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Keywords:

  • organo-halide pesticides;
  • quartz crystal nanobalance (QCN);
  • principal component analysis (PCA);
  • environmental contamination

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

This study reports the capability of a system based on single silicon OV-25 (a methylphenylsilicon (∼75% phenyl)) modified quartz crystal nanobalance (QCN) sensor with the application of principal component analysis (PCA) as a pattern recognition technique for the detection and determination of some organo-halide pesticides (Telone, Methyl Iodide, Endosulfan, and Methyl Bromide) in aqueous solutions. It was found that the sensor response is linear against the organo-halide pesticides in the concentration ranged between of 5–30 mg L−1 for Endosulfan and 5–60 mg L−1 for three other studied pesticides. The correlation coefficients (0.992, 0.989, 0.994, and 0.993), the sensitivity factors (2.27, 2.41, 3.61, and 1.44 Hz/mg L−1), and the lower limit of detections (1.4, 4.6, 2, and 4.6 mg L−1) were obtained for Telone, Methyl Iodide, Endosulfan, and Methyl Bromide, respectively. PCA has been performed based on the silicon OV-25-modified QCN sensor measurement results, and the signals were transformed into feature space. It was found that the transformed values of Telone, Methyl Iodide, Endosulfan, and Methyl Bromide were well separated. Thus, the developed QCN sensor has very good applicability to successfully determine organo-halide pesticides. Also, it was found that over 93.5% of the data variance could still be explained by using two principal components (PC1 and PC2). © 2013 American Institute of Chemical Engineers Environ Prog, 33: 267–274, 2014


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

Inherently, organo-halide pesticides have attracted much attention due to their severe toxicity and ability to deplete the ozone layer. They are generally very stable compounds and can persist for some long time. Organo-halide pesticides, generally, degrade slowly and are fat-soluble, so they can accumulate in living organisms [1]. They have such an ability to volatilize in warm climates that they can spread over long distances, with measurable concentrations detected near the Arctic Circle and in Alpine areas where they have not been used at all [2, 3]. Due to the aforementioned properties, their usage is prohibited in most developed countries. However, they are still extensively used in developing and under-developed regions because of their low cost and their effectiveness. Therefore, they can be detected in various environmental matrices such as soil [4], vegetation [5], biota [6], sediments [7], air [8], and water [9].

Organo-halide pesticides reach the aquatic environment through direct run-off, leaching, careless disposal of original chemical containers, equipment washings, and etc. Their presence in water constitutes a severe risk to aquatic ecosystems, animals, and human health and can cause severe health effects such as irritation, digestive disorders, headache, lung congestion, kidney damage, liver damage, convulsion, and even cancer. For these reasons, policies were coined to reduce contamination of ground and surface water. Maximum admissible concentration of pesticides and related products for drinking water is 0.1 μg L−1 for individual pesticides and 0.5 μg L−1 for total concentrations given by the European Union Drinking Water Directives. Additionally, pesticides residue in surface water must be less than 1–3 μg L−1. Some reports indicates that residues of organo-halide pesticides presented much higher degree than the acceptable level for drinking waters, surface waters, and underground waters of some regions of countries [10].

Table 1 indicates the most important preconcentration techniques for determination of organo-halide pesticides. These preconcentration techniques usually coupled with gas chromatography (GC) or high performance liquid chromatography (the detection limits ranged between 10 and 40 ng L−1) [15]. Despite their very good sensitivity and reliability, these techniques do have some practical limitations such as instrumental complexity, difficult sample manipulation, specialist operator implementation, and need for preconcentration steps. Consequently, these shortcomings motivated the researchers to look for new and simple detection methods capable of determining trace amounts of organo-halide pesticides in water. Given this, quartz crystal nanobalance (QCN) sensors appear as a promising solution in this regard.

