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Analytica Chimica Acta 668 (2010) 26–34

Contents lists available at ScienceDirect

Analytica Chimica Acta

journa l homepage: www.e lsev ier .com/ locate /aca

omputer controlled-flow injection potentiometric system based on virtualnstrumentation for the monitoring of metal-biosorption processes

. Floridoa, C. Valderramaa,∗, S. Nualarta, L. Velazco-Molinab, O. Arias de Fuentesb, M. del Vallec

Departament d’Enginyeria Química, Universitat Politècnica de Catalunya, Av. Diagonal 647, Edifici H Planta 4a , 08028 Barcelona, SpainInstituto de Ciencia y Tecnología de Materiales-Universidad de La Habana, Zapata y G. Vedado, CP 10400, Ciudad de La Habana, CubaGrup de Sensors i Biosensors, Department de Química, Universitat Autònoma de Barcelona, Edifici Cn, 08193 Bellaterra, Barcelona, Spain

r t i c l e i n f o

rticle history:eceived 26 October 2009eceived in revised form1 December 2009ccepted 11 January 2010vailable online 18 January 2010

a b s t r a c t

A completely automated flow-injection system was developed for the monitoring of biosorption studies ofCu(II) ion on vegetable waste by-products. The system employed flow-through Cu(II)-selective electrodes,of epoxy-resin-CuS/Ag2S heterogeneous crystalline type, and computer controlled pumps and valves forthe flow operation. Computer automation was done through a specially devised virtual instrument, whichcommanded and periodically calibrated the system, allowing for the monitoring of Cu(II) ions between 0.6and 6530 mg L−1 at a typical frequency of 15 h−1. Grape stalk wastes were used as biosorbent to remove

eywords:rape stalk wastesopper sorptionirtual instrument

on-selective electrodelow-injection potentiometry

Cu(II) ions in a fixed-bed column with a sorption capacity of 5.46 mg g−1, obtained by the developed flowsystem, while the reference determination performed by FAAS technique supplied a comparable valueof 5.41 mg g−1.

© 2010 Elsevier B.V. All rights reserved.

n-line monitoring

. Introduction

Heavy metals are still being used in various industries due toheir technological importance. Considering the harmful effects ofeavy metals, it is necessary to remove them from liquid wastest least to the limit accepted by the regulations [1]. Aside from thenvironmental damage, human health is likely to be affected ashe presence of heavy metals beyond a certain limit brings seriousazards to living organisms. For instance, Cu(II) has been proved toause liver damage or Wilson disease. Many technologies, whichnclude precipitation, flotation, ion exchange, membrane-relatedrocess, electrochemical technique and biological process, haveeen employed to remove heavy metal ions from wastewater [2];hese conventional techniques can reduce metal ions, but they doot appear to be highly effective due to the limitations in the pHange as well as the high material and operational costs involved3,4]. In regards of its simplicity and high-efficiency characteris-

ics, the sorption process is one of the few alternatives availableor the removal of heavy metals [5]. A low-cost sorbent is hereefined as one which is abundant in nature, or is a by-product oraste material from another industry. Such materials could be an

∗ Corresponding author. Tel.: +34 93 4011818; fax: +34 93 4015814.E-mail address: cesar.alberto.valderrama@upc.edu (C. Valderrama).

003-2670/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.aca.2010.01.019

alternative to the conventional sorbents [6]. One of these sorbentsis grape stalk wastes generated in the wine production process,which has been satisfactorily applied in batch studies of copperand nickel [7,8]; lead and cadmium [6] and chromium removal[9]; furthermore, a previous study demonstrated that grape stalksare prominent sorbents for nickel removal in fixed-bed column[10]. Batch experiments are used to obtain equilibrium sorptionisotherms and to evaluate the sorption capacity of sorbents forgiven metals present in fluid phases [11]. However, in the prac-tical operation of full-scale biosorption processes, continuous-flowfixed-bed columns are often preferred [12,13]. These columns arepacked with the biosorbent and the aqueous metal solution ispumped upward through the column for its retention. Up to now,for the characterization of the sorption process, samples were col-lected from the column outlet during days or weeks and metalconcentration was analyzed at the end of the experiment by FAAS.Frequently, this procedure resulted in non-completed or over-timeexperiments and not-well defined breakthrough curves. For thisreason, the use of in-line or on-line real-time instrumentationwould introduce a better performance for the metal-biosorption

process characterization. Moreover and from a general point ofview, the threat of chemical, biological and radiological agentsintroduced into our air or water supplies has also stimulated thedemand for continuous, real-time, in situ chemical monitoring[14].

