Array of potentiometric sensors for simultaneous determination of copper, silver, and cadmium ions in complex mixtures

Electrochimica Acta 62 (2012) 84–90

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Array of potentiometric sensors for simultaneous determination of copper, silver, and cadmium ions in complex mixtures Abbas Shirmardi-Dezaki a,∗ , Mojtaba Shamsipur b , Morteza Akhond c , Hashem Sharghi c , Mohammad Mahdi Doroodmand c a

Department of Chemistry, Islamic Azad University-Masjedsoleyman Branch, Masjedsoleyman, Iran Department of Chemistry, Razi University, Kermanshah, Iran c Department of Chemistry, Shiraz University, Shiraz, Iran b

a r t i c l e

i n f o

Article history: Received 4 August 2011 Received in revised form 23 November 2011 Accepted 25 November 2011 Available online 7 December 2011 Keywords: Array Ion-selective electrodes Silver Cadmium Copper

a b s t r a c t A programmed switching system combined with an array of potentiometric sensors consisting of seven potentiometric sensors (i.e., ion-selective or cross-selective electrodes) was connected directly to a pH/potentiometer and a computer (PC) to sequentially acquire the potential corresponding to water sample mixtures. The acquired potentials were recorded and saved on the PC and were used as input variables for an artificial neural network to simultaneously yield the concentrations of Cd2+ , Cu2+ , and Ag+ in simple and complex mixtures. A feed-forward, back propagation network with a Levenburg–Maquart algorithm was employed to optimize the network parameters. Certain characteristics of each of the seven ion-selective electrodes, including selectivity coefficients, calibration curves, and response times, were also studied. A five-second delay time was used when recording the potentials of the electrodes using the switching system. The array system was also optimized for the selection of the ion-selective electrodes. A four-electrode array system was found to be the best choice for the prediction of Cd2+ , Ag+ and Cu2+ ion concentration, but application of all seven ion-selective electrodes was necessary for prediction of these primary ions in samples containing a combination of zinc and nickel ions as interfering ions. © 2011 Elsevier Ltd. All rights reserved.

1. Introduction Ion-selective electrodes (ISEs) are among the best, safest, fastest and most inexpensive tools for controlling the quality of real samples. The use of ISEs dates back to the mid-1970s [1–4]. The working mechanism of ISEs may differ based on their membrane constituents, such as chalcogenide glass [5,6], organic plasticized polymeric mixed with active neutral or ionic carriers [7,8], and Longmuir-Blodgett [9] or electropolymerized films [10]. The selectivity, reproducibility and long-term stability of ISEs depend on their membrane composition. Several reported ISEs are able to determine the presence of only one species, but some of them suffer from a lack of selectivity and reproducibility. These sensors are called cross-selective electrodes [11,12].

Abbreviations: PC, personal computer; ISE, ion selective electrode; ANN, artificial neural network; PVC, poly(vinyl chloride); o-NPOE, o-nitrophenyloctyl ether; DES, diethyl sebacate; DBP, dibutyl phtalate; NaTPB, sodium tetraphenylborate; KTpClPB, potassium tetrakis(p-chlorophenylborate); THF, tetrahydrofuran; FIM, fixed pot interference method; RT, response time; kA,B , potentiometric selectivity coefficient. ∗ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Shirmardi-Dezaki). 0013-4686/$ – see front matter © 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.electacta.2011.11.111

