation for EPMA includes cutting,

embedding, grinding, and polishing.2,3,6 A flat and well-polished

sample surface is required for EPMA. The quality of polishing of

a sample directly affects the accuracy of the analytical results. An

irregular geometry of the sample surface could strengthen or

reduce the collection of X-rays. Moreover, a poorly polished

sample surface makes it hard to focus, leading to the loss of the

characteristic X-ray intensity (Fig. 3) because the location where

an electron beam bombards the sample may be out-of-focus

during analysis.10 The surface irregularities such as scratches or


Fig. 3 The loss of characteristic X-ray intensity when the analytical

position is out-of-focus (shift from Rowland Circle) in Z direction (a) and

X direction (b) during analysis (after JEOL10).

Fig.

4 A schematic diagram displaying the additional absorption produced

by a pit on the sample surface (after Sweatman and Long8).

Table 2. Diffracting crystals for the light elements

Manufacturer Crystal 2d(nm) Analysis elements

JEOL

STE 10 B~O

LDE1 6 C~F

LDE2 10 B~O

LDEB 14.5 Be~B

LDE1L 6 C~F

LDE6L 12 B~C

LDE1H 6 C~O

LDE2H 10 B~C

LDE3H 20 Be~B

LDE5H 8 C~N

LDE6H 12 B~C

Shimadzu

PbST 10.02 B~N

LSA55 5.5 C~F

LSA70 7.7 C~O

LSA80 8 B~N

LSA120 12 B~C

LAS200 20 Be~B

LSA300 30 Be

CAMECA

(L)PC0 4.5 N~Na

(L)PC1 6 C~F

(L)PC2 10 B~O

(L)PC3 20 Be~B

L Boron 14.5 B

L Nitrogen 6 N

pits on the sample surface affect the number of the detected

characteristic X-rays, especially the absorption of characteristic

X-ray by the samples. Fig. 4 shows the additional absorption of

the characteristic X-ray by the sample around the pit on the

sample surface owing to the extended trajectory of the

characteristic X-ray. The additional absorption affects the

detected intensity of the characteristic X-ray and absorption

correction, generating spurious results. Furthermore, poorly

polished sample surfaces have more severe effects on the

analysis of light elements.8 Thus, the surfaces of the samples

must be flat and unscratched to avoid surface morphology

effects.

Coating. For electron microanalysis, a thin conductive film

needs to be coated on the surface of non-conductive samples to

prevent the charging effect on the sample surface and sample

heating,11 which could affect detection of characteristic X-ray and

image observation. The elemental composition of the coating

material should be different from the element to be analyzed in

the samples to minimize effect of coating on the analytical results.

Carbon and some metals and alloys, for example, Au and Au-Pd

alloy, are commonly used for the sample surface coating. Gold

has an atomic number of 79 and produces a complicated family

of X-ray lines when interacting with an electron beam, thus

strongly interfering with and absorbing the X-rays generated in

the sample. Gold also affects the landing energy of the incident

electron beam. Thus, gold coating is unsuitable for EPMA,

although gold is often used to coat samples for SEM imaging. In

contrast, carbon is a light element with an atomic number of 6

and is almost transparent to characteristic X-rays from the sample.

Its effect on the X-rays from the sample is minimal and can be

ignored during analysis. Therefore, carbon coating is used for

EPMA. However, the effect of the carbon coating must be

considered in the analysis of light elements or carbon-containing

samples.

The thickness of the carbon coating affects the X-ray intensity.

Kerrick et al.12 reported that the characteristic X-ray intensities of

F, Si, Fe, Na, and Sr decreased with an increase in the thickness

of the carbon film, especially for light elements (Fig. 5).

Differences in the thickness of the carbon coating between the

samples and standards lead to errors in the analytical results,

especially for lower-energy X-rays.13,14 However, the loss of

characteristic X-ray caused by the carbon film remains the same

for the unknown sample and standard with similar carbon film

thickness. Thus, it is essential to have the same thickness of the

carbon coating on both the standards and samples for the

quantitative analysis. Nevertheless, it is challenging to maintain

the same thickness of the carbon coating for all thin sections

loaded in the coating device even during the same evaporation

process. Zhang and Yang13 proposed a suitable method to

minimize the difference in carbon film thickness during coating.

Unknown samples and standards are often coated at different

times, which makes it difficult to coat them with the same carbon

film thickness. Therefore, a mathematical model was proposed to


Fig. 5 The characteristic X-ray intensity loss with the increasing carbon

film thickness (after Kerrick et al.12).

correct such errors to compensate for the changes in the

characteristic X-ray intensity owing to different thicknesses of

the carbon coating on the unknown sample and standard.15 This

correction should be widely adopted to obtain accurate results,

especially when using a low accelerating voltage or analyzing a

light element.

ANALYTICAL PROCEDURES AND EXPERIMENTAL CONDITIONS

A peak search is required for the element of interest before the

quantitative analysis, especially for the minor and trace elements,

because the peak of the minor and trace elements cannot be

searched in the sample during the analysis. These conditions

include accelerating voltage, beam current, beam diameter,

counting time, diffracting crystal, characteristic X-ray line,

background, analysis sequence of elements, and standards.

