Neutron imaging of hydrogen-rich fluids in geomaterials and

Earth-Science Reviews 129 (2014) 120–135

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Earth-Science Reviews

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Neutron imaging of hydrogen-rich fluids in geomaterials and engineeredporous media: A review

E. Perfect a,⁎, C.-L. Cheng a,b, M. Kang a,c, H.Z. Bilheux c, J.M. Lamanna d, M.J. Gragg e, D.M. Wright b

a Department of Earth and Planetary Sciences, University of Tennessee – Knoxville, Knoxville, TN 37996, United Statesb Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United Statesc Chemical and Engineering Materials Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United Statesd Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee – Knoxville, Knoxville, TN 37996, United Statese Altamont Environmental, Inc., Asheville, NC 28801, United States

⁎ Corresponding author. Tel.: +1 865 974 6017.E-mail address: [email protected] (E. Perfect).

0012-8252/$ – see front matter © 2013 Elsevier B.V. All rihttp://dx.doi.org/10.1016/j.earscirev.2013.11.012

a b s t r a c t
a r t i c l e i n f o
Article history:Received 15 July 2013Accepted 25 November 2013Available online 12 December 2013

Keywords:Neutron imagingPorous mediaRadiographyTomographyWaterHydrocarbons

Recent advances in visualization technologies are providing newdiscoveries aswell as answering old questionswithrespect to the phase structure andflowof hydrogen-richfluids, such aswater and oil, within porousmedia.Magnet-ic resonance and x-ray imaging are sometimes employed in this context, but are subject to significant limitations. Incontrast, neutrons are ideally suited for imaging hydrogen-rich fluids in abiotic non-hydrogenous porousmedia be-cause they are strongly attenuated by hydrogen and can “see” through the solidmatrix in a non-destructive fashion.This reviewpaper provides an overviewof the general principles behind the use of neutrons to image hydrogen-richfluids in both 2-dimensions (radiography) and 3-dimensions (tomography). Engineering standards for the neutronimaging method are examined. The main body of the paper consists of a comprehensive review of the diverse sci-entific literature on neutron imaging of static and dynamic experiments involving variably-saturated geomaterials(rocks and soils) and engineered porous media (bricks and ceramics, concrete, fuel cells, heat pipes, and porousglass). Finally some emerging areas that offer promising opportunities for future research are discussed.

© 2013 Elsevier B.V. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1202. Neutron imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

2.1. Neutron transmission radiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1212.2. Neutron computed tomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

3. Neutron imaging standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244. Imaging studies on geomaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.1. Rocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244.2. Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5. Imaging studies on engineered porous media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1275.1. Bricks and ceramics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1275.2. Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285.3. Fuel cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285.4. Heat pipes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305.5. Porous glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

6. Discussion and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

ghts reserved.

1. Introduction

As imaging technologies continue to improve our ability to visualizethe phase structure and flow of hydrogen-rich fluids at the pore scale,the resulting high resolution data sets provide opportunities for

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121E. Perfect et al. / Earth-Science Reviews 129 (2014) 120–135

evaluating existing models, and developing new theoretical frame-works. While magnetic resonance imaging (MRI) and x-ray imagingcontinue to be employed in this context, both techniques are subjectto significant limitations. For example, x-ray imaging relies on the useof tracers to differentiate between air and water in variably-saturatedporous media (Basavaraj and Gupta, 2004), while MRI is limited bythe range of pore sizes that can be visualized (Chen et al., 2003) andthe presence of iron in the solid matrix (Hall et al., 1997). In contrast,neutrons are ideally suited for this application because of their strongattenuation by hydrogen inwater and oil, and their relative insensitivityto both the gas phase in pores and solid constituents, such as silica andiron.

Brenizer (2013) has reviewed the history of neutron imaging from itsconception to the present day. The neutron itselfwas discovered by JamesChadwick in 1932. Only 3 yr later Hartmut Kallmann and Ernst Kuhn inBerlin, Germany began tomake radiographic images of objects using neu-trons. However, little progress was made on neutron imaging until the1950's when technical improvements in the film employed opened upthe field to practical applications. Neutron imaging is based onmeasuringthe transmitted intensity of neutrons through an object, either in two di-mensions (radiography) or three dimensions (tomography).

In the geosciences neutrons were initially used to measure thewater content of soil. Research based on the thermalization of“fast” neutrons released from a source probe inserted into an accesstube commenced in the 1950's following the seminal paper byGardner and Kirkham (1952). It was not until the 1970's, however,that neutron imaging was first applied to natural and engineered po-rous media (Reijonen and Pihlajavaara, 1972; Subraman andBurkhart, 1972; Wilson et al., 1975; Lewis and Krinitzsky, 1976). Al-though there have been previous reviews of this topic with respect toapplications in earth science, material science, and engineering(Lehmann et al., 2004; Wilding et al., 2005; Winkler, 2006; Banhartet al., 2010; Hess et al., 2011; Kardjilov et al., 2011), none of these fo-cused specifically on neutron imaging of hydrogen-rich fluids invariably-saturated porous media.

This review paper provides an overview of the general principles be-hind the use of neutron radiography and tomography. The standards forneutron imaging are also examined. Themain body of thepaper consistsof a comprehensive review of the diverse scientific literature on neutronimaging of static and dynamic experiments involving hydrogen-richfluids in variably-saturated abiotic porous media. We consider the fol-lowing natural and engineered materials: bricks, ceramics, concrete,fuel cells, heat pipes, porous glass, rocks, and soils. The focus is onnano-, micro- and meso-scale porous systems in which capillary forcesdominate over gravity. Research onmacro-porousmaterials such as air-craftwingswith an internal honeycomb structure (Hungler et al., 2009),or biological materials such as plant roots and wood xylem tissue (e.g.,Nakanishi and Matsubayashi, 1997), is beyond the scope of this reviewand will not be covered. Finally, some new developments in neutronimaging that offer exciting opportunities for future research will bediscussed.

2. Neutron imaging

Neutron imaging beamlines have traditionally been installed atreactor-based facilities, although a few are associated with spallationsources. Table 1 lists the most well-known existing neutron imagingfacilities, along with their beamline parameters. Many of these havebeen in operation for decades. Over time, twomain factors have ensureda rapid increase in neutron imaging capabilities and applications:(1) higher neutron fluxes at some facilities, and (2) advances in digitalimaging. As a result, thermal neutron fluxes can be as high as108 n cm−2 s−1, while the use of charge-coupled device (CCD) camerasallows for 2-dimensional (2D) real-time radiographs with spatial resolu-tions of up to ~15 μm (Table 1).

2.1. Neutron transmission radiography

Neutron transmission radiography (NTR) is a non-destructive, non-invasive 2-dimensional (2D) imaging technique based on the attenua-tion (absorption and scattering) of a neutron beam as it passes througha sample, as illustrated in Fig. 1. The resulting “flat” image is amap of theneutron attenuation within the sample under investigation. Neutronsinteract with the nucleus of the atom rather than with its electroncloud. The interaction forces between neutrons and nuclei are not corre-lated with the atomic number of the element, but instead depend uponthe particular isotope of the element (Anderson et al., 2009; Strobl et al.,2009). For example, neutrons are highly sensitive to light isotopes suchas 1H, 6Li, 10B, and rather insensitive to heavier isotopes such as 82Pb.

For a monochromatic (single wavelength) beam traversing a homo-geneous sample, themeasured intensity, I, is given by the Lambert–Beerlaw (Anderson et al., 2009):

I ¼ I0e−μτ ð1Þ

where I0 is the incident beam intensity, μ is the attenuation coeffi-cient in cm−1 and τ is the sample thickness. In the case of a polychro-matic neutron beam going through a heterogeneous samplecomprised of n elements, Eq. (1) becomes:

I λð Þ ¼Z λmax

λmin

I0 λð Þe −Σni¼1 τiμ i λð Þð �dλ½ ð2Þ

where λ is the neutron wavelength, τi is the thickness of element i,and μ i λð Þ ¼ σ i λð ÞmρNA

M is the linear attenuation coefficient of elementi, where σi (λ) is the microscopic cross section of element i, m isthe number of moles of a molecule, ρ is the density, M is the molec-ular weight, and NA is the Avogadro constant.

Both absorption and scattering influence the level of contrast in a 2Dimage. Imaging a thick sample with a polychromatic neutron beam canresult in artifacts due to beam hardening (Hassanein, 2006). As thebeam passes through the sample, its mean energy increases (i.e., it be-comes “harder”) because the lower-energy neutrons are preferentiallyabsorbed, leaving behind only the higher energy neutrons.

As indicated by Eq. (2), the contrast mechanism strongly dependsupon the radiation source, i.e. the range of neutronwavelengths availableat the beamline. Using the different neutronwavelengths at pulsed spall-ation sources it is possible to obtain multiple radiographs of the samesample, each with very different contrasts (a kind of “multispectral” im-aging known as time-of-flight imaging).