Pollution caused by heavy metal ions is a major environmental problem. Mining and industrial activities may discharge large quantities of effluents into bodies of water. Copper is a major trace element in the environment due to its use in paints, electronics, and other industrial resources [13]. In spite of being an essential trace nutrient at ambient levels, its toxicity for aquatic organisms and human beings has been reported [14,15]. The toxicity of copper is related to free Cu2+ but not to the total dissolved copper, which makes copper ISEs more applicable in clinical and environmental studies. Cadmium is also extensively used in many industrial processes, including in metal plating, cadmium–nickel batteries, mining, pigments, and alloys [16], but it may affect on human blood pressure, kidney damage, testicular tissues, and red blood cells [17]. Silver is also widely used in industrial processes. It is used as a dopant with copper and cadmium in brazing alloys, in jewelry, mirrors, photographic processes, dental amalgam, and burn ointments, such as silver sulfadiazine [18–20]. Fortunately, silver has low toxicity. However, chronic intake may cause “argyria”, which is a blue-gray discoloration of tissues, and toxicity in the liver and kidney has also been reported [21]. Thus, the simultaneous determination of silver, copper, and cadmium would be of major interest for water quality studies. In the last two decades, our group has reported several ion-selective electrodes for Cu2+ , Ag+ , and Cd2+ [22–29]. Some of these sensors were

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selective only for simple or binary samples, while others were cross selective. Therefore, they can be used together in a homemade array system for the simultaneous determination of Cu2+ , Ag+ , and Cd2+ ions in the presence or absence of certain foreign ions, such as nickel and zinc that may coexist with them in natural water. An artificial neural network (ANN) is a nonlinear calibration tool that helps to generate the response model, which can then be used to predict sample concentrations [30–32]. Because the potential response of ISEs is nonlinearly related to the concentration of the primary ion, an ANN was the most successful tool reported for the qualification and quantification of the analytes [33,34]. ANNs transform potential responses as input variables to the output concentration in training steps. A multilayered, feed-forward neural network trained with a back-propagation learning algorithm is a popular technique because of its flexibility for discovering more complex relationships. 2. Experimental 2.1. Reagents and materials Reagent grade high molecular weight poly(vinyl chloride) (PVC), o-nitrophenyloctyl ether (o-NPOE), diethyl sebacate (DES), dibutyl phtalate (DBP), sodium tetraphenylborate (NaTPB), and potassium tetrakis (p-chlorophenylborate) (KTpClPB) were used as predict sample concentrations prepared by Merck, Germany. Ligands 1–6 were synthesized and characterized in our laboratory [22–29]. The nitrate salts of all of the cations (Merck) used were of the highest purity available and were used without further purification, except for vacuum drying over P2 O5 . Triply distilled deionized water was used throughout the studies. 2.2. Electrode preparation The general procedure for preparing each of the PVC membranes was to thoroughly mix an optimized amount of plasticizer, PVC, the proper ionic additive, and the ionophore in a glass dish that was 2 cm in diameter. Then, the mixture was completely dissolved in 4 mL of tetrahydrofuran (THF). The solvent was evaporated slowly until an oily concentrated mixture was obtained. Spectroscopic grade graphite rods that were 50 mm long and 3 mm in diameter were employed. A shielded copper wire was glued at one end of the graphite rod, and the electrode was sealed in the end of a PVC tube of approximately the same diameter with epoxy resin. The working surface of the electrode was polished with fine alumina slurries on a polishing cloth, sonicated in distilled water and dried in air. To prepare the membrane-coated electrode, a polyaniline film was created electrochemically at the polished graphite rod (Fig. 1) using a 1 M aqueous anilinium hydrochloride solution. Then the polyaniline film coated graphite rod was immediately immersed in the membrane cocktail, removed after 2 s, and allowed to dry in air. After evaporation of the THF, a thin membrane film was formed at the graphite tip. The membrane was formed on the graphite surface over the polyaniline conductive thin film. Finally, the electrode was conditioned by soaking in a 1.0 × 10−2 M metal nitrate solution for 48 h.

Fig. 1. Repetitive cyclovoltammogram for oxidation/reduction of an aqueous anilinium hydrochloride solution (1 M) at a graphite surface.