Accelerating voltage. It is desirable to select an accelerating

voltage higher than two times the critical excitation energy to

improve the excitation efficiency of characteristic X-rays and

obtain an ideal peak/background ratio.6 An accelerating voltage

of 15 kV is often used for analyzing oxides in geological samples,

while 20 kV is used for sulfide analysis (such as analyzing Cu

and Zn in sulfide). Low and high accelerating voltages can be

applied for light16,17 and trace elements,18 respectively, to obtain

high counts.

Beam current. A high beam current increases the X-rays

generated in the samples, thereby improving the precision of the

analysis. However, the X-ray counts could exceed the upper limit

of the X-ray counter in WDS when the beam current is high,

leading to incorrect results. A beam current of 2×10-8 A is

effective for analyzing major elements in geological samples.

However, a low beam current should be used when the migration

of element occurs during the analysis, such as Na in feldspar. In

contrast, a high beam current, up to 5×10-7 A or 9×10-7 A,18,19 can

be used for analyzing trace elements.

Beam diameter. Beam diameter is often small (such as afocused

beam or 1 μm spot size) to improve spatial resolution. However,

in some cases, a small beam spot could potentially damage the

sample or cause the migration of the element. A large beam spot

size is generally used when the element migration occurs during

the analysis. Beam diameter used for the sample and standard

analysis can be different when it is smaller than 20 μm.

However, the beam diameter should be similar when it is larger

than 20 μm because a large beam spot reduces the characteristic

X-ray intensity (Fig. 6).20

Additionally, the analytical area should be homogeneous. A

heterogeneous area can yield inaccurate analytical results when a

large beam is employed, partly because EPMA considers the

analysis area homogeneous in the matrix correction process.2 For

a heterogeneous area, the actual matrix where the emitted

characteristic X-rays travel through is different from that used in

the matrix correction procedure,6 leading to additional errors.10

Counting time. Counting time is the time used for measuring a

peak or background. Background counting time is usually half of

the peak counting time; however, it can be extended properly to

improve the detection limit. Counting time varies depending on

the concentrations of the elements. Peak counting time of major

elements is usually from 10 s to 30 s, while that of minor or trace

elements can be from 30 s to 120 s to improve the precision.

However, the counting time for migratory elements should be

shorter.

Background. Continuous X-rays generated by bremsstrahlung

radiation contribute to the background intensity in the WDS

X-ray spectra. These background X-rays are subtracted to obtain

the net intensity of the characteristic X-rays. In a routine analysis,

the background intensity is usually obtained through linear fitting

according to the intensity measured on one side or both sides of

the peak (Fig. 7a). However, the actual background may be

curved (Fig. 7b), and a two-point linear fitting often results in

errors, especially for light or trace element analyses. Methods

such as two-point exponential, polynomial, or multi-point

fittings19,21,22 can be applied for a better estimation of the

background intensity. In addition, the mean atomic number


Fig.

6

A graph showing the changes in the detected characteristic X-ray

intensity with the increasing beam diameter.20

(a) Na, Mg, Al, and Si with

a thallium acid phthalate (TAP) crystal and (b) Si in olivine and jadeite

with a TAP and polyethylene terephthalate (PET) crystal. The gray area in

the graph ranges from 98 wt% to 102 wt% on the vertical axis.

Fig.

7

A schematic diagram demonstrating the background intensity

obtained by interpolation (Bg1 and Bg2): (a) linear fitting applicable when

the background curvature is small; (b) linear fitting not applicable when

the background curvature is large (after Reed6).

(MAN) background calibration curve method can be applied for

the background intensity correction,23 acquiring the background

intensity correction by measuring the peak intensities in a set of

standards without the element of interest. This method can avoid

the background measurement artifacts, thereby improving the

measurement precision for the trace elements compared to

traditional off-peak measurements.

Diffracting crystals. Diffracting crystals with different

interplanar d spacings (Tables 1 and 2) are used to analyze

different elements. A smaller d spacing is essential to obtain a

better peak resolution in WDS. Counting rate and peak resolution

should also be considered when selecting diffracting crystals.

Counting rate increases when the diffraction angle is large,

although the peak resolution may be poor. For example,

distinguishing Ce Lβ1 from Nd Lα1 is difficult with PET crystals

and easy with LIF crystals in the JEOL EPMA (Fig. 8).

Analysis sequence of elements. Elements that easily migrate

under electron beam bombardments, such as Na, K, and F,

should be analyzed first. In addition, the analysis sequence of the

elements should be arranged based on the diffraction angles from

large to small or vice versa to improve the analytical efficiency

and minimize movement and wearing of the WDS spectrometer.