The following worked example illustrates the impact of two differentwavelengths on neutron transmission. First order approximations of theattenuation coefficients for water (H2O) in thermal (1.54 Å) and cold(9 Å) monochromatic neutron beams can be calculated based on Eq. (3):

μH2O¼ σ Hð Þ � 2� ρ H2Oð Þ � NA=M H2Oð Þ þ σ Oð Þ � ρ H2Oð Þ � NA=M H2Oð Þ ð3Þ

where σ(H) = 82 barn and σ(O) = 4 barn at 1.54 Å (National Institute ofStandards and Technology, 2013), σ(H) = 110 barn and σ(O) = 6 barn at9 Å (Brookhaven National Laboratory, 2013), ρ H2Oð Þ ¼ 1 g cm−3,NA = 6.022 × 10−23 mol−1, andM H2Oð Þ ¼ 18.02 g mol−1. The resultingvalues for μH2O

are 5.62 cm−1 and 7.55 cm−1 for thermal and cold neu-trons, respectively. Using these values in Eq. (1) gives the neutron trans-mission (I/I0) as a function of water thickness. Fig. 2 shows thetransmission curves for water in thermal and cold monochromatic neu-tron beams assuming no scattering effect. For any given water thickness,attenuation of the cold neutron beam is greater than with the thermalneutron beam.

Secondary scattered neutrons, as well as background from the envi-ronment, can also cause artifacts in the levels of contrast in radiographicimages (Hassanein, 2006). The errors produced by scattering and back-ground are often much larger than those due to beam hardening

Table 1Prominent neutron imaging facilities and their beam line characteristics.†

Country Institution Facility/instrument Reactorpower(MW)

Flux “on sample”(n cm−2 s−1)

L/D Spatialresolution(μm)

Field of view (rectangle,cm x cm; circle, cmdia.)

Australia‡ ANSTO§ OPAL (DINGO) 20 5 × 107 250–1000 20–30 20 × 20Austria Institute of Atomic and Subatomic

Physics/Vienna University of TechnologyTRIGA II 0.25 1 × 105 45–125 50–100 90 cm dia.

Brazil IPEN IEA-R1 5 1 × 106 55–110 NA 25 cm dia.Canada Royal Military College of Canada Slowpoke-2 0.02 3 × 104 100 140–300 20 × 20

60 × 60China‡ CIAE CARR 60 8 × 107 160–3000 150 10 × 10

PKU PKUNIFTY 4.5 5 × 105 25–200 300 20 × 20Germany HZB CONRAD (BENSC BER-II) 10 6 × 106 170–500 50 20 × 20

30 cm dia.FRM-II ANTARES 25 9 × 107 200–8000 50–200 40 × 40

20 cm dia.FRM-II NECTAR 25 3 × 107 150–300 600 30 × 30

20 cm dia.Hungary KFKI-AEKI WRS-M 10 6 × 105 100 NA 25 cm dia.Japan JRR-3M TNRF/TNRF-2 20 2.6 × 108 125–450 20–60 25 × 30

Kyoto University KUR 5 1.2 × 106 100 200 16 cm dia.Korea HANARO NR-port 30 1 × 107 190–270 NA 25 × 30South Africa NECSA SANRAD

(SAFARI-1)20 1 × 107 150–500 50–100 10 × 10; 13 × 13;

25 × 2536 cm dia.

Switzerland PSI SINQ(NEUTRA)

1.4 4 × 106 250–850 50–500 40 cm dia.30 × 30

SINQ(ICON)

1.4 1 × 107 100–10,000

20 15 cm dia.25 × 25

USA National Institute of Science and Technology NCNR BT-2 20 6 × 107 100–6000 15–50 10 × 1020 × 2525 cm dia.

Oak Ridge National Laboratory HFIR CG-1D 85 2 × 107 400–800 50–100 4 × 46 × 6

McClellan Air Force Base Nuclear radiationCentre

MNRC(TRIGA)

2 2 × 107 50–400 25–50 35 × 4323 cm dia.

Penn State University Radiation Science andEngineering Center(Penn State Breazeale Reactor)

2 3 × 107 50–100115–155

30–60 (2D)130–150 (3D)

23 cm dia.

Cornell University TRIGA Mark II 0.5 6 × 106 70–130 125–450 410 cm2

† Sources: Anderson et al. (2009), de Beer and Radebe (2012), IAEA (2009, 2013); ISNR (2010), Lehmann et al. (2011b), ORNL (2012); Deinert et al. (2005b); ITMNR-7 (2013).‡ Facility under development.§ Abbreviations:

ANSTO: Australian Nuclear Science and Technology OrganizationIPEN: Institute. De Pesquisas Energetiscas e NuclearesIAEA: International Atomic Energy AgencyHFIR: High-Flux Isotope reactorHZB: Hellmholtz-Zentrum BerlinCONRAD: Cold Neutron RADiographyANTARES: Advanced Neutron Tomography And Radiography Experimental SystemTNRF: Thermal Neutron Research FacilityHANARO: High-Flux Advanced Neutron Application ReactorFRM-II: Forschungs-Neutronenquelle Heinz Maier-Leibnitz research reactor Munich IICIAE: China Institute of Atomic energyPKUNIFTY: Peking University Neutron Imaging. Facility

OPAL: Open Pool Australian Lightwater reactorTRIGA: Training, Research, Isotopes, General Atomics reactorSLOWPOKE: Safe Low-Power Kritical ExperimentNCNR: NIST Center for Neutron ResearchSANRAD: South African Neutron. RadiographyKFKI-AEKI: KFKI-Atomic Energy Research InstituteJRR-3M: Japan Research Reactor No.3 ModifiedKUR: Kyoto University Research ReactorPSI: Paul Scherrer InstituteNFNBR: National Facility for Neutron Beam ResearchCARR: China Advanced Research ReactorNECSA: South African Nuclear Energy CorporationNA: Data not available

122 E. Perfect et al. / Earth-Science Reviews 129 (2014) 120–135

(Hassanein, 2006). Samples that are very thick or have a strong scatteringcross section (i.e., H2O) will have a greater probability that the scatteredneutrons will hit the detector. The angular distribution of the scatteredneutrons can result in significant deviations from the Lambert–BeerLaw, Eq. (1), depending upon the sample to detector distance (Radebeet al., 2011; Kang et al., 2013a).

The effects of secondary scattering are usually most pronouncedin samples with large water thicknesses located close to the detec-tor. Hassanein et al. (2005) indicated that errors due to secondaryscattering might be more than 45% for water with a thickness of4 mm. In contrast, Hussey et al. (2010) employed random uncer-tainty analyses based on neutron counting statistics and concludedthat scattering effects at the neutron imaging facility of the NationalInstitute of Standards and Technology (NIST), Gaithersburg, MD arelimited.

Different approaches have been proposed to remove scatteringeffects. One is to restrict measurements to very thin sections ofwater (Hussey et al., 2010) or very low water contents (Kim et al.,2012). Another approach is to correct for the scattering usingMonte Carlo modeling of point scattering functions (Pleinert et al.,1998; Hassanein et al., 2005, 2006b). The Quantitative Neutron Im-aging (QNI) software program was developed for this purpose andhas been shown to correct for nonlinearities in water calibrationdata (Radebe et al., 2011). However, modeling does not provide auniversal fix and alternative solutions such as experimental determi-nation of the scattering component need to be investigated(Hassanein et al., 2006b). Until a better solution is developed bythe neutron imaging community, investigators must rely on empiri-cal water thickness calibrations to take into account the beam hard-ening, secondary scattering, and background effects associated with


a particular beam line/sample configuration (Tumlinson et al., 2008;Kang et al., 2013a).

To minimize scattering artifacts, samples are often positioned a fewto several centimeters away from the detector. However, this protocolproduces an unsharpness effect which can be detrimental to the bestachievable spatial resolution. For static measurements (i.e., with nomotion unsharpness), two types of unsharpness are present: (i) the de-tector system unsharpness (due to optical components, such as lensesand mirrors at the detector), and (ii) the geometric unsharpness (dueto the beamline optics, i.e. cone beam geometry instead of ideal parallelbeam geometry).

The geometric unsharpness is ameasure of the loss of spatial resolu-tion. The highest achievable spatial resolution is obtained when a thinsample is placed against the detector (i.e., where the image is formed).The spatial resolution decreases as the sample is moved away from thedetector, either because of its thickness or to minimize scattering ef-fects. This loss in spatial resolution can be quantified by the geometricunsharpness, Ug, given by (ASTM, 2012: E94-04, E2698-10; ISO, 2013:19232-5):

Ug ¼ l=LD

� �½4�

where l is the sample to detector distance,D is the beam optics definingaperture diameter, and L is the distance between the aperture and thedetector. The L/D ratio is often referred to as the figure of merit for aneutron imaging beamline and corresponds to the apparent focal spotsize. In practice, the actual L/D ratio is different from the physical ratiobecause of wall scattering in the collimator or guide, which can affectthe anticipated resolution (Brenizer, 1992). A large L/D ratio improvesspatial resolution at the cost of flux, which is not always practical. Theprospect of using coded apertures (Skinner, 1984; Caroli et al., 1987) in-stead of a single aperture may allow for large L/D ratios without loss offlux, and ultimately promises high spatial resolution measurements(Xiao et al., 2009; Zou et al., 2011).