(Fig. 2), which is the electrode inlet port of a Metrohm model 780 pH/potentiometer. To sequentially receive the potential corresponding to each of the ISEs, both the pH meter and the switching system were programmed using a RS232 setting and programming on a microcontroller, respectively. For the potential of each electrode to be displayed on the pH meter, the microcontroller was connected to the computer using a serial port. The potentials were collected on the PC using a program written in the visual basic 6.0 environment by our group. Then, the data were saved as a notepad file that can be imported as a matrix of rows and columns into MATLAB for data processing using the ANN toolbar in MATLAB version 7.5.0.342. 3. Results and discussion 3.1. Selectivity coefficient measurements To evaluate the ability of the electrodes to work under harsh conditions, it is better to determine the selectivity of the electrodes in complex mixtures of cations such as Cu2+ , Cd2+ , Ag+ , Zn2+ , Co2+ , Ni2+ , Al3+ , and Fe3+ . It is necessary to obtain selectivity coefficients of the electrodes under that experimental condition, and the array system was used to obtain the selectivity coefficients, as the experiment is more efficient, and we can obtain

2.3. Instrumental set up Seven coated graphite ion-selective electrodes (Table 1) were mounted on a homemade disc at identical distances to the doublejunction Ag/AgCl/3 M KCl reference electrode. The ISEs and the reference electrode were connected to a homemade eight-port switching system in which each electrode and the reference electrode were connected to a different port. The output of the system was directly connected to another port of the switching system

Fig. 2. Manifold of the array system, sample (1), electrodes held with a disc (2), switching system (3), input of the electrodes to the switching system (4), electrode output from switching system (5), pH meter (6), reference electrode connection to the pH meter (7), electrodes input to the pH meter (8), pH meter RS232 port (9), PC (10), and serial port of PC (11).

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Table 1 Summary of the electrode characteristics. Electrode

Ionophore structure

Ionophore (wt.%)

PVC (wt.%)

Plasticizer (wt.%)

Ionic additive (wt.%)

R.T. in array (s)

R.T. alone (s)

Ref.

HS

1. Cu2+ -selective electrode

3.4

29.4

58.9 (o-NPOE)

8.3 (NaTPB)

7

4

[27]

2. Cu2+ -selective electrode

2.8

31.3

62.7 (DBP)

3.2 (NaTPB)

7

5

[29]

3. Cd2+ -selective electrode

3.8

28.1

56.1 (o-NPOE)

12 (OA)

6

5

[23]

7.0

32.6

67.4 (o-NPOE)

1.3 (KTpClPB)

6

4

[29]

1.0

32.7

65.3 (o-NPOE)

1.0 (KTpClPB)

6

5

[22]

2.4

30.2

60.4 (o-NPOE)

6(OA)

7

5

[28]

–

–

5

3

–

N

OH

S

S

4. Ag+ -selective electrode

S

S

S

S

5. Co2+ -selective electrode

6. La3+ -selective electrode

7. Unmodified graphite electrode

N

S

S

N

–

the selectivity coefficients of similar electrodes for each experimental run. The fixed interference method (FIM) was used to obtain the selectivity coefficients [35]. In the present study, the selectivity coefficients were evaluated from potential measurements on solutions containing a fixed concentration of interfering ion (1.0 × 10−2 M) with a varying amount of the primary ion. For example, to obtain the selectivity coefficients of electrode nos. 1 and 2, which are selective for the Cu2+ ion, the concentration of all of the aforementioned cations (except the Cu2+ ion) were held constant at 1.0 × 10−2 M, and the concentration of the Cu2+ ion was varied. Therefore, the selectivity coefficient is calculated for these electrodes from the following equation: pot

log kCu,M =

aCu2+

aB zA /zB

where aCu 2+ is the activity of the primary ion (Cu2+ ) at the lower detection limit in the presence of interfering ion B with activity of aB and charges zA and zB . This strategy is repeated to obtain the selectivity coefficients for the rest of the electrodes, which are sensitive pot pot pot to Cd2+ and Ag+ . The resulting log kCu,M , log kCd,M , and log kAg,M values are summarized in Table 2. Electrode nos. 1 and 2 respond selectively to Cu2+ ion. However, electrode nos. 4 and 5 are not selective for the Cu2+ ion but exhibit a small selectivity coefficient for Cu2+ ion (interference from Cu2+ ). Electrode no. 3 is selective for Cd2+ ion and exhibits a small selectivity coefficient (smaller than 1.0 × 10−2 ). Electrode no. 4 is selective for Ag+ ion with a little interference from the Cu2+ ion. The unmodified graphite electrode (Electrode no. 7) also exhibits selectivity for Ag+ ion with a small selectivity coefficient for the Cu2+ ion.