Standard. Chemical compositions of standards are obtained

through different analytical techniques, such as wet chemical

analysis, X-ray fluorescence spectroscopy, and EPMA. It is ideal

to select standards with the same chemical composition as the

sample to minimize matrix effect. The concentrations of the

elements to be calibrated in the standard should be sufficiently

high to generate enough characteristic X-rays and ideally higher

than that in the samples. In addition, the quality of a standard

must be evaluated. For EPMA applications in geosciences,

natural minerals are the most commonly used standards.24

Although these standards were previously evaluated as

homogeneous by various analytical techniques, the quality,

especially the homogeneity, may not be guaranteed. Thus, EPMA

operators should examine the quality of standards in their

laboratory and select appropriate standards accordingly.

DATA EVALUATION AND INTERPRETATION

Assuming all major elements are analyzed, and results are not

normalized to 100 wt%, the data quality of the quantitative

EPMA is first evaluated by examining whether the total content

of all measured elements is approximately 100 wt%. Generally,

the total analytical error of an EPMA instrument provided by the

manufacturer is approximately 2 wt%. Therefore, a total of

98 wt%–102 wt% is considered as a good result.

Theoretically, the total is 100 wt% when all elements are

correctly measured during the quantitative EPMA. For example,

the total for olivine, pyroxene, feldspar, and garnet is

approximately 100 wt%. However, the theoretical total is less

than 100 wt% when some elements, such as H and Li, are not

measured. For example, hornblende and mica contain

approximately 2.5 wt% and 4.5 wt% H2O, respectively, and the

Fig. 8 The effect of the diffracting crystal on resolution in a wavelength

dispersive (WD) spectra: peaks are recognizable with LiF (a); peaks are

unrecognizable with PET (b) (after Reed6).


theoretical total is approximately 97.5 wt% and 95.5 wt%,

respectively. An analytical total within the range of “theoretical

total - 2 wt%” and “theoretical total + 1 wt%” is deemed suitable.

The good analytical total is closer to the lower side due to the

possible presence of unmeasured minor or trace elements.

Another criterion to evaluate the analytical results is by

examining the atomic ratios or atomic % according to the

chemical formula of minerals. For example, the S atoms in pyrite

(FeS2) is 2/3 of the total atoms. Additionally, the Si atoms in

olivine ((Mg, Fe)2[SiO4]) is 1/3 of the total cations.

Minor or trace elements have slight effect on the analytical

total and atomic ratio. Therefore, it is difficult to determine the

quality of their analytical result. A proper secondary standard

containing the minor or trace elements analyzed in the sample

can be measured during the sample analysis to evaluate the

analytical quality of the minor or trace elements.

ANALYTICAL PRECAUTIONS FOR SPECIAL MINERALS

In geosciences, some minerals are special and require additional

attention during the quantitative EPMA. They include: (1)

minerals with migrant elements and time-dependent X-ray

intensities (TDI) during the analysis, such as feldspar, carbonates,

apatite, glass, and hydrous minerals; (2) minerals containing light

elements that are difficult to analyze, such as C in carbonate, Be

in beryl, B in tourmaline, and hydrous minerals; and (3) minerals

with variable oxidation-state oxides, such as magnetite, hematite,

and cuprite. The precautions essential for these “special”

minerals during the quantitative EPMA are described below.

Feldspar. Sodium and potassium in feldspar can easily migrate

during EPMA, leading to underestimated Na and K contents and

an overestimated Si and Al contents. Therefore, a low beam

current (10 nA) and a large beam spot (10 μm or more), is

preferred during the analysis. Additionally, Na and K should be

analyzed first. In addition to examining the analytical total, the

atomic ratios should also be checked to evaluate the data quality.

The atomic ratio of feldspar that can be used for the data quality

evaluation includes Na + K+ 2×Ca = Al and 3×Na + 3×K +

2×Ca = Si (in cation number). The minerals or materials without

TDI (such as homogenous jadeite standard) are preferred as the

standard for Na. However, when albite minerals are selected as

standards, it is necessary to decrease the beam current and

increase the beam diameter to ensure that there are no TDI for Na

during the standard analysis.

Carbonate minerals. Significant element migration under

electron beam irradiation was also discovered in carbonate

minerals.25-27 The optimized analytical conditions for carbonate

minerals include a beam current of 5 nA and beam diameter of

10 µm for calcite; 10 nA and 10 µm for dolomite; 10 nA and 5

µm or 20 nA and 10 µm for siderite; and 20 nA and 5 µm for

other carbonates.25 Additionally, the easily migrated elements

should be analyzed first. The carbon content is stoichiometrically

calculated (such as the ratio of C:O = 1:3) based on their

chemical formula (MCO3, where M is a metal cation) or by

fixing the CO2 content from the difference between 100 wt% and

the MO content, and subsequently the CO2 content is included in

the matrix correction. In addition, silicate minerals are more

suitable than carbonate minerals to serve as standards for the

carbonate mineral analysis. When carbonate minerals are

selected as standards, it is also necessary to ensure that there are

no TDI during the standard analysis.