Table 1. The most important preconcentration techniques for determination of organo-halide pesticides.
Preconcentration techniqueDLLMEaSPEbSPMEcHS-SPMEd
  1. a

    Dispersive liquid–liquid microextraction (DLLME).

  2. b

    Solid-phase extraction (SPE).

  3. c

    Solid-phase microextraction (SPME).

  4. d

    Headspace solid-phase microextraction (HS-SPME).

Detection limits1–25 ng L−112–51 ng L−10.03–0.9 ng g−12 to 70 ng L−1
Reference11121314

Recent years have witnessed the development of the QCN sensors and considerable interest has been arisen in their application for monitoring various environmental pollutants [16, 17]. Also, the QCN technique is used to detect some pesticides. Sensors based on QCN have been developed for detection and determination of telone [lower limit of detection (LLD) = 1.89 mg L−1] [18], imidacloprid and thiacloprid (LLD = 0.25 mg L−1) [19], carbaryl insecticide and 3,5,6-trichloro-2-pyridinol (LLD = 11 µg L−1), organophosphorus and carbamate (LLD = 1 mg L−1) [20, 21]. Although these sensors were described to be much less sensitive than chromatographic techniques coupled with mass spectroscopy detection [11-15], they are fast, simple, and continuous. They can, also, be used directly for the field operation. However, to the best of the researchers' knowledge application of QCN technique for determination of organo-halide pesticides in aqueous solutions has not been studied yet.

Thereafter, with respect to the importance of determination of organo-halide pesticides in aqueous solutions, this study developed a simple and sensitive sensors, based on silicon OV-25-modified QCN, for detection and determination of some organo-halide pesticides (Telone (1,3-dichloropropene), Methyl Iodide (Iodomethane), Endosulfan (6,7,8,9,10,10-Hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepine-3-oxide), and Methyl Bromide (Bromomethane)). Moreover, interference of some major salts present in water (NaCl, NaHCO3, CaSO4, and MgSO4) as potential interferents were studied [22]. A chemometrics method, based on principal component analysis (PCA), was, further, used for discrimination among organo-halide pesticides in the fabricated QCN sensor.

EXPERIMENTAL

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

Reagents and Materials

To meet the objectives of the study, Endosulfan, Methyl Iodide, and Telone were obtained from Sigma Aldrich Co.; Silicon OV-25 was obtained from Fulka chime (Switzerland); Methyl Bromide was purchased from Linhai Jianxin Chemical Co. (China) and Chloroform, Acetone, and all other reagents were purchased from Merck Chemicals (Germany).

Instrumentation

The study made use of 10-megahertz (10 MHz) AT-cut quartz crystals with gold electrodes on both sides which were purchased from International Crystal Manufacturer (ICM, OK). For QCN experiments, a homemade apparatus, described in researchers' previous work, was used [23].

Preparation of Silicon OV-25-Modified QCN Electrode

A solution casting method was used to cast polymer film on the surfaces of the quartz crystal electrodes. Using a Hamilton microliter syringe (Hamilton Bonaduz AG, Switzerland), 3 μL of silicon OV-25/Chloroform solution (0.2%, w/v) was dropped on top of the gold electrode of the quartz crystal. A Hamilton microliter syringe (Hamilton Bonaduz AG, Switzerland) was used to drop 3 μL of silicon OV-25/Chloroform solution (0.2%, w/v) on top of the gold electrode of the quartz crystal. A thin layer of silicon OV-25 was obtained after solvent evaporation. To regenerate the electrode, polymeric coating was dissolved in chloroform followed by drying with acetone.

Procedures

All measurements were performed in a glass cell with an internal volume of 15 mL. All solutions were filtered using a syringe filter (0.2 μm) before their injection into the cell. First, 10 mL of distillated water was added to the cell. Then, a certain volume of analytes was injected into the water by Hamilton microliter syringes and the frequency shift of the crystal (Δf) was recorded to plot calibration curve for each analyte. To recover the crystal, after the draining of the solution, air stream was purged through the cell for 10 min. It should be mentioned from the outset that all measurements were performed at room temperature (25°C).