–

3.2. Response time study of the array system The response time (RT) of the electrodes was measured using IUPAC recommendations. Therefore, the practical response time required for the ion-selective electrodes to reach a potential within ±1 mV of the final equilibrium value after successive immersion of each electrode in a series of solutions containing the primary ion for each of the related ion-selective electrodes, with each exhibiting a 10-fold difference in the concentration of the primary ion, was measured. Because the response time of the ion-selective electrodes depends on the sample composition, two points were considered for measuring the response time of each electrode in each experimental run. The first was to obtain the response time of each electrode separately by switching off the non-selective electrodes in the array. The second was to apply solutions having the same concentrations (5.0 × 10−3 M) as the rest of the coexisting cations in each experimental run. For example, to obtain the response time of electrode no. 1, the Cu2+ ion-selective electrode, this electrode was turned on, while the rest of the electrodes, i.e., electrode nos. 2–7, were turned off. Therefore, the solutions had a ten-fold difference in the concentration of Cu2+ ion and a 5.0 × 10−3 M concentration of both Cd2+ and Ag+ ions. The average of the response times for each electrode during the ten-fold concentration changes and over the concentration range 1.0 × 10−6 –1.0 × 10−2 M of Cu2+ in the presence of constant concentrations of both Cd2+ and Ag+ ions were obtained, and the results are summarized in Table 1. From the sixth column of Table 1, the response times of the electrodes are seen to be in the range 4–7 s. Therefore, a potential acquisition delay time of 6 s was set for the homemade switch box to ensure that the responses of all of the electrodes leveled off. This delay time was set in the visual basic program that controls the homemade switch

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Table 2 Selectivity coefficient measurements for the electrodes. Electrode no. pot log kM,M Mn+

1 pot log kCu,M

2 pot log kCu,M

3 pot log kCd,M

4 pot log kAg,M

5 pot log kCo,M

6 pot log kLa,M

7 pot log kAg,M

Cu2+ Cd2+ Ag+ Co2+ Ni2+ Al3+ Pb2+ Fe3+ Zn2+

0.0 −3.7 −3.1 −4.2 −4.4 −5.1 −3.8 −3.9 −5.3

0.0 −4.1 −2.5 −2.3 −3.9 −3.5 −1.5 −2.1 −2.2

−2.4 0.0 −3.3 −2.6 −3.7 −3.3 −3.4 −2.7 −2.6

−1.0 −2.9 0.0 −3.6 −3.7 −4.1 −3.2 −3.3 −3.8

−1.9 −3.1 −2.1 0.0 −2.3 −3.6 −2.9 −3.2 −3.5

−2.7 −3.0 −2.5 −3.2 −4.0 −2.4 −2.1 −2.4 −4.0

−2.3 −3.8 0.0 −4.0 −4.0 −4.0 −4.0 −4.0 −3.2

Fig. 4. Correlation plots for the prediction of Cu2+ , Cd2+ , and Ag+ in the presence of Ni2+ and Zn2+ using {2–6}. Fig. 3. Correlation plots for the prediction of Cu2+ , Cd2+ , and Ag+ in the absence of interfering ions using {2–7}.

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Table 3 Calibration samples. Sample no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Table 4 Optimization of the array.