Apatite. Fluorine in apatite shows strong TDI during the electron

beam irradiation,28-31 especially when the incident electron beam

is parallel to the c-axis of apatite (Fig. 9). A TDI correction model

based on the mathematical relationship between the characteristic

X-ray intensity and time was proposed to measure the F content

in apatite.31,32 The model extrapolates the X-ray intensity to the

moment when the electron beam just arrives at the sample

surface (time=0). In addition, it was proposed that cooling the

samples with a liquid nitrogen cold trap in EPMA could reduce

the TDI effect.33

However, not all laboratories can perform automated TDI

correction and use liquid nitrogen cold traps due to software and

hardware limitations. The following are the considerations for the

quantitative EPMA of apatite:

(1) Avoid exposing apatite to a high electron beam current

before analysis. Observe it with a low beam current and

minimize the observation time.

(2) A low beam current and large beam diameter can be used

to minimize the F migration. Analyze F and Cl first, with a short

analytical time.30

Fig. 9 The variation in the X-ray intensity of different crystal planes of

fluorapatite with the exposure time of electron beam (Modified after

Stormer et al.31).


(3) Polish apatite crystal such that its c axis is parallel to the

polished surface since apatite exhibits less TDI effect in this

direction.28

(4) Select diffracting crystals LDE1 (JEOL), PC1 (Cameca),

and LSA55 (Shimadzu) for analyzing F since they have higher

count rates than the traditional TAP crystals. It is necessary to

select an appropriate background position for the measurement

because the peak shape of F is broad when using these diffracting

crystals.

(5) Utilize topaz, BaF2, MgF2, or materials with a weak TDI as

the standard for analyzing F. Fluorine in apatite has strong TDI

effect, which is not a good choice as a standard for analyzing F in

apatite.30 Fluorite is also unsuitable as a standard for F in apatite

because F in fluorite shows significant TDI.

(6) Examine the F concentration. According to the apatite

chemical formula, the F concentration in the end member of

fluorapatite is 3.77 wt%. Thus, the F concentration in apatite

higher than 3.77 wt% should be treated carefully.

Glass. Sodium and potassium in silicate glass also exhibit TDI

under electron beam bombardment, particularly those in rhyolite

or H2O-bearing glass.34 Experimental conditions suitable for

analyzing glass include reducing the current density by a lower

beam current and utilizing a larger beam diameter,20,34,35

conducting TDI correction,34 and analyzing at cryogenic

temperatures.36

The following are the considerations for the EPMA of glass:

(1) Use a low beam current and a large beam diameter. When

the beam diameter exceeds 20 µm, use the same beam diameter

for both standards and samples.

(2) Determine whether the sample exhibits TDI under the

selected conditions before analysis. Reduce the beam current

and/or increase the beam diameter till the TDI is negligible when

the sample demonstrates TDI effect.

(3) Analyze a secondary glass standard with a composition

similar to the sample for data quality control or evaluation.

Difference between 100 wt% and an actual total content obtained

is often used to determine the concentrations of H2O or volatiles

in glass. However, the difference may also arise due to the

surface discharge of the sample37 or TDI. The H2O content

calculated by the difference method must be involved in the

matrix correction for glass samples with high H2O content.

Hydrous minerals. All the components in a sample, including

H2O, should be considered for the matrix corrections in

quantitative EPMA. The total of hydrous minerals obtained by

EPMA is less than 100 wt% because EPMA does not measure

the H2O content. Matrix correction factors can be incorrect when

H2O is not included in the matrix correction. However, the effect

can be ignored for minerals with low H2O content, such as mica

and hornblende. In contrast, inaccurate results can be obtained for

minerals with high H2O content, such as chlorite, serpentine, and

turquoise, when H2O is not considered in the matrix correction.

Tourmaline and beryl. The accurate EMPA of light elements is

problematic due to the high absorption of the low energy X-rays

and spectral interferences from higher-order X-ray lines of

heavier elements.16 During the EPMA of tourmaline and beryl, B

and Be are calculated instead of being measured. The chemical

formula of tourmaline is (Na,Ca,K)R3Al6[Si6O18](BO3)3(O,OH,F)

(R = Mg, Fe, Li, Mn, Cr, V, or Al) with approximately 10 wt%

B2O3 while that of beryl is Be3Al2[Si6O18] with approximately 14

wt% BeO. The contents of B2O3 and BeO can be calculated

according to the atomic ratio B:Si=1:2 and Be:O=1:6 with B2O3

and BeO included in the matrix correction when B and Be are

not measured directly by EPMA.

Variable oxidation state oxides. Concentrations obtained by

quantitative EPMA are usually expressed as oxides of elements

with oxygen calculated by stoichiometry, i.e., proportioning O

according to the valence state of a cation and then participating in

the matrix correction. Therefore, the valence states of the cations

are determined before the analysis.