Data Processing

The PCA method is widely used to classify compounds and mixtures [17, 18]. PCA calculations, using singular-value decomposition (SVD) algorithm, were performed with the MATLAB software version 7.0.4. PCA was applied according to our previous article [17].

RESULTS AND DISCUSSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

Development and the Amount of Coating Material

To determine the optimum thickness of the coating, the researchers measured the response time and sensitivity of the sensor. The sensitivity of the sensor depends on the coating thickness while the coating thickness itself depends on the silicon OV-25 concentration in the casting solution. Therefore, the crystal was coated with 3 μL of various concentrations of silicon OV-25/Chloroform solutions (0.1%–0.5%, w/v). The frequency response of the crystal with different amounts of silicon OV-25 was recorded on exposure to 40 mg L−1 Telone solution (Figure 1). It was found that with increasing the coating thickness, the magnitude of the response (frequency shift) also increases. The maximum frequency shift was recorded for crystal coated using 0.5% w/v silicon OV-25/Chloroform solutions. As can be seen in Figure 1, decrease in the slope regime of frequency shift was obvious after 0.2% w/v; however, the film produced by higher concentration than 0.5%, w/v disturbs the resonance stability which occasionally leads to failure in the quartz crystal oscillation. On the other hand, response time obtained for crystal with different thicknesses of silicon OV-25 revealed that the QCN crystal with a thicker coating exhibit a higher response time for a constant concentration of Telone (Figure 2). The shortest response time was obtained for a crystal coated by 0.1% (w/v) silicon OV-25 solution.

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Figure 1. The effect of the silicon OV-25 concentration on frequency changes of silicon OV-25-modified quartz crystal electrode exposed to 40 mg L−1 Telone.

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Figure 2. The effect of the silicon OV-25 concentration of casting solution (polymer thickness) on response time of OV-25-modified quartz crystal electrode exposed to 40 mg L−1 Telone solution.

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Based on these observations and considering the tradeoff between response time and sensitivity, the coating obtained by using 3 μL of 0.2% (w/v) silicon OV-25 solution was chosen as the optimum thickness. It had significant sensitivity and its response time was less than 25 min. Therefore, this response time improved the response times reported by other researchers for the determination of pesticides using QCN sensors. For instance, Karousos et al. [21] reported the response times in the order of 30–45 min, and Kim et al. [24] reported the response times in the range of 40–50 min for the determination of organophosphorus and carbamate pesticides.

Sensors Response

Typical frequency changes of a quartz crystal coated using a 3 μL of 0.2% (w/v) silicon OV-25 solution, was recorded upon exposure to 50 mg L−1 Telone solution (Figure 3). The oscillation frequency of the working crystal decreased due to the adsorption of Telone on the surface of the modified electrodes. In the structure of investigated pesticides, there are halide groups containing negative charge. On the other hand, there is Si atoms with positive charges in the structure of silicon OV-25. The electro static interactions are responsible for adsorption of studied pesticides. To recover the modified quartz electrode after a measurement (regeneration of the sensor), it was exposed to distilled water (Figure 3). The results showed that desorption of Telone using distilled water is protracted. So, to recover the coat, after draining the solution, hot air stream was purged through the cell for 10 min, as the investigated pesticides are volatile compounds. They will be evaporated in the process where, hot air was purged through the cell. It will return the frequency of the crystal to its initial value. This observation confirms that the sensing interaction between the silicon OV-25 coating and analytes is a typical physical adsorption.

image

Figure 3. Typical frequency changes of the silicon OV-25-modified quartz crystal electrode in exposure to 50 mg L−1 of Telone solution: (a) Sample injection; (b) Distilled water.