2.2. Neutron computed tomography

Computed tomography (CT) is a 3-dimensional (3D) reconstructionof an object based on a series of 2D projection images (radiographs) ac-quired at different angles; 0 to 180° assuming a perfectly parallel beam,or 0 to 360° for the best CT performance. This process is facilitated bymounting the object on a stage that can be rotated during imaging. CTreconstructions provide information about the 3D geometry/topologyof a sample, and the principles behind CT imaging are similar for bothx-ray and neutron sources. Neutron CT (NCT) can reveal either theinternal structure and texture of hydrogen-rich solid materials such as

Fig. 1. Layout of a typical neutron imaging beamline (Reprinted with permission fromNanda et al., 2012. Copyright 2012 American Chemical Society).

oily shale, or the 3D distribution and interconnectivity of hydrogen-rich fluids such as water and oil within the pore spaces of non-hydrogenous materials.

Neutron tomographic images are generally reconstructed assumingparallel beam projection (Vontobel et al., 2006). After normalization ofthe 2D radiographs, the Radon transform (Radon, 1917) is applied tothe data, producing sinograms. The results are then “back-projected” tothe sample and cross section (or reconstructed) slices of the sample areobtained. Fig. 3 shows the Radon transform, Pθ(t), of an object f(x,y) forparallel beam geometry and the corresponding Fourier transform,Sθ(ω), of Pθ(t) based on the Fourier slice theorem. The projections,Pθ(t), of an object f(x,y) rotated by an angle of θ along the beam pathare given by:

Pθ tð Þ ¼Z Z ∞

−∞f x; yð Þδ xcosθþ ysinθ−tð Þdxdy ð5Þ

where t = xcosθ + ysinθ. According to the Fourier slice theorem, the1-dimensional (1D) Fourier transform of a parallel projection is equalto a slice of the 2D Fourier transform of the object, i.e.

Sθ ωð Þ ¼Z ∞

−∞Pθ tð Þe−i2πωtdt ¼

Z Z ∞

−∞f x; yð Þe−i2πω xcosθþysinθð Þ dxdy ¼ F u; vð Þð6Þ

where (u,v) = (ωcosθ, ωsinθ), ω = frequency, and F(u,v) is theFourier transform of f(x,y). The object at the point f(x0,y0) can thenbe reconstructed by simply performing a 2-dimensional inverseFourier transform of the projection data, i.e.

f x0; y0ð Þ ¼Z Z ∞

−∞F u; vð Þe−i2π x0uþy0uð Þ dudv ð7Þ

The sequence of calculations described above is implemented in thefiltered back projection (FBP) algorithm, which is the most commonlyused CT reconstruction method (Kak and Slaney, 2001). An alternativetechnique, known as the iterative reconstruction algorithm, has beendeveloped to help reduce noise and improve image quality (Vontobelet al., 2006). Strobl et al. (2009) suggested using scans over the rangeof 0 to 360° for large samples and a cone-beamreconstruction algorithmto improve image quality due to deviations from perfect parallel neu-tron beam geometry.

The transverse images resulting from the reconstruction process arecombined to produce a 3D volume rendering of the sample. The 3D vol-ume rendering converts pixels to voxels using linear interpolation oftwo consecutive cross-section slices. Qualitative and quantitative dataanalyses are often undertaken on transverse, sagittal or coronal slicesobtained from the volume rendering.

Fig. 2. Transmission (I/I0) as a function of water thickness for twodifferent neutron energylevels.


As a polychromatic neutron beam passes through an object, its at-tenuation depends on the elemental composition of the material andthe energy spectrum of the neutrons. Low energy neutrons will nottransmit as much as higher energy ones, which causes deviationsfrom the Lambert–Beer Law referred to as beam hardening (seeSection 2.1). During data analysis, careful attention must be paid tobeam hardening and scattering effects, the image unsharpness, andthe evaluation of experimental errors (Hassanein et al., 2005).

3. Neutron imaging standards

Two groups are involved in producing standards relevant to neutronimaging, namely the International Organization for Standardization(ISO) and the International Committee for Non-Destructive Testing(E07) of the American Society for Testing and Materials (ASTM).Brenizer (1992) reviewed the standards produced by these two groupsup to and including the early 1990's. At that time ISO was consideringstandards for neutron radiography, but none had been produced.Since then ISO has published standards on the principles and rules forthermal neutron radiographic testing, 11537:1998 (ISO, 2013) and fordetermining the beam L/D ratio value in thermal neutron radiography,12721:2000 (ISO, 2013). ISO also has a standard for determiningimage unsharpness in radiographs that is applicable to neutron radiog-raphy: 19232-5:2013 (ISO, 2013).

The E07 Committee of ASTM currently has seven standards specificfor neutron radiography: E545-05, E748-02, E803-91, E1496-05,E2003-10, E2023-10, and E2861-11 (ASTM, 2012). These standards pro-vide introductory material, basic guidance, common practices, and testmethods related to film-based NTR. Other ASTM standard practicesand guides, such as E94-04 and E2698-10 (ASTM, 2012), provide usefulinformation on image quality, precision, and potential bias (e.g., geo-metric unsharpness, distortions, and backscattering due to variationsin object-to-film/source-to-object distances) for both film-based anddigital radiography.

While both ISO andASTMhave developed generic CT standards (ISO,2013: 15708-1/2:2002 and ASTM, 2012: E1441-11, E1570-11, E1672-12, E1695-95), there are currently no standards that deal specificallywithNCT. However, a recent initiative by the International Atomic Ener-gy Agency (IAEA) in collaboration with the Paul Scherrer Institut,Switzerland, Necsa, South Africa, and the Korea Atomic Energy ResearchInstitute, South Korea involves the evaluation of a set of test objects toprovide a standardizedmethod to quantify the tomographic capabilitiesof neutron imaging beamlines worldwide (Kaestner et al., 2013).

Fig. 3. (a) Radon transformation, Pθ(t), of an object f(x,y) at an angle of θ with t = xcosθ +projection Pθ(t), where (u,v) = (ωcosθ, ωsinθ) and ω = frequency.

4. Imaging studies on geomaterials

4.1. Rocks

In rocks some water may be incorporated into the mineral struc-ture of the solid phase. Neutron imaging has been applied to visual-ize the distribution of this “structural” water. For example, Winkleret al. (2002) utilized NCT to analyze hydrous mineral growth in peg-matite granite, basanite, and garnet-mica schist samples. Structuralwater does not contribute significantly to variable saturation. How-ever, it must be accounted for by normalizing images acquired dur-ing wetting or drying with respect to images of the initially-drysolid matrix.

Most of themobile water in rocks occurs in the void spaces betweenassemblages of mineral grains. The volume percentage of rock that isvoid space between grains is the primary porosity. In addition to the pri-mary porosity, fractures in rocks create secondary porosity. The totalporosity,ϕ, is the sum of the primary and secondary porosities, and typ-ically ranges between 0.02 and 0.30 (Table 2) depending upon factorssuch as rock type, diagenesis, weathering, and fracturing. The specificsurface areas of consolidated rocks are generally relatively small(Table 2). In contrast, some unconsolidated rocks can have specific sur-face areas in the same range as those for soils.

The intrinsic permeability, k, of rocks can range over several orders ofmagnitude (Table 2), depending on total porosity, pore-size distribution,pore shape, and pore connectivity. These factors are highly dependenton rock type, with unfractured igneous rocks generally having the low-est k values, and karst limestone the highest.

Over the past 20 yr or so, several studies have assessed the utility ofneutron imaging for visualizing the distribution of pore water in rocksunder equilibrium (or static) conditions. Kupperman et al. (1990) andRhodes et al. (1992) tested the feasibly of using neutrons to imagewater in tuff samples for permanent disposal of radioactive waste. Theyemployed a dual energy technique with relatively coarse spatial resolu-tion. Pleinert and Degueldre (1995) used NTR to determine the total po-rosity of crystalline rock (granodiorite and mylonite) samples saturatedwith water. The same group also used NTR (as a complementary tech-nique to positron emission tomography) for imaging a cylindrical coreof granodiorite rock into which holes of varying diameters had beenbored (Degueldre et al., 1996). These simulated pores were filled withhardened hydrogen-rich epoxy resin, allowing visualization of flowpaths and determination of the total porosity. Solymar et al. (2003a)used NCT to relate variations in water content after air flushing an

ysinθ assuming parallel beam geometry, and (b) Fourier transformation, Sθ(ω), of the

Table 2Typical value ranges for selected physical properties of various abiotic porous media.