Chimica Acta 668 (2010) 26–34 27

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Table 1Physical and chemical properties of grape stalk wastes.

Property Grape stalks

Elemental analysisa (wt%)C 42.4H 5.8N 0.8S n.d.BETb surface area (m2 g−1) 0.42Total porosity (%) 72.3Bulk density (g dm−3) 304Apparent density (g dm−3) 1101

Net amount of cation released (meq g−1)due to proton uptakeCa2+ 0.382Mg2+ 0.126K+ 0.107Na+ 0.001CECc (mmol g−1) 0.36

A. Florido et al. / Analytica

Flow injection analysis (FIA) is nowadays an accepted method-logy for the development of monitoring applications. FIA has beenmployed with many different detection schemes, among themlso with ion-selective electrodes (ISEs) [15,16]. Normally, systemsith potentiometric detection, also called flow injection potentio-etric (FIP) [17] systems, are quite simple. This is because the

tages required to be performed are merely dilution and pH oronic strength adjustment, while the detection is achieved throughhe sensor element. Besides, functional ISE response characteristicsre normally improved when used in FIA systems, an interestingdvantage to be considered [18]. Flow systems with potentiomet-ic detection exist also with the more recent variants of sequentialnjection analysis (SIA) [19] and lab-on-valve (LOV) [20] method-logies.

Apart from adapting commercial ISEs to flow systems, specialonfigurations may be used to facilitate integration in the flow path.his can be a flow-through design, with the selective membraneresenting a tubular shape. This configuration resulted in a veryonvenient way to place the sensors in series to perform multipleetection, as very low distortion develops from one sensor to theext one [21,22]. FIP systems have been described for the detec-ion of anions [16] as well as cations [15], including heavy metals.he latter have been detected with ISEs furnished with crystallineembrane [23,24] or polymeric poly(vinyl)chloride membranes

25].Monitoring systems employing the FIA technique are an inter-

sting option when the analytical tasks are to be performed close tohe process being studied. FIP systems are even more attractive, ashe use of potentiometric detection greatly simplifies their opera-ion and requirements. This is because they normally use commonnd stable reagents (e.g. a pH buffer or an ionic strength adjustor),hich overall determines an easy maintenance. FIP systems have

een described for the monitoring of chloride dynamics in soils26], urea during hemodialysis treatment [27,28] and cyanide [29]r surfactants [30] in environmental field situations.

In order to characterize the electrochemical behaviour of theensors used in FIP systems, as well as in process and qualityontrol schemes necessary in industrial or agricultural plants, theeed of continuous monitoring in many points in all the processas become evident. Consequently, monitoring systems require a

arge number of measurements, repeatedly during long periods ofime. Therefore, computer-based instrumentation may be in helpor the above needs. In this sense, virtual instrumentation is these of customizable software and modular measurement hardwareo create specific computer-based measurement systems accord-ng to the user interest [31–34]. Virtual instrumentation manageshe PC hardware and others devices (i.e. multifunction DAQ cards),nd performs measurements like a real instrument. Moreover,heir use allows not only for the simple recording, but for storing,rocessing and analyzing the information or even taking controlctions automatically. Virtual instrumentation is a clear trend withomputer-based measurement systems.

One of the more employed programming environments forevelopment of virtual instrumentation is LabVIEW (National

nstrument, USA). The use of this kind of instrumentation underhe graphical LabVIEW environment is a low cost choice to be con-idered for the development of flow-potentiometric systems andesults in a single, simple of use, reliable and easily expandablenstrument [35–37].

The general aim of the present study is to develop an automaticonitoring system for its use in the recovery of Cu(II) ions from

queous solutions. The system, a real-time automatic monitoringow-injection system based on potentiometric chemical sensors isroposed for the on-line control of biosorption processes. For thiseason, we develop different aspects of the biosorption monitoringystem. First of all, we present two virtual instruments (VIs): one

a Elemental Analyzer EA 1110 CE Instruments (Italy) [8].b BET (Brunauer, Emmet, Teller) method.c Cation exchange capacity (CEC) [7].

permits the manual control of all the components (valves, pumps,sensor data acquisition), and the other VI, used for long-termexperiments, manages the complete on-line monitoring system,controlling all the components automatically in a pre-set orderfixed by the user. The real-time automatic flow-system developedin this work is applied in the flow-injection potentiometric moni-toring of the biosorption of copper(II) ions, taking place in fixed-bedcolumns filled with grape stalk wastes. Manifold and flow parame-ters are optimized and, for comparison purpose, the Thomas’ modelis used to predict the experimental breakthrough data obtained bythe FAAS and by FIP techniques.