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Linearity of the Response

Linearity of the sensor was evaluated by exposing the silicon OV-25-modified quartz crystal electrode to various concentrations of Telone solutions ranging from 5 to 80 mg L−1 for silicon OV-25-modified quartz crystal electrode (Figure 4). It is obvious from these results that the value of the frequency differences increases monotonically with increasing Telone concentration. This result suggests the possibility of fabricating a sensor using silicon OV-25-coated QCN electrode. The calibration curves were created by plotting the frequency shifts against the various concentrations of analytes (Figure 5). The frequency shifts were recorded during the exposure of samples to the modified electrode for 1500 s. As shown in Figure 5, there was a good linear relationship in the range of 5 to 30 mg L−1 for Endosulfan and 5–60 mg L−1 for three other pesticides. The linearity of the response range for OV-25-coated QCN electrode is more extensive than the linearity of the response range optical sensor (0.1 to 10 mg L−1) fabricated for determination of chlorine [25]. The values of 0.992, 0.989, 0.994, and 0.993 were calculated for correlation coefficient (R2) of Telone, Methyl Iodide, Endosulfan, and Methyl Bromide, respectively. These values are comparable with correlation coefficient (0.9992) obtained for the determination of chlorine by optical sensor [25]. According to these results, all tested analytes can be determined, satisfactorily, within the concentration range of 5–30 mg L−1 for Endosulfan and 5–60 mg L−1 for three other pesticides.

image

Figure 4. Frequency shifts of a silicon OV-25-modified quartz crystal electrode as a function of time in direct exposure to various concentrations of Telone: (a) 5 mg L−1, (b) 10 mg L−1, (c) 15 mg L−1, (d) 20 mg L−1, (e) 30 mg L−1, (f) 40 mg L−1, (g) 50 mg L−1, (h) 60 mg L−1, (i) 70 mg L−1, and (j) 80 mg L−1.

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image

Figure 5. Calibration curves for determination of Telone, Methyl Iodide, Endosulfan, and Methyl Bromide, using silicon OV-25-modified quartz crystal electrode (Exposure time: 1500 s). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

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Reproducibility of the Response

To investigate the reproducibility of the sensor response, the silicon OV-25-coated QCN was exposed, frequently, to 30 mg L−1 Endosulfan and 40 mg L−1 of three other pesticides, and the frequency shift was recorded. The experiment was repeated five times (Table 2). After reaching equilibrium, air was purged through the cell until desorption was fully achieved. The relative standard deviations (R.S.D.s) for organo-halide pesticide indicated that the sensor has a good repeatability for detection of all compounds.

Table 2. Reproducibility of the silicon OV-25-coated QCN electrode exposed to organo-halide pesticide at the concentration of 30 mg L−1 for Endosulfan and 40 mg L−1 for three other pesticides.
CompoundFrequency shift (Hz)Frequency shift (Hz)Frequency shift (Hz)Frequency shift (Hz)Frequency shift (Hz)R.S.D. %a
  1. a

    Relative standard deviation.

Telone96989597951.3
Methyl Iodide99102100951053.7
Endosulfan1171141201131142.5
Methyl Bromide52515453522.4

The obtained values for R.S.D.s were 1.3%–3.7% which compares with R.S.D.s of 4%–10% reported previously for organo-halide pesticide quantitation by GC-MS and the values of 5%–15% reported previously for dispersive liquid–liquid micro extraction [11, 15]. This improvement indicates the superior response reproducibility of the QCN based method.

Sensitivity of the Sensor

The sensitivity of a sensor is expressed as the slope of the calibration curve. The sensitivity of the silicon OV-25-coated electrode against target analytes was calculated. The bar graph (Figure 6) illustrates the sensitivity of the QCN sensors toward examined analytes. As expected, the sensors showed different sensitivities to the organo-halide pesticide. The maximum and minimum sensitivities of sensor were toward Endosulfan and Methyl Bromide, respectively.

image

Figure 6. Sensitivity of silicon OV-25-modified quartz crystal electrode toward organo-halide pesticide. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

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LLD of the Sensor

The LLD is defined as the lowest concentration of analyte that can be detected and is defined as a concentration in which the response is, significantly differentiated from noise signal. The LLD was calculated from calibration graph according to the method described by Miller and Miller [26]. The LLD values and other analytical parameters of examined compounds are given in Table 3.