Cu2+ (mol dm−3 ) −4

3.0 × 10 5.0 × 10−4 1.0 × 10−3 1.1 × 10−3 1.0 × 10−4 5.0 × 10−2 1.0 × 10−3 1.0 × 10−2 1.0 × 10−3 1.0 × 10−4 8.0 × 10−5 3.0 × 10−4 4.0 × 10−2 1.0 × 10−3 1.0 × 10−4 1.0 × 10−5 1.0 × 10−3 1.0 × 10−4 1.0 × 10−4 1.0 × 10−4 1.0 × 10−5 1.0 × 10−5 3.0 × 10−2 1.0 × 10−4 8.0 × 10−6 1.0 × 10−5 1.0 × 10−1 1.0 × 10−5 1.0 × 10−5 8.0 × 10−6 1.0 × 10−3 1.0 × 10−1 4.0 × 10−5 1.0 × 10−3 1.0 × 10−4

Ag+ (mol dm−3 ) −3

3.0 × 10 2.0 × 10−4 1.2 × 10−3 3.0 × 10−4 1.0 × 10−2 3.0 × 10−2 5.0 × 10−4 5.0 × 10−2 1.0 × 10−3 1.0 × 10−4 1.0 × 10−3 1.0 × 10−3 1.0 × 10−4 1.0 × 10−3 1.0 × 10−3 1.0 × 10−5 1.0 × 10−4 1.0 × 10−4 5.0 × 10−3 1.0 × 10−2 1.0 × 10−4 1.0 × 10−5 3.0 × 10−2 1.0 × 10−4 8.0 × 10−6 1.0 × 10−4 1.0 × 10−5 1.0 × 10−4 1.0 × 10−3 8.0 × 10−4 1.0 × 10−5 1.0 × 10−4 4.0 × 10−3 6.0 × 10−5 1.0 × 10−5

Cd2+ (mol dm−3 )

Array no.

Array

Target Iona

R2 calibration

R2 prediction

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

{1}b {1} {1} {2} {2} {2} {3} {3} {3} {4} {4} {4} {5} {5} {5} {6} {6} {6} {7} {7} {7} {1, 3, 4} {1, 3, 4} {1, 3, 4} {2, 3, 4} {2, 3, 4} {2, 3, 4} {3, 4, 5} {3, 4, 5} {3, 4, 5} {3, 4, 6} {3, 4, 6} {3, 4, 6} {1, 3–5} {1, 3–5} {1, 3–5} {1–5} {1–5} {1–5} {1–5, 7} {1–5, 7} {1–5, 7} {1–7} {1–7} {1–7} {2–7} {2–7} {2–7} {2–6} {2–6} {2–6}

Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+ Cu2+ Cd2+ Ag+

0.9999 0.8553 0.2669 0.9916 0.0358 0.8226 0.2080 0.9925 0.5760 0.0598 0.1539 0.9929 0.9853 0.3430 0.5048 0.9984 0.8622 0.5523 0.0827 0.8160 0.6925 0.9229 0.9628 0.9407 0.9993 0.9304 0.9308 0.9966 0.9288 0.9959 0.9888 0.9966 0.9424 0.9255 0.6996 0.9615 1.0000 0.9943 0.9949 0.9347 0.9999 0.9998 0.9757 0.9991 0.9988 0.9964 0.9998 0.9990 0.9997 0.9992 0.9676

0.9669 0.0119 0.1399 0.9861 0.0261 0.0096 0.1757 0.9802 0.0175 0.0017 0.2948 0.9944 0.9330 0.0269 0.1552 0.9506 0.0096 0.1755 0.0389 0.5155 0.1251 0.0114 0.9681 0.9899 0.7255 1.0000 0.0049 0.7807 0.9649 0.9932 0.2045 0.9974 0.9338 0.0019 0.4333 0.9749 0.7668 0.9606 0.9632 0.7207 0.9939 0.9987 0.9795 0.7641 0.9770 0.9988 0.9986 0.9984 0.9808 0.9645 0.9992