2. Experimental

2.1. Reagents and materials

Metal solutions were prepared by dissolving appropriateamounts of CuCl2·3H2O(s) in deionised water (Milli-Q, Millipore).All reagents were analytical grade and were purchased from Merck(Darmstadt, Germany). Metal standard solutions of 1000 mg L−1

purchased from Carlo Erba (Milano, Italy) were used for flameatomic absorption spectroscopy (FAAS).

Grape stalk wastes generated in the wine production process(supplied by a wine manufacturer of the Empordà-Costa BravaDO region, Girona, Spain), were rinsed three times with deionisedwater, dried in an oven at 110 ◦C until constant weight, and finallycut and sieved for a particle size of 0.8–1.0 mm. Their properties assorbents are listed in Table 1.

2.2. Sorption column experiments

All column experiments were conducted in duplicate in glasscolumns of 72 mm length and 10 mm internal diameter (Omnifit)and uniformly packed with 1.4 g of grape stalks treated as explainedabove. During the column sorption operation the aqueous metalsolution containing 35 mg L−1 of Cu(II) was pumped upwardsthrough the column at a constant flow rate (30 mL h−1) and grapestalks particle size of 0.8–1.0 mm. Samples were collected from theoutlet of the column by a fraction collector (Gilson FC204) at pre-set

time intervals. The pH of the solution was measured by using a glasselectrode and the metal concentration in solution was determinedby FAAS using a Varian Absorption Spectrometer (Model 1275).The Cu(II) metal determination was performed at a wavelength of324.8 nm.

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8 A. Florido et al. / Analytica

.2.1. Breakthrough capacityThe breakthrough point is chosen arbitrarily at some low value,

amely Cb (mg L−1). When the effluent concentration, namely Cx

mg L−1), is closely approaching to 90% of the initial concentra-ion of sorbate, C0 (mg L−1), then the sorbent is considered to bessentially exhausted [38–40].

The capacity at exhaustion qcolumn (mg g−1) is determined byalculating the total area below the breakthrough curve. This areaepresents the amount of solute sorbed by mass of solid in theorption zone that goes from the breakpoint to exhaustion [39–41].

column =∫ Vx

Vb(C0 − C)dV

m(1)

here C is the outlet metal concentration (mg L−1) and m is theass of sorbent (g).The column sorption process requires prediction of the

oncentration–time profile or breakthrough curve for the effluent.arious mathematical models can be used to describe fixed-bedorption. One of the simplest and most widely used is the Thomas’odel [42,43], that allows to determine the maximum concentra-

ion of solute in the solid phase as well as the sorption rate constant.he linearized form of the model is expressed as:

n(

C0

C− 1

)= kThq0m

Q− kThC0Veff

Q(2)

here kTh is the Thomas’ rate constant (mL min−1 mg−1), q0 is thequilibrium metal uptake per gram of the sorbent (mg g−1), Q is theolumetric flowrate (mL min−1), Veff is the volume of effluent (mL)nd m is the mass of sorbent inside the column (g).

A linear plot of ln[(C0/C) − 1] against Veff/Q (or t) allows to deter-ine values of kTh and q0 from the intercept and the slope of the

lot, respectively.The dynamic behaviour of a fixed-bed sorption column can be

redicted by the above-described model. Linear regression coeffi-ients (R2) show the adequacy of the fits between the experimentalata and the linearized forms of Eq. (2); and Marquardt’s per-ent standard deviation (MPSD) indicates an estimation of erroretween the experimental and theoretical values of C/C0 used forlotting breakthrough curve, it can be calculated by using the fol-

owing equation [44]:

PSD = 100

√√√√ 1N − P

n∑i=1

[(C/C0)exp − (C/C0)theo

(C/C0)exp

]2

i

(3)

here N is the number of data points and P is the number of param-ters (or the degrees of freedom of the system).