Porous medium Solid phase description Specific surface area(m2/g)

Porosity(m3/m3)

Intrinsic permeability (m2) Sources

Bricks andceramicsa

Formed materials (e.g., building bricks, pottery)prepared by heating (and subsequently cooling)selected soil fractions (e.g., clay)/crushed rocksin the presence of water

0.6–12.3 0.19–0.43 5 × 10−18–3.3 × 10−14 Xu et al. (1997), Dondi et al.(2003), Kyritsis et al. (2009)

Concretea Construction material made by mixing togethercrushed rocks/selected soil fractions (e.g., sand),cement (a mixture of oxides of calcium, siliconand aluminum), and water

4.4–200 0.12–0.19 9.4 × 10−18–5.7 × 10−16 Lydon (1995), Tsivilis et al.(2003), Sani et al. (2005),Odler (2003)

Fuel cell gasdiffusionlayera

Typically carbon fiber paper or woven cloth thathas been wet-proofed with PTFE. Can be coatedon one side with a microporous layer formedfrom a mixture of carbon black, polymer binder,and hydrophobic agents.

1–120 0.44–0.90 9.6 × 10−14–1.6 × 10−9 Ihonen et al. (2004), Williamset al. (2004), Gostick et al.(2006), Holley and Faghri(2006), Song et al. (2006)

Heat pipe wicka Sintered powder, grooved tube or screen meshstructures fabricated using metal (e.g., steel,aluminum, nickel, copper) foams and felts, carbonfibers, polymers, and ceramics

0.06–75.0 0.15–0.97 7.3 × 10−13–1.7 × 10−9 Canti et al. (1998), Holley andFaghri (2006), Huang et al.(2009), Shkolnikov et al.(2010)

Porous glassa Glass containing silica (~96%), boron and sodiumoxides (~3%) produced by phase separation andliquid extraction with pores ranging between0.4 nm and 1 μm

70–200 0.28–0.35 0.2 × 10−19–1.8 × 10−19 Elmer (1992), Bentz et al.(1998), Gelb and Gubbins(1998), Gruener et al. (2012)

Rocksb Mixture of consolidated or unconsolidated primaryminerals, commonly including calcite, feldspar, mica,quartz, and/or silica

0.22–4.9 0.02–0.30 2.2 × 10−19–3.9 × 10−9 Churcher et al. (1991),Hammecker and Jeannette(1994), Labrie and Conlon (2008)

Soilsb Unconsolidated mixture of primary (e.g., feldspar,mica, quartz) and secondary (e.g., kaolinite,montmorillonite) minerals, plus organic matter

0.8–143.2 0.17–0.45 8.4 × 10−12–2.8 × 10−10 Currie (1966), Fish and Koppi(1994), Pennell et al. (1995)

a Man-made (engineered) material.b Naturally-occurring geomaterial.


initially water-saturated sandstone core at a pressure of ~50 kPa to thedistribution of coarse and fine laminae.

de Beer et al. (2004a) and de Beer andMiddleton (2006) determinedthe porosity of water-saturated sandstones using NTR. These authorsobtained a ~1:1 relationship between porositymeasured by NTR versusresults from a conventional measurement method for values of ϕ b20%(Fig. 4). Aboveϕ = 20% the data deviated from a 1:1 relation because ofthe detrimental scattering effects of neutrons onto the detector at highwater contents resulting in the underestimation of porosity (Fig. 4). deBeer et al. (2004a) also analyzed iron ores, which often containhydrogen-bearingminerals such as limonite and goethite, and compileda list of attenuation coefficients of elements and compounds relevant forimaging water in different rock types.

Many imaging experiments involving the movement of water andother fluids in rocks under dynamic conditions have been done with ei-ther the preservation of natural building stone in mind, or for purposesof reservoir characterization in petroleum engineering. Jasti et al. (1987)were the first to acquire images of water moving in rock pores using dy-namic NTR. These authors flooded an initially mineral oil-saturatedBerea sandstone core with water in order to observe the migration ofthe immiscible front, while Jasti and Fogler (1992) recorded fluid distri-bution changes due to a miscible tracer pulse in flooding experimentsperformed on Berea sandstone cores.

Middleton and Pàzsit (1998) and Sváb et al. (2000) used dynamicNTR to investigate oil displacing heavy water in samples of Visingsösandstone from Sweden. Members of this same research group alsopresented three different petrophysical applications of neutron imaginginvolving water movement in rocks (Middleton et al., 2001). Theexperiments imaged were: (1) vertical water infiltration in to aninitially-dry porous rock, (2) oil flooding of a sandstone rock initially-saturated with heavy water, and (3) water flooding of an initially-dryclay-rich rock. Solymar et al. (2003b) performed oil–water immiscibledisplacement experiments at the same facility. The sampleswere Green-sands (glauconite sandstones) from the North Sea, which are of interestas oil reservoirs. Flow of oil displacing water through the pore space of

the samples was recorded by a low-light TV camera connected to asuper VHS recorder. Images were collected into stacks of 10 over~0.5 s, a time resolution better than the fluid front advancement, andstored as 8-bit grayscale images. Piston-like displacements were ob-served in samples with narrow pore-size distributions, while flowchanneling occurred in more heterogeneous samples.

Middleton et al. (2005) investigated spontaneous imbibition ofwater into air-filled Mardie Greensand and Barrow group sandstonesamples using dynamic NTR. The observed data were fitted to a simplediffusion equation with a constant diffusion parameter. In addition tothe static porosity measurements discussed previously, de Beer andMiddleton (2006) also imagedwater displacing oil in Fontainbleu sand-stone using a Hassler Cell setup. “Heavy water” (D2O) was used insteadof H2O, to enhance the detectable neutron intensity contrast betweenthe two fluid phases. The relative concentrations of the two fluidscould be quantified yielding the position of the D2O front as a functionof time.

In another dynamic study, Hassanein et al. (2006a) imaged the cap-illary imbibition of deionized water and a 20% solution of NaCl intoinitially-dry rock samples (Mansfield sandstone, Salem limestone, andHindustan whetstone) of various sample sizes, up to 40 cm. The move-ments of the fluids were imaged over several hours, with exposuretimes of 15–25 s per frame and a pixel resolution of 272 μm. Scatteringeffects caused the water contents to be underestimated. Following cor-rection by Monte Carlo modeling of the point scattered function, thewetting front position was plotted as a function of the square root oftime, the slope of which yields the sorptivity, S. Their results show var-iations in S as a function of rock type, sample size, solution type, andmode of imbibition (top down versus bottom up). Cnudde et al.(2008) further explored the usefulness of high-speed NTR for quantify-ing water uptake in porous rocks by capillarity.

Kang et al. (2013b) estimated the sorptivity andunsaturateddiffusiv-ity of Berea sandstone from neutron radiographs acquired continuouslyduring spontaneous imbibition. Their estimates appear to be the first re-ported values of these hydraulic parameters for this important rock type,


which is usedwidely as a standard for petrophysical investigations in thegeology and petroleum engineering fields.

Polsky et al. (2013) investigated the applicability of neutron imagingfor enhanced geothermal system applications. They visualized an air–water interface moving within a fracture in granite. Radiographic im-ages were acquired with a resolution of approximately 300 μm and10 ms exposure time. Relatively high fidelity flow structure detailscould be resolved, including the curvature of the interface. Hall (2013)employed time lapse NTR to quantify differential water imbibitioninto an air dry sandstone core. Local fluid flow velocities, extractedfrom image analysis of the neutron radiographs, indicated that waterflow was faster within a compactant shear band. This behavior wasattributed to higher capillary forces associated with damage such as in-creased microcrack density.

In terms of NCT, several experiments involving the dynamics of fluidflows in rocks have been reported. Masschaele et al. (2004) and Diericket al. (2005) presented tomographs for water and other fluids movinginto limestone and sandstone rock samples, some of which had beentreated with a water repellent. Each tomograph was constructed from100 projections, 10 frames/s over 10 s, at a resolution of 8 pixels or1.5 mm. The dynamic processes imaged were relatively slow comparedto the high speed imaging capability. The capillary imbibition of waterrepellents, consolidants (fluids, which upon hardening, reestablish co-hesion between particles of deteriorated building stone), and gasoline,as well as the effects of water migration on the porous rocks, wereshown as examples.

Hameed et al. (2009) and Zawisky et al. (2010) conducted neutronimaging experiments (both NTR and NCT) to compare the uptake oftwo different consolidants by porous building stone used in historicbuildings. Samples were soaked in a bath of consolidant. Uniform pene-tration of the consolidant into the samples was expected. However,neutron imaging revealed that the consolidant was heterogeneouslydistributed, with pronounced surface effects (Zawisky et al., 2010).