When a preset valence state of an element (such as Fe) does

not match the actual valence state of the element in a mineral

(such as magnetite), the calculated O content and the total will be

incorrect. For example, the total obtained will be 1.6 wt% less

than the theoretical total 100 wt% when the Fe valence is

incorrectly set as +2 for hematite (Fe2O3). In contrast, an

accurate result is obtained when the Fe valence state is correctly

set to +3.38 Iron in minerals of the spinel group, such as

magnetite Fe2+Fe3+2O4, exists as both Fe3+ and Fe2+ in different

proportions. It has been demonstrated that careful electron

microprobe analyses with stoichiometry and charge balance for

Fe-bearing minerals such as magnetite and ilmenite yield

Fe3+/(Fe2++Fe3+) ratio similar to those from Mössbauer analyses,

albeit with larger relative errors.39 However, the software

provided by the instrument manufacturers may only allow an

integer value to be input, creating an obstacle for analyzing

oxides with variable oxidation states. In this case, cations with

variable oxidation states can be analyzed and determined as a

simple substance (setting the valence state to 0), and O can be

calculated from difference and included in the matrix

correction.38

RECENT ADVANCES AND CHALLENGES IN EPMA

Light element analysis. Light elements, such as Be, B, C, N, O,

and F, emit low energy “soft X-rays” (energy less than 1 keV)

(Table 3). Detection of these low energy soft X-rays, thereby


Table 3. Characteristics of the light element Kα lines (after Goldstein

et al.7)

Element Symbol Z λ(Å) E(keV)

Beryllium Be 4 114.0 0.109 Boron B 5 67.6 0.183

Carbon C 6 44.7 0.277

Nitrogen N 7 31.6 0.392 Oxygen O 8 23.62 0.525

Fluorine F 9 18.32 0.677

Fig. 10

(a) The peak shape of C Kα peaks recorded from SiC and Fe3C

with differences in position and shape (after Bastin and Heijligers43); (b)

Two extremes of the B Kα peak shape recorded from ZrB2 with the

rotation of the specimen (after Bastin and Heijligers44).

determining light element concentrations by EPMA is

challenging and is often less efficient and less accurate. These

challenges include: (1) samples, diffracting crystals, and detector

windows that absorb the low energy characteristic X-rays

strongly; (2) reliable mass absorption coefficients of light

elements remain to be determined; (3) low energy characteristic

X-rays are interfered by the X-rays generated from heavier

elements; (4) peaks of characteristic X-rays from light elements

shift and the peak shapes of the light elements change too in

different matrices or compounds; (5) the lack of suitable light

element standards; and (6) the carbon contamination and

thickness difference of the carbon film on the sample surface.

Nevertheless, the accuracy and precision of the light element

analysis can be improved through the following steps:

(1) Use a diffracting crystal with large d spacings, such as

layered dispersion element (LDE, Table 2), to obtain significantly

high counts and peak/background ratios.

(2) Use a suitable acceleration voltage for the light element

analyses. For example, the intensity of Be in beryllium metal is

the highest when the acceleration voltage is 12 kV.40 Although

the intensity of the characteristic X-rays increases with increasing

the acceleration voltage, a higher acceleration voltage also

increases the depth of characteristic X-rays, causing a high

absorption effect.16,40,41

(3) Minimize the effect of coating material, primarily carbon,

on the characteristic X-rays of light elements. The conductive

thin film coated on a sample surface can significantly absorb

low-energy characteristic X-rays emitted by the light elements.

Carbon contamination on the sample surface due to the heat from

electron beam bombardment make the absorption effect even

worse.15 Differences in the carbon film thickness between the

standards and samples also lead to errors in the analytical results.

Therefore, it is strongly recommended to use a liquid nitrogen

cold trap for cooling,15,42 coat standards and samples with the

same thickness of the carbon film,13 and correct uncertainties

from carbon film thickness and counting rate to effectively

alleviate the above-mentioned problems.15

(4) Consider effects of the peak shift and peak shape on the

measured intensities. Changes in the peak shift and peak shape of

a light element have been observed in a different matrix

(Fig. 10a).43 Changes in the peak shape also occur in different

crystallographic planes within the same mineral (Fig. 10b).16,44

The light element content obtained from the peak height will lead

to erroneous results when effects of peak shifts and peak shape

changes are not resolved,. The methods proposed in previous

studies,44 such as peak area integral intensity and area/peak factor,

can assist in obtaining more reliable results.

(5) Use an appropriate mass-absorption coefficient for and a

primary standard similar to the composition of the unknown

sample (preferably also similar in crystallographic orientation),

which is crucial for the light element analysis.16,44

In addition, utilizing pulse height analysis for alleviating

interference from heavier element40,45 and developing ultra-soft

X-ray spectrometer technology will also assist in analyzing light

elements.46,47

Trace element analysis. Trace element analysis using EPMA

has gained extensive attention in recent years. The principal

challenge is to effectively improve the detection limit and

increase the accuracy and precision of the analytical results. The

precision of the analysis is improved by increasing the

accelerating voltage, beam current, and counting time. In most

cases, a high accelerating voltage also helps improve the

detection limit. Although a high beam current and long counting

time improve precision, they can also cause TDI.19,48,49

Nevertheless, allocating the counting time to different locations

on the homogeneous sample surface reduces the TDI effect,

which is well utilized for analyzing glass and quartz.22,50 In

addition, using large diffracting crystals and simultaneously

analyzing the same element with multiple spectrometers improve

the precision and detection limit.22,51

Factors affecting the trace element analysis include peak

interference, background fitting, and secondary fluorescence

effect.19,21 The peak interference is mainly caused by the

low-order X-rays of some elements (Fig. 11a) and may be

separated by overlapping the peak deconvolution and

correction.30,52 A “step” is a sharp decline in the X-ray continuum

background (Fig. 11b),6 and a “hole” is a negative peak in the

X-ray continuum background (Fig. 12).22 The “steps” and “holes”

observed in the background or underneath the peak also


Fig. 11

Wavelength dispersive scans of quartz at the position for (a) the Al

Kα peak exhibiting a curving background and (b) the Ti Kα peak

demonstrating “step” in the left background of Ti Kα (after Cui et al.19).