.3. Virtual instrument-flow-injection potentiometry system

.3.1. ElectrodesFlow-through tubular electrodes selective to copper(II) ions

ere used as flow-injection detectors in the monitoring of biosorp-ion studies, and prepared as is presented in the literature [45].irst of all, equimolar mixtures of copper(II) sulphide and sil-er(I) sulphide were used as sensing material and prepared asescribed elsewhere [46]. The particle size of the salt mixture was2 �m. The sulphide mixture was dispersed in non-conductivepoxy resin (Araldite M and HR hardener, Ciba-Geigy, mixed in:0.4 weight ratio) in the proportion, 0.325 g of CuS/Ag2S and 0.2 g

f the resin. This preparation was used to fill the 1.75 mm ID channelrilled in the membrane support. A composite prepared from thebove mentioned non-conductive epoxy resin and graphite powderMerck) mixed in the weight ratio of 1:0.9, was used as conductive

embrane support: [47]. Afterwards, the central channel of these

ca Acta 668 (2010) 26–34

tubular electrodes was drilled at 0.75 mm i.d. diameter. The flow-through tubular electrode was placed inside a Perspex sandwich(see detail in Fig. 1) in order to be inserted in the flow system.

An Orion double-junction electrode (Model 90-02-00) was usedas reference electrode, using refilling solution Orion (ref. 90-00-02)in the inner compartment, and 0.1 mol dm−3 NaNO3 solution in theouter one.

2.3.2. Apparatus and materialsExperiments were performed either at room temperature or at

25 ◦C in a Medilow JP Selecta (Abrera, Barcelona, Spain) tempera-ture controller. Other equipment used included 8-channel GilsonMinipuls-3 (Villiers-le-Bel, France) peristaltic pumps and NRe-search (West Caldwell, NJ, USA) 3-way and 2-way solenoid valves.A bubble trap model 006BT, 8-way connectors and Teflon® tubing(0.8 mm i.d.) were purchased from Omnifit (Cambridge, UK).

2.3.3. Virtual instrumentation hardware and softwareThe virtual instrumentation hardware is formed by a PC, a data

acquisition card, a signal conditioning circuit and LabVIEW as vir-tual instrumentation software [37]. The sensors are connected tothe analog input of the acquisition card through the interface, andthe instrument permits the simultaneous potential acquisition upto a maximum of sixteen electrodes, an important feature for multi-parametric determinations. The potential signals proceeding fromthe ISEs and reference electrodes are sent to the conditioning cir-cuit where they are amplified by sixteen INA116 instrumentationamplifiers (Burr-Brown, Arizona, USA) and filtered by a RC low-passfilter (cutoff 2 Hz). Each channel output potential is sent to the dataacquisition card, connected to the PC in the PCI slot. The acquisi-tion card converts the analog potential differences between eachISE and the reference electrode to a digital signal for each channel.

All the monitoring system is controlled by the VIs implementedin LabVIEW 7.1TM. A PCI-6221 (National Instruments) data acquisi-tion card, featuring 16 Analog Inputs, 250 ksamples s−1, and 16-bitresolution, was used. The VI, combined with this card, permitsthrough the analog inputs to acquire, to process and to store thepotential values, meanwhile through the 24 digital inputs/outputs(I/O), the 3-way and 2-way solenoid valves are controlled. Theperistaltic pump speed was commanded by one of the 2 ana-log outputs by using the same VI. In order to interconnect allthe circuitry, a valve and pump interface was constructed in ourlaboratories containing 3 driver modules (Cooldrive, NResearch),controlling 5 solenoid valves each and the switches (on/off andforward/backward) of the peristaltic pump.

2.3.4. Flow-injection manifold for the monitoring of thebiosorption processes

The manifold used in the flow-injection system is presentedIn Fig. 1A, and consists in two different parts. One is where themetal-biosorption processes takes place, with the feeding metalsolution, the peristaltic pump at fixed flow rate, and the fixed-bedcolumn containing grape stalk wastes. The other part is the on-lineFIP system, with the peristaltic pump controlled by the VI, 2-wayand 3-way solenoid valves, holding and mixing coils, connectors,tubing, flow-through tubular electrodes, and reference electrode.