−3

1.3 × 10 1.0 × 10−3 3.0 × 10−2 3.0 × 10−3 4.0 × 10−4 4.0 × 10−3 1.0 × 10−3 5.0 × 10−2 1.0 × 10−4 1.0 × 10−3 6.0 × 10−3 1.0 × 10−2 1.0 × 10−3 6.0 × 10−2 1.0 × 10−4 1.0 × 10−3 1.0 × 10−4 5.0 × 10−4 2.0 × 10−4 1.0 × 10−3 1.0 × 10−5 1.0 × 10−4 3.0 × 10−2 1.0 × 10−4 1.0 × 10−5 1.0 × 10−5 1.0 × 10−3 1.0 × 10−3 1.0 × 10−4 8.0 × 10−5 1.0 × 10−4 1.0 × 10−3 4.0 × 10−5 1.0 × 10−3 1.0 × 10−3

box. It is interesting to note that the response time of each electrode is much larger when the electrode is used in a solution containing only the primary ion. 3.3. Neural network architecture and training To obtain a reliable model of the response of the array system to the concentrations of the cations, we need to have calibration and prediction data sets. The model was built based on a calibration set by neural networks, evaluated for its prediction ability, and then generalized using the prediction set. For calibration, 35 samples containing random concentrations of Cu2+ , Ag+ , and Cd2+ ions were used in the calibration set, and 10 samples were used in the prediction set (Tables 3 and 5). Ten additional samples were also used in further predictions of the cations in the presence of nickel and zinc ions (Table 6). ANN was used to obtain a suitable model between the response of the array system and the concentrations of the metal ions. Moreover, the response of each electrode was individually inspected by the ANN to determine which electrodes in the array system would result in the best prediction for each cation. In addition, various combinations of the electrodes were tested for prediction of each cation. First, the data collected from all of the electrodes were auto scaled and fed to the neural network algorithm to build models for electrode combinations. The predictability of each of the electrode combinations toward Cu2+ , Ag+ , and Cd2+ ions in the calibration and prediction samples were evaluated based on the resulting squared correlation coefficient, R2 . The R2 values were compared to determine the compatibility of each array configuration for the determination of each of the species (Table 4). As Table 4 indicates, each of the electrodes (1–6) can predict only one of the ions of interest. This point can also be predicted from the selectivity

a b

Target ion is the ion by used to make its neural network model.. The number in {} is the electrode no. the array was constructed from.

coefficient data (Table 2). Electrode nos. 1, 2, 5, and 6 are useful for the prediction of Cu2+ ion, but they have a reduced ability to predict the other ions because electrode nos. 1 and 2 are Cu2+ ion-selective electrodes, as observed from the selectivity data. The prediction capability of electrode nos. 5 and 6 can also be justified by their selectivity coefficients for copper ion. If their ions of interest were negligible or not present, copper ion would be an interfering ion for these electrodes. Consistent with the selectivity data, electrode nos. 3 and 4 can only predict the concentration of Ag+ and Cd2+ ions, respectively, but they have a reduced ability to predict the other ions. From Table 4, electrode no. 7 is not able to precisely predict the concentration of any of the ions. However, electrode no. 7 responds not only to Ag+ ion but also to all of the coexisting cationic species in the mixtures studied [34]. Thus, electrode no. 7 probably contains information about the concentration of the ions of interest and may also aid in the construction of a more precise model

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Table 5 Prediction of Cu2+ , Ag+ , and Cd2+ ion concentrations in tertiary aquatic mixtures (prediction samples) using {2–7} array. Actual concentration (mol dm−3 )

Sample no.