Fig. 12 Wavelength dispersive scans of SiO2 and TiO2 at the position for

the Ti Kα peak showing “holes” in the continuum, both adjacent to and

directly underneath the analytical peak position (after Donovan et al.22).

significantly affect the trace element analysis. Therefore, the

interference peaks, “steps”, “holes” and other interfering factors

present in the background need to be avoided when selecting the

background positions for analysis. The secondary fluorescence is

the excitation of an element in an adjacent phase by the primary

X-rays from the measurement spot. This phenomenon can occur

far away from the original measurement spot since X-rays

penetrate very far, for example, a chromite host with an olivine

inclusion when analyzing Cr in the olivine,3 or a neighboring

rutile close to quartz when analyzing Ti in the quartz.48 This

results in an overestimation of the concentration of the analyzed

element. Therefore, it is essential to select the measurement spots

with a sufficient distance from the neighboring mineral19,48 or

utilize simulation software for correcting the secondary

fluorescence effect to avoid or reduce secondary fluorescence

effect.53-55 For elements with a curved background shape rather

than a straight line, such as Al Kα in quartz (Fig. 11a), a

two-point exponential curve fitting for the background or a blank

correction (measure a zero-concentration standard with a matrix

similar to the unknown to obtain an accurate background

intensity) is superior to a two-point background interpolation

method.22 The multipoint background fitting is another effective

method for fitting a curved background.19,21 In addition, the

MAN method adopted by the Probe Software, an EMPA

application software, is also a reliable fitting method for the

background intensities.23 In an analytical procedure, a suitable

secondary standard and a sample can be analyzed simultaneously

to effectively monitor the accuracy of the results.

EPMA U-Th-Pbtotal dating. The EPMA U-Th-Pbtotal dating, also

known as the chemical U-Th-Pbtotal isochron method (CHIME),

is widely applied for accessory minerals rich in U and Th, such as

zircon, monazite, xenotime, thorianite, and uraninite.56-59 This

method assumes that the dating mineral system is closed (without

the Pb loss), and the initial Pb content is negligible. The basic

dating equation was continuously improved, and several methods

were proposed, including isochron, average apparent age,

three-dimensional fitting, and two-dimensional fitting

methods.57,58,60,61 Theoretically, the initial Pb content can be

estimated and corrected by applying isochron and

three-dimensional fitting methods. However, its applications

become problematic when the initial Pb is heterogeneously

distributed in the minerals.

Since most accessory minerals have low contents of U, Th,

and Pb (mainly Pb), high-quality measurements of these

elements play a vital role in the CHIME dating method.62

However, analyzing U, Th, and Pb is a challenge for rare earth

element-rich (REE-rich) minerals (such as zircon, monazite, and

xenotime) due to the complex spectral characteristics of REEs

that affect the Pb analysis.62,63 Hence, interference correction is

essential when interferential elements exist in a sample.57 Some

methods such as shared background measurement, multipoint

background analysis, and MAN method can be used for

background interference to obtain accurate backgrounds.21,23,64

Combined analysis with WDS/EDS. EDS and WDS are widely

used in the microanalysis of major elements in minerals and

materials. The application of silicon drift detector EDS

(SDD-EDS) has greatly improved the EDS performance with the

development of semiconductor detectors and improvement of

EDS energy resolution.65 The SDD-EDS has high counts

throughput and extremely high stability in terms of peak shape

(resolution) and peak position (calibration),66 making it possible

to utilize EDS for accurate quantitative analysis. Thus, EDS has

become an analytical tool for the precise quantification of major

elements (>1 wt%) even when severe peak interference

occurs.67-71 Moreover, trace elements analysis with EDS can

obtain a detection limit of 250 ppm or less in the absence of peak

interference.68

The peak resolution of SDD-EDS is lower than that of the

WDS, thereby limiting the determination of trace element

content by EDS. However, the simultaneous analysis, short

measurement time, and simple operation make EDS suitable for

analyzing major elements, especially for easily migrated

elements. A rapid and accurate component information of the

sample can be obtained when a WDS with high resolution and

low detection limit is combined with the simple and fast EDS.

In order to combine WDS and EDS, it is necessary to meet the


Fig. 13

Five wavelength-dispersive spectra acquired on pure standards of

Cr, Mn, Fe, Co, and Ni at 5 kV and 50 nA, using a LDE1 crystal (after

Pinard and Richter79).