Wilding et al. (2005) reported several geological applications of neu-tron tomography. In terms of fluid flow, these authors presented NCTreconstructed images of CO2 reaction fronts in carbonate rocks. Theyalso investigated water flow into a fractured volcanic ash deposit, theBandelier Tuff, from Los Alamos, NM. The hydrologic properties of thisrock are of interest in addressing the environmental impacts of possibleradioactive nuclide contamination at Los Alamos National Laboratory.Ten mL of water was added to the top of the core sample and allowedto migrate into the matrix. The 250 μm voxel size was larger than theaverage pore size in the sample. The resulting tomograph image showedpreferential flow of water along a deformation band comprised of fine-grained material.

Fig. 4. Total porosity of sandstone samples measured using neutron radiography (NRad)compared to values obtained using a conventional method. Solid and dashed lines repre-sent the best fit linear regression equations for ϕ b20% and N20%, respectively (De Beeret al., 2004a).

4.2. Soils

The solid phase of soil is comprised of primary and secondary (clay)minerals, plus organicmatter (Table 2). Themineral particles are classi-fied as sand, silt, and clay based on their size range: 0.05–2.0, 0.05–0.002, and ≤0.002 mm, respectively. The specific surface area of soilcan be highly variable depending on the relative proportions of thesesize fractions (Table 2). The pore spaces between soil particles are filledwith varying amounts ofwater and air. The total porosity of soil normal-ly ranges between 0.17 and 0.45 (Table 2). Coarse-textured soil gener-ally has less porosity than fine-textured soil, even though its meanpore size is larger. Koliji et al. (2008) have used neutron tomographytomeasure changes in the porosity of soil aggregates caused by externalmechanical loading. Typical values for the intrinsic permeability of soil,corresponding to the fully-saturated condition, are given in Table 2.Under variably-saturated conditions, permeability decreases rapidly,and in a non-linear fashion, with decreasing soil water content. Aswith rocks, neutron imaging of mobile water requires that correctionsbemade to account for the presence of any hydrogen in the solid phase.

Neutron imaging has been employed to investigate both the staticsand dynamics of soil water, using NTR and NCT. The technique wasfirst applied to soil in the 1970's when Wilson et al. (1975) and Lewisand Krinitzsky (1976) compared radiographic images of soil obtainedbyNTRwith those determined by using x-ray units.Most neutron imag-ing studies of soil water have employed thermal neutrons. D2O is some-times substituted for H2O (e.g., Papafotiou et al., 2008) because itattenuates neutrons ~7× less than normal water (H2O), allowing forthe use of thicker samples.

In terms of statics, Lopes et al. (1999) appliedNCT to observe the dis-tribution of water within compacted soil. NCT has also been used to vi-sualize and quantify static distributions of water in glass beads(Lehmann et al., 2006). Kim et al. (2012) used NTR to study the staticdistribution of thin films of water in a partially-saturated sand column.

Neutron imaging has also been employed to determine the soilwater retention curve under quasi-equilibrium conditions. Deinertet al. (2005a) and Tumlinson et al. (2008) extracted water retentioncurves from the static distributions of water within sand columns im-aged usingNTR andNCT, respectively. Vasin et al. (2008) obtained aver-age drainage curves for columns of coarse and fine sand, as well as fortwo heterogeneous sand columns comprised of these two sands packedin random and periodic grid arrangements, using NCT performed underquasi-equilibrium conditions. Fig. 5 shows changes in the 3D distribu-tion of water within the randomly packed column during a monotonicdrainage sequence. Cheng et al. (2012) used NTR to quantify hysteresisin the average water retention curve for a sand column under quasi-equilibrium wetting and drying conditions. These authors encountereddiscrepancies in NTR-determinedwater contentmeasurements relativeto independent hanging water column data. The discrepancies weresimilar to those reported by de Beer and Middleton (2006) andHassanein et al. (2006a), and were likely due to scattering associatedwith large water thicknesses in the center of the saturated column.Theywere effectively removed byworkingwith relative (water) satura-tions rather than volumetric water contents. Recently, Kang et al.(2014) applied NTR to determine multiple pixel-scale (or point) waterretention curves for a single sand column.

Neutron imaging has also been applied to investigate the dynamicsof water flows in soil. Clarke et al. (1987) tracked the movement ofwater and development of ice lenses during soil freezing using NTR.Brenizer and Gilpin (1987), and later Deinert et al. (2002, 2004), usedreal time NTR to quantify the advance of wetting fronts into initiallydry sand columns. Tullis et al. (1994) and Tullis and Wright (2007)used NTR to study unstable finger flows in a layered soil (fine overcoarse sand). Hincapié and Germann (2009, 2010) investigated fingerflows during gravity-driven infiltration in unsaturated sand boxes.Gilbert and Deinert (2013) developed a method for determining radialand vertical water content profiles within axisymmetric preferential


flow fields using NTR. The transient infiltration of water into packedbeds of soil aggregates (Carminati et al., 2007a,b; Carminati andFlühler, 2009) and within the rhizosphere around plant roots (Oswaldet al., 2008; Carminati et al., 2010; Warren et al., 2013) has been thefocus of several studies using neutron radiography. In other dynamicapplications, NTR has been employed to investigate water imbibitioninto granular zeolite beds (Żołądek et al., 2008) and water content dy-namics during evaporative drying (Shokri et al., 2008, 2009, 2010;Lehmann and Or, 2009; Fijał-Kirejczyk et al., 2011).

In terms of NCT, several studies have imaged and quantified the porescale, 3D, spatial distribution of soil water resulting from quasi steady-state flow conditions. Most of these studies have focused on artificially-packed, heterogeneous, sand columns (Kaestner et al., 2006; Papafotiouet al., 2008; Schaap et al., 2008). Recently, however, Badorreck et al.(2010) have employed NTR and NCT to visualize the water flow patternsin mine soils with natural heterogeneities.

5. Imaging studies on engineered porous media

5.1. Bricks and ceramics

Ceramic materials are man-made porous media produced frompowders by the action of heat (sintering) and subsequent cooling.They are used in a wide range of engineering applications, includingfor example semi-conductors and disk brakes. Bricks are blocks orunits of a ceramic material used in masonry constructions. Because oftheir strength and durability they have been widely used as buildingmaterials throughout history. Bricks usually contain silica sand, clay,lime, iron oxide, and magnesium oxide. Various types of bricks (e.g.burnt clay bricks,fire clay bricks,mud bricks, dry pressed bricks, extrud-ed bricks, concrete bricks, and ceramic bricks) can be found in masonrydepending on themanufacturingmethods used. Typical porosity valuesfor ceramics and bricks range from 0.19 to 0.43 (Table 2). Because of thesmall pore sizes produced by the sintering process intrinsic permeabil-ities are usually relatively low (Table 2).

Humidity and moisture in such materials are critical to the strengthand resilience of building structures. The presence ofmoisture can causebricks and ceramics to deteriorate over time (Pleinert et al., 1998;Nemec et al., 1999; Janz, 2002). Thus, it is not surprising that neutronimaging has been employed since the early 1990's to evaluate themois-ture status of various building materials. All of these studies haveinvolved dynamic imaging of water movements.

Prazak et al. (1990) first used NTR to document water uptake inthree different types of ceramic slabs: vacuum pressed ceramic,limesand brick, and aerated concrete. They concluded that the effectivediffusivity (with dimensions of L2T−1) cannot be regarded as a materialcharacteristic because of its strong dependence on initial and boundaryconditions. Amodel, with a combination of capillary and diffusive trans-port mechanisms, was proposed to explain the experimental wettingand drying profiles. Pel et al. (1993) applied dynamic NTR to determine

Fig. 5.Quasi-equilibriumneutron tomography images of relative (water) saturation, S, in a sandmatric potentials (from left to right) of−10,−20, −30, −40, and −50 cm during a monoton

the moisture diffusivity from water content profiles measured on claybrick and kaolin clay. Their experiments were conducted under con-trolled drying conditions.

Pleinert et al. (1998) employed NTR to quantify water uptakein brick samples. The surfaces of the brick samples, which were orthog-onal to the beam path, were sealed. These authors also employedinverse numerical simulations to estimate a moisture content depen-dent transfer coefficient (with the same dimensions as diffusivity)from the imaged water content profiles. Both molecular diffusion andcapillary pressure mechanisms were considered in calculating thetransfer coefficient. Islam et al. (2000) studied the water adsorptioncharacteristics of some Bangladeshi and Slovenian building materialsusing NTR.

Buried building materials often need sealing to prevent or limitgroundwater from seeping into the base of the building. Nemec et al.(1999) applied fast, quasi-real-time NTR to study the impregnation ofsilicone-based hydrophobic agents in clay bricks. They quantified theconcentrations of two hydrophobic agents in samples and then com-pared the penetration of water in the treated samples. Their results in-dicated that moisture penetration due to capillary and diffusionprocesses was evident even at long wetting times. In addition, theynoted that the relative error in the concentration profiles (about 5%)was determined mostly by error in the calibration.