Flow parameters (injection and recovery times, flow rate, hold-ing coil length) were optimized in order to obtain the best signalsensitivity and sampling rate under low dispersion conditions (seeSection 3.3). Different synthetic samples at 12, 31 and 51 mg L−1

copper(II) concentrations were prepared and their concentrations

were determined by using directly the column inlet of the on-lineflow-injection system and by FAAS.

A solution containing 2.0 × 10−6 mol dm−3 copper(II) nitratewas used as carrier solution. This low level of the pri-mary ion was added in order to stabilize the baseline. A

A. Florido et al. / Analytica Chimica Acta 668 (2010) 26–34 29

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2g

cap

ig. 1. Manifold and hardware in the developed computer controlled-flow injectio2) used in metal-biosorption studies. The inset in part 1 shows the design of the flotabilization time (A), sensor calibration (B), cleaning and recovery time (C), sampli

.2 mol dm−3 sodium nitrate was used as ionic strength adjus-or solution. Copper(II) nitrate standard solutions in the range.0 × 10−5–1.0 × 10−2 mol dm−3 were prepared for calibration pur-ose.

.3.5. Procedure for the FIP monitoring of Cu(II) sorption onto

rape stalk wastes

Once all the system is checked with the manual VI, and allomponents are working properly and the tubing is purged, theutomatic VI starts and all experimental steps start following are-set order of the commands (see Table 2 and Section 3.2).

ntiometric system (1) and [V1–V8] on/off switching pattern of the solenoid valvesough tubular all-solid-state electrode. Part 2 indicates the sequence time for initialalysis (D and F) and delay time (E). Sequences E and F repeat continuously.

First, peristaltic pumps switch on and the feeding metal solution(35 mg L−1) starts passing through the fixed-bed column while thecarrier and ISA solutions start conditioning the sensors. Fig. 1B dis-plays the chronogram of operation of the solenoid valves in orderto perform the different stages of operation.

The full experiment consists, after a stabilization time, in the−5

sensor calibration with the standard solutions from 1.0 × 10 to

1.0 × 10−2 mol dm−3, followed with a baseline recovery time andcleaning, and finally the column monitoring. In all cases, consec-utive duplicate peaks were programmed. The last step repeatscontinuously until the user stops the VI. Specifically, in these exper-

30 A. Florido et al. / Analytica Chimica Acta 668 (2010) 26–34

Table 2Code of the different control commands used in the “.txt” document to introduce the experiment steps in the On-line Automatic Control VI. The VI imports this pre-set textfile in the initial configuration of the experiment.

Order Symbol Action (example)

Open valve v Open the valve # (between 1 and 15) (v 6, means to open valve 6)Close valve −v Close the valve # (between 1 and 15) (−v 6, means to close valve 6)Acquisition adq The system acquires the potential of the active sensor channels during the specified period of time

(adq 30, means potential acquisition during 30 s)Rotation of the peristaltic pump b The system controls the pump speed introducing a number between 0.00 and 5.00 (b 5, means

; b 0,ute th

icst

3

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maximum speedDelay d The system exec

time of 450 s)

ments, a sample was taken from the column and a full analysis wasompleted every hour. Finally, two injections of the feeding metalolution (35 mg L−1) were introduced manually in order to verifyhe exhaustion of the sorbent.

. Results and discussion

.1. Sorption of Cu(II) in fixed-bed column

Fixed-bed experiments were performed under conditionsescribed in Section 2.2. The experimental breakthrough curve ishown in Fig. 2. As can be seen, the breakthrough and the exhaus-ion times are reached after 90 and 450 mL, respectively.

The Thomas’ model was used to predict the experimental break-hrough data. The parameters of the Thomas’ model obtained fromlopes and intercepts of the linearized expression (Eq. (2)) were.28 and 5.39 for the kTh and q0, respectively. The maximumoncentration of solute on the sorbent predicted by the Thomas’odel is in good concordance to the experimental sorption capac-

ty calculated by Eq. (1) (5.41 mg g−1). Fig. 2 shows the modelledreakthrough curve for Cu(II) sorption onto grape stalks. A fairlyood agreement is observed between the experimental and mod-lled curve, furthermore, the analysis of R2 (0.98) and the MPSD5.4) values indicates a good fit of the Thomas’ model to Cu(II)orption data.