1 2 3 4 5 6 7 8 9 10

Found concentration (mol dm−3 )

Cu2+

Ag+

Cd2+

Cu2+

Ag+

Cd2+

1.0 × 10−4 3.0 × 10−4 2.0 × 10−3 3.0 × 10−3 7.0 × 10−3 3.0 × 10−4 3.0 × 10−3 2.0 × 10−3 1.0 × 10−4 3.0 × 10−4

3.0 × 10−4 1.0 × 10−4 1.0 × 10−3 3.0 × 10−3 5.0 × 10−2 4.0 × 10−3 1.0 × 10−4 5.0 × 10−4 5.0 × 10−3 4.0 × 10−4

1.0 × 10−4 1.0 × 10−3 4.0 × 10−2 1.0 × 10−2 1.0 × 10−4 5.0 × 10−2 1.0 × 10−4 5.0 × 10−4 1.0 × 10−2 6.0 × 10−3

1.0 × 10−4 3.0 × 10−4 1.7 × 10−3 2.6 × 10−3 6.2 × 10−3 5.0 × 10−4 2.8 × 10−3 1.7 × 10−3 1.0 × 10−4 3.0 × 10−4

5.0 × 10−4 1.0 × 10−4 2.1 × 10−3 4.2 × 10−3 6.4 × 10−2 3.2 × 10−3 1.0 × 10−4 9.0 × 10−4 5.2 × 10−3 4.0 × 10−4

2.0 × 10−4 8.0 × 10−4 4.0 × 10−2 9.9 × 10−3 1.0 × 10−4 5.2 × 10−2 2.0 × 10−4 5.0 × 10−4 9.5 × 10−3 6.4 × 10−3

Table 6 Prediction of Cu2+ , Ag+ , and Cd2+ ion concentrations in the presence of Zn2+ and Ni2+ foreign ions. Sample no.

1 2 3 4 5 6 7 8 9 10

Actual concentration (mol dm−3 )

Foreign ion (added) concentration (mol dm−3 )

Found concentration (mol dm−3 )

Cu2+

Ag+

Cd2+

Zn2+

Ni2+

Cu2+

Ag+

Cd2+

1.0 × 10−4 3.0 × 10−4 2.0 × 10−3 3.0 × 10−3 7.0 × 10−3 3.0 × 10−4 3.0 × 10−3 2.0 × 10−3 1.0 × 10−4 3.0 × 10−4

3.0 × 10−4 1.0 × 10−4 1.0 × 10−3 3.0 × 10−3 5.0 × 10−2 4.0 × 10−3 1.0 × 10−4 5.0 × 10−4 5.0 × 10−3 4.0 × 10−4

1.0 × 10−4 1.0 × 10−3 4.0 × 10−2 1.0 × 10−2 1.0 × 10−4 5.0 × 10−2 1.0 × 10−4 5.0 × 10−4 1.0 × 10−2 6.0 × 10−3

1.0 × 10−3 1.0 × 10−2 1.0 × 10−4 3.0 × 10−4 1.0 × 10−1 1.0 × 10−1 2.0 × 10−3 5.0 × 10−2 6.0 × 10−4 4.0 × 10−4

3.0 × 10−3 1.0 × 10−3 1.0 × 10−2 1.0 × 10−1 1.0 × 10−1 3.0 × 10−3 3.0 × 10−2 1.0 × 10−2 1.0 × 10−2 4.0 × 10−4

1.0 × 10−4 2.0 × 10−4 1.8 × 10−3 2.6 × 10−3 6.9 × 10−3 5.0 × 10−4 2.8 × 10−3 1.7 × 10−3 1.0 × 10−4 2.0 × 10−4

5.0 × 10−4 1.0 × 10−4 2.1 × 10−3 4.2 × 10−3 6.4 × 10−2 3.2 × 10−3 1.0 × 10−4 9.0 × 10−4 5.2 × 10−3 4.0 × 10−4