El Abd et al. (2005) utilized NTR to study the capillary motion ofwater in porous construction materials including bricks (Fig. 6).Czachor et al. (2002) employed amodel, representing the porousmate-rial as a collection of capillary tubes with various radii, to describe liquidtransport. Their results for siliceous bricks suggested that the smallestdiameter capillary tubes determine the upper edge of the wetting pro-file based on neutron imaging data. El Abd et al. (2009) studied waterdiffusivity in fired clay bricks. Acrylic paint was applied to the sides ofthe samples to prevent evaporation and allow water to move in one di-rection. The water level in the immersing reservoir was kept constantand covered ~3 mm of the immersed sample ends. Fickian diffusion(scaling with the square root of time) was observed. In contrast, anom-alous diffusion was reported for clay brick (super-diffusive) and silicatebrick (sub-diffusive) materials (El Abd and Milczarek, 2004). A powerlaw fit, based on the analytical model of Meyer and Warrick (1990),seemed to be better for addressing uptake in the low water content re-gion than the linear ratio fit. Milczarek et al. (2005) studied mass andheat transfer in bricks using dynamic NTR and reported a self-diffusion coefficient for water (with dimensions of L2T−1) and an imbi-bition rate parameter (with dimensions of LT−0.5). Exposure times fortheir images ranged between 0.6 and 2.5 s. The imbibition rate param-eter was found to vary linearly with the temperature. Another studyby Milczarek et al. (2008) was conducted on fired clay brick withwater in the 30–50 ° C temperature range. Their results showed a de-crease in the diffusion constant to ~17% of its value for free space. TheArchie exponent, which relates to the resistance increase caused bythe pore network, was estimated to be 1.4. The Arrhenius law was

column, comprised of randomly-packed cubic inclusions of coarse andfine sand,with basalic drying sequence (Vasin et al., 2008).


applied to these data and the resulting activation energy was found tobe the same value as for bulk water. Fijał-Kirejczyk et al. (2009) studiedtemperature–time–water content relations during drying of cylindricalsamples of kaolin clay using NTR.

Neutron imaging has also been used to visualize the distribution offluids in ceramic artifacts for archeological applications. Most recently,Prudencio et al. (2012) applied NCT to visualize the penetration depthand distribution of a polymer-based consolidant for the assessment ofimpregnation efficiency in ancient glazed tiles. Quantification ofconsolidant mass applied to ancient tiles was estimated based on thegray level of the tomographs.

5.2. Concrete

Concrete is a composite material composed of water, aggregates in-cluding gravel, sand, and crushed rock, and cement which binds the ag-gregates together. The dry cement and aggregate aremixed andwater isadded. As the concrete dries it hardens, increasing its compressivestrength while decreasing its porosity and permeability. Typicalporosity values for cured concrete range from 0.12 to 0.19 (Table 2).In the absence of fractures the intrinsic permeability is typicallybetween 10−18 and 10−16 m2 (Table 2).

Although neutron imaging lends itself quite well to imaging staticwater distributions, examples of this approach in concrete are quiterare. Reijonen and Pihlajavaara (1972) utilized NTR to detectwater pro-duced by the carbonation process in concrete and the associated thick-ness of the carbonated layer. Howdyshell (1977) determined the watercontent of fresh concrete using NTR. Other static hydraulic properties ofconcrete estimated using neutron imaging include total porosity byNTR(Zeilinger and Huebner, 1976; de Beer et al., 2004b; de Beer et al., 2005;de Beer and Middleton, 2006) and NCT (Brew et al., 2009; andMcGlinnet al., 2010), and pore-size distribution (Brewet al., 2009;McGlinn et al.,2010) by NCT. Pugliesi and Andrade (1997) usedNTR to visualize cracksin concrete samples that had been subjected to a compressive strengthtest. In order to enhance crack visualization, the samples were wettedwith an aqueous solution of gadolinium nitrate, and then dried.

Dynamic applications found in the literature deal primarily withvisualizing and quantifying changes in water content over time due toeither the drying of wet concrete or the movement of external waterinto dry concrete. de Beer et al. (2004b) employed NTR to observe theredistribution of water during the natural drying process immediatelyfollowing curing. Saturated lightweight aggregates can serve as reser-voirs during the drying process, transferring water from the aggregate

Fig. 6. Raw neutron transmission images showing the horizontal movement of water(dark gray) into initially dry columns of: (a) clay and (b) siliceous bricks (light gray) at dif-ferent times (©2005 IEEE. Reprinted, with permission, from El Abd et al., 2005).

to the surrounding matrix. This transfer of water was investigated byMaruyama et al. (2009), using NTR, and Trtik et al. (2011), using NCT,with similar results. Both studies showed water migration of at least3 mm from the aggregate into the surrounding matrix.

Zeilinger and Huebner (1976) visualized moisture migrationwhen heatwas applied to one end of amoist sample. The authors calcu-lated the vapor diffusion coefficient and the mass transfer coefficientbased on the acquired moisture profiles. More recently, Milczareket al. (2005) investigated vapor transport due to boiling of water withina concrete sample.

When wetting by capillary suction is utilized, the sorptivity ofthe sample is typically calculated. Hanziç and Illic (2003) deter-mined that the relationship between the height of capillary rise(or the volume of liquid absorbed per unit area) and the squareroot of time only holds true for times b60 h. de Beer et al. (2004b)and de Beer et al. (2005) compared traditionally measured porosityand sorptivity values with those obtained by NTR and found goodagreement between themethods. Brew et al. (2009) provide anotherexample where sorptivity values obtained by NTR agreed well withthose obtained by the traditional gravimetric method. In this studythe authors attempted to correct for neutron scattering by applyingaMonte Carlo model based on the Point Scattered Function approachdeveloped by Hassanein et al. (2006a). McGlinn et al. (2010) investi-gated the rate of water penetration into dry concrete samples, withcapillarity as the driving force, using neutron tomography.

In other studies, changes inmoisture content and distribution due towater entering fractures have been the primary concern. Kanematsuet al. (2009), Wittmann et al. (2010) and Zhang et al. (2010a,b, 2011)investigated water uptake into cracked concrete and its effects on thedeterioration of steel reinforcements (Fig. 7). In the case of reinforcedor strain-hardened concrete, lower water to cement ratios may lead toearly cracking of the concrete.

Other dynamic hydraulic properties of concrete estimated usingneutron imaging include the permeability of a samplewithin a pressurecell (Dawei et al., 1986), unsaturated diffusivity (Prazak et al., 1990),and the uptake of water when a hydrophobic agent has been appliedto the concrete (Zhang et al., 2010a,b, 2011).

5.3. Fuel cells

A fuel cell is an electrochemical energy conversion system that gen-erates electricity from chemical reactions. The style of fuel cellmost typ-ically imagedwith neutrons is the low temperature polymer electrolytefuel cell which operates below 90 °C allowing liquid water to exist. Thisdevice can contain several layers of porous material. The layer of inter-est for this review is the gas diffusion layer (GDL)which provides liquidwater removal from the catalyst layers to the gas channels whileallowing for even gas distribution along the catalyst. The GDL typicallyranges in thickness from 100 to 400 μm and is constructed of carbon fi-berswith diameters of ~10 μm. Total porosity and intrinsic permeabilityvalues for GDL's are generally quite high (Table 2). There are two basicforms of the GDL: paper and cloth. The paper GDL is a stiff materialformed by the random orientation of carbon fibers that are bonded to-gether through a graphitization process. The cloth GDL is amore flexiblematerial constructedwithwoven bundles offibers. GDL's are often coat-ed with a layer of polytetrafluoroethylene (PTFE) tomake the pores hy-drophobic. However, variability in the coating process can result in aheterogeneous distribution of hydrophilic and hydrophobic regionswithin the layer.

Fuel cells continuously producewater during operation thatmust beremoved from the cell. Due to the dynamic nature of this process nostudies have been found that deal with the statics of water in the GDL.Instead high spatial resolution NTR is employed to elucidate changesin the liquid water saturation of the GDL based on changes in operatingconditions, such as temperature, current density, pressure, and humid-ity. Testing is typically performed at constant current operation which


allows for constant water production. High resolution NTR permits theuse of standard fuel cell materials such as aluminum and graphite.This allows for more realistic thermal boundary conditions on the cellwhen compared to x-ray radiography which achieves higher resolutionthanNTR but requiresmaterials such as polymers that can alter temper-ature gradients in the cell.