.2. VI for the control of biosorption processes

First of all, we present two specific and customized virtualnstruments (VIs), developed under the LabVIEW environment, forifferent control options. The first one permits the manual controlf all the components (valves, pumps, sensor channels.), as well as

he real-time data acquisition. This VI is mainly for the optimiza-ion and sensor initial checking or sensor calibration, previous tohe main experiment. The other virtual instrument, used for long-erm experiments, presents the same characteristics of the otherI, but in this case it manages the complete on-line monitoring sys-

ig. 2. The measured and modelled breakthrough curves by the Thomas’ model forhe sorption of Cu(II) onto grape stalks (0.8–1.0 mm particle size).

means pump stopped)e last order during the specified period of time (d 450, the system has a delay

tem, controlling all the components automatically in a pre-set orderfixed by the user. It is necessary to point out that both developedVirtual Instruments let to store, process and analyze the informa-tion, displaying on the screen the user’s preferences

In both VIs, when the user clicks the white arrow to start the pro-gram, a message appears indicating which folder has to be used tosave the data. After that, the developed VIs work in different ways.

As it was said before, the Manual Control VI has two func-tions: the user can manipulate all different components of theflow-injection system and can execute the order “Acquire” for thereal-time data acquisition. For this reason the program has twointerfaces: control and acquisition. In Fig. 3A left, the control screenis shown with 3 different parts that the user can control: on the topthe solenoid valves (injection unit), on the middle the electrodes(detection unit) and on the bottom the peristaltic pump (propul-sion unit). The opening/closing control of the valves is carried out byclicking on the corresponding valve switch (block 1, the red/greencolour indicates valve is closed/open). In block 2, considering thenumber of sensors to be used and, in consequence, the numberof occupied inlets in the interface, the user must fix the channelswhich their signal must be monitorized and stored when the order“Acquire” is executed. Moreover, in block 3 the user has to introducethe period of time in seconds this order will be executed. Finally,in order to control the pump speed, first the maximum rotationspeed (in rpm) must be introduced by its own controller and, after-wards, indicate in block 4 a number between 0.00 (stopped pump)and 5.00 (maximum pump speed). A number between those valueswould make the pump to spin at proportional speed.

In Fig. 3A right, the acquisition data screen is shown. This screenis activated automatically when the user clicks the “Acquire” but-ton; then the VI starts acquiring the potentials of each channel fromthe interface and plots them versus time in two charts (8 channels ineach), and giving, additionally, the potential values for each channelat real-time. The data acquisition finishes at the time fixed in thecontrol screen. After the acquisition time, measured data is storedin a text file.

The On-line Automatic Control VI has exactly the same functionsas the Manual Control VI, but in that case executes the orders in pre-set commands. Previously to start the VI, a text file must be createdwith a specific command in each line, following the codificationpresented in Table 2. An example of the text file is shown in Fig. 3B,left. In the same figure on the right, the control interface of the VIis also presented. The only parameter that is introduced manuallyis the channel selection of the sensors (block 1). Next, the programrequests the text file, and the command lines become visible inthe block 2. Then the program starts executing the orders line byline. Blocks 3 and 4 visualizes the situation at real-time of the openvalves, pump speed, and active command.

When the program arrives to the “Acquisition” command, ascreen similar to the one shown in Fig. 3A right is activated auto-matically during the specified period of time. This acquisition datascreen has the same characteristics as mentioned in the ManualControl VI.

A. Florido et al. / Analytica Chimica Acta 668 (2010) 26–34 31

Fig. 3. Virtual instrumentation (VI) developed in the present work: (A) Manual Control VI. Left, frontal panel for the manual control of valves (1), sensor channels (2),acquisition time (3) and pump speed (4). Right, frontal panel where potentials versus time curves (1), real-time potential values (2) and channel legend (3) of each sensorare showed. (B) On-line Control VI. Left, text file with an example of the experiment steps and different control commands. Right, frontal panel for the automatic control VI,i eed an

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3.3.2. Injection time and recovery timeThe injection volume of the sample (or the standard) introduced

into system was controlled by the time the corresponding solenoid

ndicating sensor channels (1), experiment steps (2), active valves (3) and pump sp

It is important to point out that both developed VIs obtain poten-ial values every second that are the mean of 100 acquired transientecordings. Those “stable” potentials are saved in a “.dat” docu-ent, and all this information is in a format accessible to other

rograms usually employed for data processing such as Origin orxcel.