Fig. 14 Fe L X-ray emission spectra of almandine and andradite at 10 kV

with the difference spectrum. The “flank method” measurement positions

(Fe Lα and Fe Lβ) are defined in the regions having the largest difference

between spectra (after Höfer and Brey87).

requirements of the operating software to control the

simultaneous working of WDS and EDS. In addition, suitable

operation conditions should be established. The normal working

conditions of WDS and EDS are different. WDS requires a large

beam current to obtain a higher count rate, which generates high

counts in EDS and leads to a long dead time. When WDS and

EDS are combined, WDS can be used to analyze trace elements

(0.01 wt%–1 wt%) and elements with overlapping peaks in EDS

(such as the L line of Ni, Cu, and Zn), while EDS can be used to

analyze major elements. Thus, the advantages of WDS and EDS

can be availed to improve the efficiency of analysis.72

Quantitative analysis at low accelerating voltages for

improved spatial resolution. With field emission guns equipped

in EPMA, large and steady beam currents under low accelerating

voltages become available,2,6 leading to an improved spatial

resolution down to submicron scale. The low accelerating voltage

EPMA has been applied to study volcanic processes by diffusion

chronometry across submicron crystal zones,73 and for

developing alloy materials with desirable properties.2 However,

the following challenges remain for this technology:

(1) Carbon accumulation and contamination due to the

polymerization and deposition of hydrocarbon molecules around

the analysis point absorbs the low energy X-rays.15,74 Plasma

cleaning, gas cleaning, liquid nitrogen traps, and Peltier

thermoelectric cooling devices can be used to mitigate the impact

of carbon contamination.15,42,75,76 Replacing oil pumps with

oil-free vacuum pumps can eliminate carbon contaminations at

the source.2 In addition, some correction methods can be used to

correct the carbon contamination effect.15

(2) The surface morphology of the samples, such as polishing

quality, coating, and surface oxidation, can significantly affect

analyses at low voltages because the electron beam cannot

penetrate the sample deeply enough.74,77

(3) The L and M X-ray lines should be used at low voltages

because high-energy X-ray lines of elements are not generated at

low accelerating voltages. The L and M lines are severely

overlapped and interfered by the second and third order X-rays of

other elements,78,79 making background analysis more difficult

(Fig. 13). In addition, low X-ray intensity requires a long

counting time and/or high beam current for the trace element

analysis.2

(4) For the first-row transition elements in the periodic table,

their L lines are mostly low-energy X-rays that do not have

well-determined mass absorption coefficients. Moreover, the

chemical bonding and self-absorption result in severe peak shift

for the Lα signal, invalidating the traditional absorption

correction.79-82 The methods proposed to address this issue

include: a) correcting the matrix correction coefficient;83 b)

replacing Lα with L1 (M1-L3 transition);79,80,84 and c) combining

curve correction with the area integration of Lα and Lβ.77,85

However, these solutions are only applicable for samples with

simple compositions, such as olivine and ferroalloy, and the

validity of samples with complex compositions still requires

verification.

Iron oxidation state. The peak shift of Fe Lα and Fe Lβ X-ray

lines and change of Fe Lα/Lβ intensity ratio were observed in the

Fe3+/ΣFe analysis with EPMA. The peak shift and intensity

variation differ with the oxidation state.86 Hence, the Lα and Lβ

of Fe2+ (such as almandine) and Fe3+ (such as andradite) may be

distinguished and quantified in terms of their energies of L lines

(Fig. 14). The quantitative analysis of Fe oxides is based on the

self-absorption correction, improved peak resolution, and matrix

effect correction. Among these, self-absorption is the main

influencing factor that depends on the Fe concentration and Fe

L-line X-ray path length in the sample.87

The most commonly used method to obtain Fe3+/ΣFe of

samples is known as the “flank method,” which involves a


careful consideration of the peak positions of Lβ and Lα lines and

Lβ/Lα intensity ratio.86-93 The WDS spectrometers used are

recalibrated based on the standards to strengthen the EPMA

sensitivity for the “flank method”.93 This calibration can avoid

the Fetotal content interference and the self-absorption effect.93

Furthermore, this method measures the intensity ratios on the

high- and the low-energy sides of Lα and Lβ, respectively, and

analyzes the Lα and Lβ peaks at the locations where the spectral

differences are most significant (Fig. 14), thus considerably

improve the sensitivity.87

Because the Fe2+ and Fe3+ self-absorption effects vary with

minerals, the self-absorption effect of different minerals should

be individually determined to establish a valid empirical

correction model. The chemical composition of different

end-members for garnet group minerals has no obvious effect on

the Lβ/Lα ratio, indicating that the matrix effect is the same, and

the “flank method” can be applied to different garnet

end-members.87 The studies on the self-absorption effect of

different minerals facilitates the application of the “flank method”

to other materials, such as glass,89-92 hornblende,88 and biotite.88

However, suitable and appropriate standards with accurate

Fe3+/ΣFe values and establishment of specific “flank method” for

different minerals are urgently required to promote the

application of the “flank method” in determining Fe3+/ΣFe by

EPMA.