One of the earliest examples of neutron imaging of fuel cell diffusionmedia was the study by Satija et al (2004). However, due to limited de-tector resolution the in-plane direction of the fuel cell was imagedwhich agglomerates all of the liquid water in the cell together. Thismakes it difficult, if not impossible, to uncouple the liquidwater contentof each of the individual layers. In-plane imaging has been the focus ofseveral papers such as Turhan et al. (2008), Cho et al. (2009), Owejanet al. (2009), and Spernjak et al. (2009) to list a few. It wasn't until theadvent of high-resolution detectors in the range of 25 μm thatthrough-plane imaging was made possible. This new approach allowsfor the detection of saturation profiles through each individual layer.Hussey et al. (2007) focused on the initial testing of a high spatial reso-lution detector capable of imaging the through-plane direction of thefuel cell. Other groups have also presented new detectors and tech-niques with initial images of fuel cell water contents (Boillat et al.,2008a,b; Lehmann et al., 2009; Boillat et al., 2011; Murakawa et al.,2011; Yasuda et al., 2011; Mishler et al., 2012). While the majority ofimaging facilities used for fuel cell research are thermal beams whichallow for greater material penetration, some researchers such asBoillat et al. (2008a) have used cold imaging lines to improve contrastof small quantities of water.

One of thefirst quantitative analyses of through-planewater contentdynamics in fuel cells was conducted byHickner et al. (2008). These au-thors developed a specialized fuel cell adapted for high resolution NTR.The aim of this studywas to investigate the effects of inlet gas humidity,cell temperature, gas flow rate, and current density on liquid water pro-files through the cross section of the cell. Condensation was found tooccur in the GDL as the microporous layer restricts liquid transort.This was inferred by themaximum inwater thickness found at the cen-ter of the GDL as shown in Fig. 8. The conclusion byHickner et al. (2008)of vapor transport to the GDL, where water then condenses, was latermodeled by Weber and Hickner (2008). The simulations revealed astrong heat-pipe effect (see Section 5.4) within the fuel cell. This effect

Fig. 7. Neutron radiograph showing preferential upward movement of water (dark gray)into fractured reinforced concrete (light gray) after one hour of wetting (adapted fromZhang et al., 2011).

occurs when water transport is augmented by a temperature gradientin the direction of flow.

Kim andMench (2009) developed a fuel cell based on the hardwareused by Hickner et al. (2008) and Weber and Hickner (2008) thatallowed for more precise control of the thermal boundary conditions.The anode and cathode temperatures were controlled independentlywith heating/cooling circulators. Temperature gradients were then ap-plied to the cell while neutron imaging took place. Ex situ tests wereconducted where the side channels were filled with water and imageswere taken to determine leakage rates. Flow only occurred when thehot side was on the water side and the cold side was on the dry side.

Hatzell et al. (2011) utilized the same cell configuration as Kim andMench (2009) to test the influence of the temperature gradient onphase-change induced flow. Itwas found thatwater transport increasedwith increasing temperature gradient. Ex situ testswere used to providefurther validation that the microporous layer inhibits liquid watertransport.

Turhan et al. (2010) probed the effects of gas channel surface energyon GDLwater content. Tests were conducted with hydrophobic and hy-drophilic treatedflow fields. The investigators found that the hydrophil-ic channels helped to pull water out from the GDL above the channelribs, but this made drying during purge more difficult. Tabuchi et al.(2010) tested a small 1 cm2 cell with multiple flow field arrangements.Straight channels with different rib channel widths were tested to de-termine how the channel/rib ratio affected the cross-sectional waterdistribution in theGDL. Larger rib sizeswere found to reduce cell perfor-mance due to the collection of water in the GDL above these areas. Thisincrease in liquid water content reduced gas phase transport limitingreaction rates. Cho and Mench (2010) employed high-resolution NTRto determine the drying effectiveness of different purge cycles as a func-tion of the channel/rib ratio. Their results showed that a compositepurge cycle, comprised of an initially high flow rate to remove largewater droplets followed by a low flow to dry the GDL by evaporation,provided the optimal condition for low energy purge and start-upreliability.

Manahan et al. (2011) proposed changes to the GDL to increase fuelcell performance. These researchers used an ytterbium fiber laser toperforate the GDL with larger (300 μm) diameter pores to facilitatewater movement. NTR indicated the perforations enhanced cell perfor-mance under low humidity inlet gas and low current density operation,b1.4 A/cm2. However, above this current density and at high humidityconditions, the perforations had a negative effect on cell performance.The larger pore spaces collected and retained water most likely due tothe lack of hydrophobic coating in the heat affected zone around theperforations. Water exchange between freshly generated water and ac-cumulated water within the cell can be measured by hydrogen–deute-rium contrast neutron radiography. Manke et al. (2008) collectedinitial radiographs of a fuel cell running on hydrogen gas after whichthe gas stream was switched to deuterium. Once the switch occurred,any freshly generated water would be heavy water which is nearlytransparent to neutrons compared to light water. Due to the contrastdifference between light and heavy water, the attenuation from waterin the cell would decrease as light water was replaced with heavywater. It was found that at low current densities a simple one-phaseconvective model was sufficient to predict diffusion. At higher currentdensities this model did not agree with experimental data showingthat a faster water removal process, similar to Haines jumps, waspresent.

Boillat et al (2008b) used hydrogen–deuterium labeling to measureexchange rates between hydrogen gas and protons in the membrane.It was found that the exchange rate was higher than literature valueslikely due to a higher exchange current density than is measured by tra-ditionalmeans. Cho andMench (2012) investigated the role ofmicropo-rous layers (MPL) with hydrogen–deuterium contrast. It was found thatlight water replacement occurred primarily on the cathode side wherewater is generated with no MPL but changes occurred on both sides

Fig. 8. Neutron radiographic cross-section through an operating fuel cell. Low water con-tents are denoted by the blue/purple colors, highwater contents are denoted by the pink/red colors. The pixel columnnumbers are labeled for the approximate locations of the cen-ter of themembrane (212), themacroporous layer/microporous layer interfaces (205 and219), and the boundaries of theGDL (185 and 235). The anode gas flowchannels are to theleft of pixel column185, and the cathode gas flow channels are to the right of pixel column235 (Hickner et al., 2008; Reproduced by permission of ECS - The Electrochemical Society).


with MPL present. This indicates that the MPL provides a barrier towater removal on the cathode and thus pushes water towards theanode.

Three-dimensional neutron tomography has been used to decouplechannel and diffusionmediawater content of ex situ (no reaction occur-ring) fuel cells by Sakata et al. (2009), Tang et al. (2010), Takenaka et al.(2011), Markötter et al. (2012), and Santamaria et al. (2012). Due to thelength of time required to acquire all of the images from 0 to 180° ofrotation to reconstruct the fuel cell, the fuel cells are usually shutdownto keep water in place. Image resolution is typically in the order of 75to 200 μm for this technique to allow for high temporal resolution. Be-cause spatial resolution is on the order of the diffusion media thickness,channel water content is of primary interest in these studies.

5.4. Heat pipes

Heat pipes are devices for transferring heat from one location to an-other based on the phase change of a working fluid (often water). Theworking fluid evaporates to remove heat from the hot end; the vaportravels the length of the tube where the heat is dissipated at the coldend through condensation. To produce a closed cycle where the work-ing fluid is reused, a porous wick is added to the inside of the pipe tomove the condensed working fluid from the cold end back to the hotend through capillary action. Wicks for heat pipes are made out ofmetal, carbon fibers, polymers, or ceramics with a sintered powder,mesh, or grooved structure along the interior walls. They can have alarge porosity range, from as low as 0.15 to as high as 0.75, with perme-ability values on the order of 10−9 to 10−13 m2 (Table 2).

Heat pipes are inherently dynamic in their operation and, perhapsnot surprisingly, the literature contains no examples of the use of neu-tron imaging to visualize the static distribution of water within a wickstructure. Instead the main research focus has been on dynamic NTRof fluid flow patterns and the distribution of vapor inwicks. Some initialstudies were qualitative in their analysis, looking at the spatial distribu-tion of the working fluid (Tamaki et al., 1986), or only mentioning that

neutron imaging of a working heat pipe had been done (Matsumotoet al., 1986). Others attempted to quantify vapor distribution by lookingat vaporization within the wick structure (Moss, 1967; Balasko et al.,1986), as well as water thickness and vaporization within differentwick structures at various inclinations (Moss and Kelly, 1970).Cimbala et al. (2004) used NTR to visualize partial wick dry-out and dis-tribution problems in the coolingwater system,which can limit the per-formance of a heat pipe system (Fig. 9).

A study done by Yoon (2008) looked at the dynamics of vapor andliquid flow in oscillating heat pipes by comparing volume fraction datawith temperature data at various points in the heat pipe. Wilson et al.(2008) compared flow patterns of aqueous solutions containing differ-ing amounts of diamond nanoparticles and observed the circulation ofvapor bubbles and fluid plugs. Shifts in the filling ratio of an oscillatingheat pipe system were found to induce changes in flow motion andheat transfer (Borgmeyer et al., 2010). Sugimoto et al. (2009) imageda self-vibrating heat pipe consisting of a meandering capillary channelwith butane as the working fluid. The behavior of the butane was ob-served by video imaging at 200 frames per second. Vibrationswere gen-erated (due to the pressure differential) as heat was transported fromhigh- to low-temperature regions.