.3. Optimization of the flow parameters and manifold

In the experiments performed to optimize the different flowarameters or the components of the manifold both either theanual and/or the full automatic VIs were used.

.3.1. Flow rate and bubble trapIn this study, five different experiments were carried out by

sing two different flow rates (60 and 120 mL h−1) and bubble trapypes (home-made, Omnifit or without bubble trap). Standard solu-ions ranged from 1.0 × 10−5 to 1.0 × 10−3 mol dm−3. In Fig. 4, a

lose-up of the superposed FIA peaks is presented. Results indicatedigher peaks for the 120 mL h−1 flow rate at the same concentra-ion level. On the other hand, the home-made bubble trap resultedn noisy signals, while no significant differences in the peaks werebserved if using no bubble trap or by using the Omnifit one. Finally,

d active command (4).

this bubble trap was included in the manifold considering that theobjective is to perform long-term experiments and the possibilitythat bubbles may appear in the system is high.

Fig. 4. Influence of the flow rate (60 and 120 mL h−1) and of the different bub-ble traps (home-made, Omnifit and without bubble trap) on the peak responsesat 1.0 × 10−4 mol dm−3 and 1.0 × 10−3 mol dm−3 Cu(II) concentration levels.

32 A. Florido et al. / Analytica Chimica Acta 668 (2010) 26–34

Fig. 5. Influence of the injection time on the peak responses. Peaks were obtainedat 20, 40, 60, 80, 100, 120 and 140 s, respectively, of a 1.0 × 10−4 mol dm−3 Cu(II)ss

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Table 3Comparison of the Cu(II) concentration level found in synthetic samples obtainedby FAAS or by flow-injection potentiometry by using flow-though tubular sensorsimplemented in the on-line monitoring system. Experimental values are the averageof triplicate measurements.

Cu2+ concentration level (mg L−1)

Nominal Sensor 1 Sensor 2 Sensor 3 Sensor 4 FAAS

Sensor slope(mV decade−1)

27.9 29.8 31.3 30.8

SampleM1 12.0 11.5 12.0 10.9 11.9 11.7

ence, it is possible to confirm that the FIP technique is a propermethodology to obtain confident monitoring systems.

Table 4Sorption parameters obtained by fitting the experimental (FAAS and FIP) break-through data to the Thomas’ model.

Parameter FAAS FIP

Experimental q0 (mg g−1) 5.41 5.46

−1 −1

tandard solution and at a constant flow rate of 120 mL h−1. The potential of thetationary state was 156 ± 1 mV.

alve was open, and considering a constant flow rate. In order toptimize this parameter, injections of a 1.0 × 10−4 mol dm−3 stan-ard solution at different valve opening times (20, 40, 60, 80, 100,20 and 140 s) were performed. The fiagram (FIA recording trace)btained is shown in Fig. 5. Results indicated that, among the peakslose to the 95% of the steady-state potential, 60 s was the minimumnjection time that gives sharp peaks.

The recovery time can be defined as the time elapsed betweenhe maximum of the peak and the moment in which the signaleaches a baseline not affecting the calculation of the peak heightf the subsequent injection. In practice, it is an important parame-er because it is the one determining the sample throughput. Evenhough with recovery times close to 300 s the potential achievedalues close to the initial baseline, using 120 s recovery times wasnough to obtain reproducible peak heights, allowing in this caseigher sampling rates.

.3.3. Sample holding coilThe length of the holding coil where the sample coming from the

xed-bed column is stored prior to its injection was also optimized.his is because its inner volume must be sufficient in order to permitwo 60 s consecutive injections of the same sample for compari-on purposes. Results indicated that a minimum length of 3 m wasecessary if two consecutive injections of the higher concentration

evel used as feeding solution (35 mg L−1) had to be reproducible.On the other hand, the position of the holding coil entry

o the 8-way connector related to the different entries of thetandard solutions is critical. The reason is that certain diffu-ion of analyte was observed inside the connector and someontamination of the samples and standards were observed if

very diluted and a concentrated solution were contiguous.his observed problem was solved arranging the solutions in apecific order coming from the more diluted to the more concen-rated.

.3.4. Checking of the system by using synthetic samplesIn order to verify the correct functionality of all parts of the on-

ine automatic system and the VI, synthetic samples were preparednd introduced in the system in the same place as the column inlet.ominal values of 12, 31 and 51 mg L−1 copper(II) concentrationsere used. In Table 3 concentration values obtained by using the

n-line flow-injection system for different sensors and by usingAAS are presented. One can point out that similar results wereound for both techniques.