Table 3. Analytical characteristic parameters for the determination of organo-halide pesticides by silicon OV-25-modified quartz crystal electrode.
CompoundR2aSensitivity [Hz(mg L)−1]LLDb (mg L−1)Linear calibration range (mg L−1)
  1. a

    Square correlation coefficient.

  2. b

    Low limit of detection.

Telone0.9922.271.45–60
Methyl Iodide0.9892.414.65–60
Endosulfan0.9943.6125–30
Methyl Bromide0.9931.444.65–60

The reported LLD for determination of carbaryl and dichlorvos is 1 mg L−1 [21]. Although this value is slightly lower than the LLD of silicon OV-25-coated electrode, it should be noted that the carbaryl and dichlorvos QCN sensor can only detect total pesticide content. In the other words, it cannot distinguish between different pesticides. The other problem of this sensor is enzymes deactivation. If any of the three enzymes becomes deactivated, this will lead to a response suggesting the false presence of pesticides in the sample [21].

The LLD of the developed QCN sensor for identification of imidacloprid and thiacloprid in aqueous solutions is 0.25 mg L−1 [19] which is rather lower than the sensitivity of the presented QCN sensor, based on silicon OV-25, in this research.

Interference

The most common cations in water are calcium, magnesium, and sodium while the most common anions are chloride, sulfate, and bicarbonate. The most found salts in water are sodium chloride, calcium sulfate, magnesium sulfate, and sodium bicarbonate [22]. Interference of major salts (NaCl, NaHCO3, CaSO4, and MgSO4) in waters, as expected major constituents of waters and also as potential interferents, was tested. The frequency shifts of silicon OV-25-coated electrode on exposure to 40 mg L−1 Telone in water, was recorded. The frequency shifts of the same electrode in exposure to each of interferents in a 10-fold excess concentration (400 mg L−1) were also recorded. It should be noted that changes in frequency of less than 4.8 Hz (equal with 5% of Telone response) was considered as “no response” (Table 4).

Table 4. Frequency changes of an OV25-modified quartz crystal electrode exposed to 40 mg L−1 of Telone and 400 mg L−1 of various possible interferents.
CompoundsChemical abbreviationConcentrationsFrequency shift (Hz) for OV25
Telone1,3-D40 mg L−198
Sodium chlorideNaCl400 mg L−163
Calcium sulfateCaSO4400 mg L−1No response
Magnesium sulfateMgSO4400 mg L−1No response
Sodium bicarbonateNaHCO3400 mg L−1No response

Life Time

To investigate the life time of the sensor, the researchers recorded the responses eight times over a period of 2 months (Table 5) by exposing the silicon OV-25-coated electrode to Telone solution (40 mg L−1). The results showed no significant difference between the recorded responses. Between each set of measurements, the crystal (cell) was stored in a moisture and contamination-free medium. The results suggest that the sensor can be stored at ambient temperatures for many months without loss of performance.

Table 5. Durability testing of the silicon OV-25-coated electrode exposed repeatedly to 50 mg L−1 Telone.
Testing period after coatingFrequency shift (Hz)
1 h98
5 h96
1 day98
2 day94
1 week102
2 week96
1 month101
2 months95

Identification of Organo-Halide Pesticides using PCA

The capability of the silicon OV-25-modified QCN to differentiate among between Telone, Methyl Iodide, Endosulfan, and Methyl Bromide was performed through PCA. The adsorption dynamics of the silicon OV-25-modified QCN exposed to Telone, Methyl Iodide, Endosulfan, and Methyl Bromide are shown in Figure 7. As it can be seen, the outline of the adsorption process for species are the same, but there are some differences in the shape of sensor response exposed to various analytes. The difference in the response shape of the sensor can be related to the type of analytes and their different affinity for adsorption of silicon OV-25 coating. So, shape difference of each response can be used to distinguish different organo-halide pesticides using PCA.