Standard reference materials. The standard reference materials

used in EPMA include the primary standards and secondary

standards. The primary standards are essential for every EPMA

laboratory. Nevertheless, the primary standards used in different

EPMA laboratories could vary. There are currently insufficient

primary standards that can be applied in all EPMA laboratories.

The absence of a global primary standards leads to a situation

where we cannot reliably compare the quality of the quantitative

results from different EPMA laboratories. Therefore, it is

necessary to establish globally adopted, high-quality primary

standards for most elements on the periodic table.

As discussed before, the quality of the EPMA results can be

assessed using the analytical total or atomic ratios. However, it

becomes difficult to use this approach to evaluate the quality of

the trace element analyses. Therefore, suitable secondary

standards are necessary for evaluating the analytical results.

Moreover, it is effective to evaluate the results and find the

problems by analyzing the secondary standards used under the

same analytical conditions and sessions as the samples, for

example, cross analyzing the secondary standards and unknown

samples. In addition, blank references can be used for accurate

background measurements.22,23

Utilizing a secondary standard with its giving compositions

similar to an unknown sample is always preferred. Unfortunately,

such secondary standards are often missing, especially for the

trace and light elements and for EPMA U-Th-Pbtotal dating. For

example, for Ti analysis in quartz, a blank quartz reference22 with

approximately 0 ppm of Ti and a quartz reference material94 with

57±4 ppm of Ti can be used to measure the background and

monitor the quality of the analytical results, respectively.

However, it could be beneficial to monitor the accuracy of the

Ti analysis in quartz with secondary standards having low (such

as 5 ppm) or relatively high (such as 100 ppm and 500 ppm) Ti

content.

CONCLUSIONS AND OUTLOOKS

The past 70 years have witnessed widespread applications of

EPMA in analyzing minerals or other materials for major, minor,

trace, and light elements, for U-Th-Pbtotal dating, and for

determining the iron oxidation state. Improvement of hardware

and software promotes the development of the analytical

techniques of EPMA. For example, the availability of large

diffracting crystals promotes the analyses of trace elements. Field

emission gun enables high spatial resolution analysis, and

implementing the latest soft X-ray analysis improves the analysis

of light elements. The future progress of EPMA technology lies

in: 1) further improvement of the instrument hardware and

software, 2) optimization of the existing analysis methods, 3)

development of practical methods for individual minerals and

elements, 4) understanding the mechanism of interference factors

and related issues, and 5) optimized matrix correction model.

Developments, advances, and novel applications of EPMA

techniques also require hardware manufacturers and instrument

users to work together to address the challenges and difficulties

encountered.

AUTHOR INFORMATION

Shui-Yuan Yang is an associate professor of

geosciences at the State Key Laboratory of

Geological Processes and Mineral Resources,

China University of Geosciences in Wuhan

since 2013. He is also the lab manager of the

EPMA. He worked as a visiting fellow at the

Research School of Earth Sciences, Australian

National University in 2020. He received a

BSc in geochemistry (2008) and a PhD in

mineralogy, petrology, and economic geology

(2013) from the Nanjing University. His main topic of research is the use

of elemental and isotopic geochemistry to reconstruct the genesis of

granitoids (especially in South China) and its related uranium and REE

deposits. He also focuses on the chemical and boron isotopic

compositions of tourmaline and its implication in magmatism and related

mineralization. A growing interest is the study of the analytical methods of

EPMA and their applications to geosciences.



*S.-Y. Yang


Notes


ACKNOWLEDGMENT

This research was funded by the National Key R&D Program of

China (2017YFC0601404), Natural Science Foundation of China

(41773040), and Fundamental Research Funds for the Central

University, China University of Geosciences (Wuhan)

(CUGCJ1818). We are grateful to Prof. Wei Guo and four

anonymous reviewers for providing valuable comments and

suggestions, which helped in significantly improving this

manuscript.

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200

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Depth analysis of elements distribution in materials.

Element μg/kg Element μg/kg Element μg/kg Element μg/kg Element μg/kg

Li 0.5 V 0.5 Y 0.5 Ba 0.5 Hf 0.5

Be 0.5 Cr 0.5 Zr 1 La 0.5 Ta Electrode

B 0.5 Mn 0.5 Nb 0.5 Ce 0.5 W 1

F 5 Fe 0.5 Mo 1 Pr 0.5 Re 1

Na 0.5 Co 0.5 Ru 0.5 Nd 0.5 Os 1

Mg 0.5 Ni 0.5 Rh 0.5 Sm 0.5 Ir 1

Al 0.5 Cu 1 Pd 1 Eu 0.5 Pt 1

Si 1 Zn 1 Ag 1 Gd 0.5 Au 1

P 1 Ga 1 Cd 5 Tb 0.5 Hg 5

S 1 Ge 5 In Matrix Dy 0.5 Tl 1

Cl 1 As 0.5 Sn 5 Ho 0.5 Pb 1

K 1 Se 5 Sb 1 Er 0.5 Bi 1

Ca 5 Br 1 Te 1 Tm 0.5 Th 0.5

Sc 0.5 Rb 0.5 I 1 Yb 0.5 U 0.5

Ti 1 Sr 0.5 Cs 1 Lu 0.5

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