2.0 × 10−4 8.0 × 10−4 4.0 × 10−2 9.9 × 10−3 1.0 × 10−4 5.2 × 10−2 2.0 × 10−4 5.0 × 10−4 9.5 × 10−3 6.4 × 10−3

for predicting all of the ions simultaneously. It is necessary to use a proper combination of the electrodes. To do so, the information obtained above was applied. For example, electrode nos. 3 and 4 are necessary for predicting the concentrations of Ag+ and Cd2+ ions, but they are not sufficient for predicting the Cu2+ ion. Therefore, combinations of one or more of the electrode nos. 1, 2, 5, or 6 could potentially be useful in simultaneously predicting the aforementioned cations. Finally, the presence and absence of the unmodified graphite, electrode no. 7, was also studied. As Table 4 indicates, the predictability of the system for Cu2+ ions was improved with the array {3, 4, 5}, but it is still poor in predicting Cu2+ ion, as its R2 is 0.7807 for the prediction samples. In addition, this system failed to predict the concentrations of the Cd2+ ion. To compensate for this failure, other Cu2+ sensitive electrodes were combined with that array, and thus, arrays {1, 3–5} and {1–5} were also tested. As the results in Table 4 indicate, the ability of the system to predict Cu2+ ion did not improve sufficiently, although the improvement for predicting Cd2+ ion was appreciable. The combination of electrode no. 7 (the unmodified graphite electrode) with {1–5} produced poorer results. However, the inclusion of electrode no. 6 into the array to form the {1–7} array improved the goodness of fit to some extent. Finally, the exclusion of electrode no. 1 from the array (to form array {2–7}) gave the best results. From the obtained results, it can be concluded that array {2–7} would be an optimal choice for the prediction of Cu2+ , Ag+ , and Cd2+ ions in their ternary mixtures. 3.4. Analysis of samples Ten samples containing various concentrations of Cu2+ , Cd2+, and Ag+ ions with or without Zn2+ and Ni2+ ions were also analyzed, and the results are summarized in Tables 5 and 6, respectively. From Table 5, the predictability of the {2–7} array system for all of the ions in the absence of the foreign ions is seen to be excellent (Fig. 3). It is interesting to note that, in the presence of Zn2+ and Ni2+ ions, the exclusion of the cross-selective unmodified graphite

Table 7 Comparison between {2–7} and {2–6} for the predictability in determining the concentration of Cu2+ , Ag+ , and Cd2+ in the complex mixture containing nickel and zinc as foreign ions. Array {2–7} {2–6}

R2 -Cu2+ found in mixture

R2 -Ag+ found in mixture

R2 -Cd2+ found in mixture

0.7249 0.9965

0.9975 0.9984

0.9999 0.9728

electrode no. 7 (i.e., array {2–6}) resulted in a better prediction of Cu2+ , Ag+ , and Cd2+ ion concentration compared to that of the optimal array {2–7} because the presence of foreign ions are most likely detected by electrode no. 7 and its exclusion from the array may result in the best prediction (Fig. 4). The predicted concentrations of the Cu2+ , Ag+ , and Cd2+ ions in the presence and absence of the interfering ions are summarized in Tables 5 and 6. For comparison, the squared correlation coefficients for the prediction of the cations in the presence of the interfering ions are also provided in Table 7 for both the {2–7} and {2–6} arrays. 4. Conclusion A seven-electrode array system consisting of either selective or cross-selective electrodes were used together with a computerized switching system to obtain electrode potentials sequentially. The potential of the electrodes was used as input for the neural network to obtain the concentration of the Cu2+ , Ag+ , and Cd2+ ions in ternary, quaternary, or more complex samples composed of primary ions in the presence or absence of zinc and nickel interfering ions, which naturally coexist with the primary cations in industrial water effluents. A feed-forward, back propagation neural network was applied to train the networks in each experimental run. Optimization of the neural network parameters was also performed. The best electrode arrangements were selected based on their corresponding squared correlation coefficient of calibration and prediction. Array {2–7}

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was found to be the array of choice because it resulted in the best fit for ternary samples. In addition, for predictions of Cu2+ , Ag+ , and Cd2+ in more complex samples (e.g., in the presence of nickel and zinc ions), it is necessary to exclude the unmodified graphite electrode (electrode no. 7) from the array.

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