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Figure 7. The frequency responses of silicon OV-25-modified QCN electrode exposed to 30 mg L−1 the analytes: (a) Methyl Bromide, (b) Telone, (c) Methyl Iodide, and (d) Endosulfan.

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The PCA method has been successfully applied to classify compounds and mixtures [17, 18]. PCA contains an orthogonalization procedure such as SVD that decomposes the primary data matrix by projecting the multidimensional dataset onto new coordinate base formed by the orthogonal directions with data maximum variance. The data matrix consists of a number of experiments, each consisting of a number of variables. The eigenvectors of the data matrix are called principal components (PCs). Generally, the kth principal component, PCk, is a linear combination of the n response vectors (Xn,j) for the analyte under study:

  • display math(1)

where n is the number of the variables, j indicates different samples and the coefficients (an,k) are called loading. The magnitude of each eigenvector is expressed by its own eigenvalue, which gives a measure of the variance related to that principal component. The variance is related to the quantity of information which is supplied by the component. By elimination of the less important eigenvectors, it is possible to achieve fewer vectors without any considerable information loss. So, during data processing, the results are transformed in a plane or in a space of the first two or three eigenvectors. The coordinates of the data in the new base are called their score. The scores plot is usually used for the classification of the data clusters. In this study, the input data of the primary matrix are the response of the QCN sensor at various time windows (at every 10 s for 1500 s) after the organo-halide pesticides injection into the cell. All analytes were exposed to the cell for four different concentrations including 10, 15, 20, and 30 mg L−1. To account for different response magnitudes from different concentrations, the researchers normalized and standardized the responses. Moreover, PCA calculations, using SVD algorithm, were performed with the MATLAB software version 7.0.4.

The results of the PCA indicated that the majority of the information (more than 93.5%) was provided by first principal component (PC1) and second principal component (PC2). The obtained PCA results have been displayed in Figure 8. The graph illustrates the score plot of the PCA data for different concentrations of analytes. As shown in Figure 8, an easy discrimination of species was possible because the data were clustered in four different regions, independent of their concentrations.

image

Figure 8. PCA score plot of Telone, Methyl Iodide, Endosulfan, and Methyl Bromide in the PC1-PC2 plane of the normalized data matrix obtained from the transient responses of silicon OV-25-modified QCN sensor for four different concentrations. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

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CONCLUSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

A simple, inexpensive, quick, and sensitive method based on QCN technique was developed for determination of some organo-halide pesticide in water. QCN frequency shifts versus concentration of analytes exhibited good linear correlation within the concentration range of 5 to 30 mg L−1 for Endosulfan and 5–60 mg L−1 for three other pesticides. The correlation coefficients of calibration curves were 0.992, 0.989, 0.994, and 0.993, for Telone, Methyl Iodide, Endosulfan, and Methyl Bromide, respectively. A PCA was performed based on the frequency response of the sensor in every 10 s as the input data. The results showed that the data for organo-halide pesticide were clustered in four different regions, independent of their concentrations. PCA results indicated that the analytes can be clearly discriminated from each other. It can be concluded that the silicon OV-25 modified QCN can be potentially used to determine organo-halide pesticide compounds in real water samples.

ACKNOWLEDGMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED

This article is published as part of a research project supported by the University of Tabriz Research Affairs Office. The authors are grateful to the University of Tabriz for the financial support.

LITERATURE CITED

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EXPERIMENTAL
  5. RESULTS AND DISCUSSIONS
  6. CONCLUSION
  7. ACKNOWLEDGMENTS
  8. LITERATURE CITED
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