Liquid plug formation, which can block vapor passage and decreaseperformance, has been investigated by Sugimoto et al. (2011) andWilson et al. (2011). Sugimoto et al. (2011) eliminated a liquid plugformed in a bend in the heat pipe tubing by inserting a thin metalplate. Thompson et al. (2011) studied the effects of various Tesla-typevalves on circulation and flow behavior in an oscillating heat pipe, con-cluding that the addition of the Tesla-type valves promoted circulatoryflow and lowered thermal resistance.

5.5. Porous glass

Porous glass contains ~96% silica (as compared to common quartzglass, fused quartz, ~63% silica) and features an interconnected porousmicrostructure (Table 2). It is one of the most common nano-engineered materials along with carbon, silicates (zeolites), and poly-mers. Porous glass is generally made through a process of phase separa-tion and liquid extraction. It can be produced in high quality with meanpore sizes of ~0.4 nmup to ~1 μm, andwith a very narrowdistributionalrange (Table 2). Due to its unique properties (i.e., high chemical, thermaland mechanical resistance) and functionalization of the inner surface,porous glass is used for a wide range of applications in science and engi-neering (Elmer, 1992; Gelb and Gubbins, 1998; Yao et al., 2003).

To date only one study has appeared in which neutrons have beenutilized to image water within porous glass. Gruener et al. (2012) ap-plied neutron radiography to investigate the anomalous behavior ofthe wetting front width during spontaneous imbibition of water in ananoporous glass (Vycor) with elongated pores. The quantified datafrom the neutron imaging were compared against results from a theo-retical pore-network model and numerical simulations. The wettingfront followed the well-known square root of time scaling. However,the interface width was strongly dependent upon the pore aspectratio (Fig. 10). Large values of this ratio (i.e., more elongated pores) in-hibit the formation of a connected vapor–liquid interface and lead to arapid broadening of the imbibition front. These authors also notedthat neutron imaging permits observation of the advancing front deepwithin the matrix, which is usually difficult to do by other means(e.g. NMR).

6. Discussion and future directions

It has been shown that neutron imaging (by transmission radiogra-phy and/or computed tomography) can be a powerful non-destructivetool for visualizing hydrogen-rich fluids within diverse porous media(both natural and engineered), under static and dynamic conditions.The value of neutron imaging in determining physical and hydraulic

Fig. 9. Neutron radiograph of an operating heat pipe. The indentations (indicated byarrows) on either side of the evaporator show that liquid water (dark gray) has beenreplaced by vapor (light gray) signifying partial wick dry-out (Cimbala et al., 2004).

Fig. 10. Progression and broadening of the imbibition front of water in nanoporous Vycorglass (with a pore aspect ratio between 5 and 7) as quantified by neutron imaging. Graphshows the laterally averaged degree of pore filling as a function of height and time. Thesquare root of time behavior of thewetting front advance is shown as a solid line (Grueneret al., 2012).


properties of variably-saturated materials has been demonstrated withnumerous examples drawn from the literature.While many of the earlystudies were essentially proof-of-principle in nature, yielding mainlydescriptive datasets, neutron imaging is increasingly being used to ex-tract detailed quantitative information at the pore/pixel scale of resolu-tion. Properties that have been measured, or should be quantifiable, bymeans of neutron imaging include total porosity, pore-size distribution,fluid content and distribution, capillary pressure–saturation parame-ters, liquid flow and transport parameters, such as sorptivity and hy-draulic diffusivity, the gas diffusion coefficient, and soil compactioncharacteristics, amongst others. However, there is an urgent need forthe establishment of appropriate standards for digital neutron radiogra-phy and tomography to instill confidence in such measurements(Kaestner et al., 2013).

In terms of new research, there is always the desire for increasedspatial and temporal resolution with respect scintillator/camera capa-bility. The current state-of-the-art for temporal resolution is in thesub-μs range (Siegmund et al., 2009; Tremsin et al., 2013), which is suf-ficient for most applications involving flow of hydrogen-rich fluids inporous media. In terms of spatial resolution, the current state-of-the-art is ~15 μm, and the push is on to get down to 1 μm (Tremsin et al.,2008; Siegmund et al., 2009). Unfortunately, for many natural andengineered materials even 1 μm spatial resolution will be insufficientto visualize fluid distributions within individual pores. This is certainlythe case for nanoporous materials which represent a rapidly-growingarea of interest. As a result, researchers are forced to work with imagesthat represent only average saturations at themicro-scale (e.g., Grueneret al., 2012). In this context, the spatial resolution target for the neutronimaging community should be the sub-μm range, although it is uncer-tain if such a goal can be achieved in the foreseeable future.

In conjunction with advances in imaging resolution, the problem ofscattering from samples containing large amounts of water (eitherbecause they are fully saturated or very thick) will need to be resolved.Scattering can result in the significant under estimation of water con-tent (or porosity) as illustrated in Fig. 4. Since most imaging studiesinvolving variably-saturated porous media require data collection overa broad range of fluid contents, and from sample volumes sufficient toaccount for heterogeneity, focusing only on small sample sizes and/orlowwater contents is too restrictive.Modeling the point scatter functionand/or the development of beamline specific calibration equations arecurrently the best options available. However, there is a need to bolsterthese two approaches with new imaging configurations that permit ex-perimental determination of the scattering components.

Combining images obtained using both neutron and x-ray imagingcould be very beneficial for future research (Banhart et al., 2010).These techniques currently overlap at 0.1 to 1 mm length scales interms of sample size and image resolution (Kaestner et al., 2008). Neu-tron imaging provides detailed information on the distribution of water

within a porous medium, but cannot see the air or solid phases. In con-trast, x-ray imaging provides detailed information on the geometry ofpore-solid interfaceswithin thematrix. By stitching together images ob-tained from both techniques it should be possible to visualize the distri-bution of fluids with respect to individual pore bodies and necks. Suchimages would be very valuable in studies of multiphase flow andtransport.

Many natural porous media contain hydrogen within their mineralmatrix, along with naturally-occurring organic matter. To date, howev-er, most neutron imaging studies of water in rocks and soils have fo-cused on samples that do not contain much native hydrogen. As wemove forward with imaging studies on a wide range of natural mate-rials, however, there will be a need to conduct additional detailed cali-brations to account for the presence of this matrix hydrogen. In fact,the method appears to be ideally-suited for visualizing the fine scalespatial distribution of organic matter in soil aggregates, an importanttopic for soil science, but one that does not yet appear to have been in-vestigated using neutron imaging. In geology and petroleum engineer-ing, hydrogen-rich fluids, such as oil, natural gas, and methane in rockreservoirs are of major importance as they relate to our energy econo-my. While some research has already been conducted on imagingsuch fluids (e.g., Middleton and Pàzsit, 1998; Solymar et al. (2003b),this application also appears to be ripe for further detailed investiga-tions, especially given the rapid rise to prominence of non-traditionalextraction methods such as hydraulic fracturing.

Finally, we would be remiss if we did not mention the exciting op-portunities offered by multispectral neutron imaging (also known astime-of-flight or energy selective neutron imaging). In this type of imag-ing, images are acquired at specific energy bands. Then, by “tuning” theimages across energy bands, it becomes possible to discriminate, for ex-ample, between the liquid and solid phases of water (Lehmann et al.,2011a). This is an important capability since information on the spatialdistributions of unfrozen liquid water and ice within a porous mediumat sub-zero temperatures is urgently needed in applications as diverseas permafrost thawing and the performance of fuel cells.While it is pos-sible to perform energy selective neutron imaging at continuous poly-chromatic neutron sources using wavelength selectors (Treimer et al.,2006), pulsed spallation sources, such as the existing SINQ neutron im-aging facility at PSI, Switzerland, and the SNS-VENUS, ISIS-TS2, ESS-ODIN, and J-PARC-ERNIS instruments currently under development inthe US, UK, Sweden, and Japan, respectively, offer the best prospectsfor progress in this area.


Considering current beam line capabilities and the prospects for fu-ture advances in resolution and energy selective imaging we are veryexcited about the future for neutron imaging and its applications to nat-ural and engineered porous materials. We believe this non-destructivetechnique will play an increasingly important role in advancing pore-scale understanding of the phase structure and flow of hydrogen-richfluids in variably-saturated porous media.

Acknowledgements

This review is based in part on assignments prepared by graduatestudents participating in a seminar course (GEOL 685) on imagingwater in porous media taught by the first author at the University ofTennessee - Knoxville (UTK) in the spring semester of 2012. Fundingwas provided by the Laboratory Directed Research and DevelopmentProgram of Oak Ridge National Laboratory (ORNL) and the Joint Direct-ed Research andDevelopment Programof theUT-ORNL ScienceAllianceat UTK.

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