M2 31.0 29.9 31.7 29.7 30.6 32.2M3 51.0 50.1 54.00 50.4 49.8 53.4

3.4. Evaluation of FIP monitoring in Cu(II) sorption onto grapestalk wastes

In Fig. 6 a fiagram obtained in the complete experiment for thesorption of Cu(II) onto grape stalks performed in a fixed-bed columnis presented. One can observe an enlargement of the calibrationpeaks, and another enlargement with two consecutive samplingsin order to show the reproducibility of the determinations. Thelast two peaks at 35 mg L−1 were obtained manually introducingdirectly the feeding solution and one can conclude from the pre-vious peaks that the sorbent placed in the fixed-bed column wascompletely exhausted.

Fig. 7 shows the breakthrough curves obtained by flame atomicabsorption spectrometry and by the flow-injection system basedon potentiometric chemical sensors. As can be seen both tech-niques follow similar behaviour and no significant differences areobserved. In order to compare the performances of both method-ologies, the experimental breakthrough data obtained by the FIPtechnique was fitted to the Thomas’ model in order to predict theexperimental behaviour; additionally the sorption capacity wascalculated by Eq. (1) and then compared to value obtained by theFAAS technique. Table 4 reports the experimental (FAAS and FIP)and the sorption parameters obtained by fitting the experimentalbreakthrough data to the Thomas’ model. It is observed that theexperimental sorption capacity reports a slight difference betweenFAAS and FIP techniques. Furthermore the parameters obtained bythe Thomas’ model were almost the same for both techniques. Anal-ysis of R2 indicates that the first parameter was exactly the samefor both models confirming that parameters obtained were veryclose for both techniques. The MPSD parameter was slightly higherfor the FIP technique that corresponds to the higher differenceobserved between the theoretical and the experimental sorptioncapacity q0. Fig. 7 also represents the predicted Thomas’ model;as the parameters obtained by fitting the experimental data areclosely the same for both techniques a single curve is obtained.

The differences observed between FAAS and FIP techniques arevery small and considering a well-established model as a refer-

Thomas kTh (mL mg min ) 0.27 0.24q0 (mg g−1) 5.39 5.41R2 0.98 0.98MPSD 5.4 6.8

A. Florido et al. / Analytica Chimica Acta 668 (2010) 26–34 33

Fig. 6. Fiagram obtained in the complete experiment for the sorption of Cu(II) onto grape stalks performed in fixed-bed columns (A). The two enlargements are: a magnificationof the calibration area peaks (B), and two samplings with two consecutive peaks and with a delay time of 1 h (C).

34 A. Florido et al. / Analytica Chimi

Fmg

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ig. 7. The measured by the on-line automatic FIP system and by FAAS and theodelled breakthrough curves by the Thomas’ sorption model Cu(II) removal onto

rape stalks (0.8–1.0 mm particle size).

. Conclusions

A real-time automatic monitoring flow-injection system basedn potentiometric chemical sensors has been developed for then-line monitoring of biosorption processes. Operation could beompletely automated thanks to the easy use and (re)configurationf developed virtual instruments for its computer control. Biosorp-ion studies performed show that grape stalk waste can bemployed as sorbent for the removal of Cu(II) from aqueous solu-ions in fixed-bed column experiments. The Thomas’ model wassed to predict the experimental breakthrough data obtained byhe FAAS and by FIP techniques; a fairly good agreement betweenxperimental and predicted curves was obtained, with experi-ental sorption capacities that were closely the same. Parameters

btained by the Thomas’ model from measures obtained with bothechniques were practically undistinguishable and the statisticalarameter MPSD reported a slight deviation between both tech-iques. In conclusion, the conjunction of virtual instrumentationnd flow-injection potentiometry has appeared as an integratedhoice for the automatic monitoring of laboratory or pilot-plantrocesses, resulting in a versatile, expandable and user-friendly

nstrument.

cknowledgments

We wish to acknowledge the contribution of MICINN projectsTM2008-06776-C02-02/TECNO and TEC2007-68012-C03-02/MICSpanish Ministry of Science and Innovation). Authors arextremely grateful to Carme Gauchia for analysis of the samples.

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