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Gold Nanoparticle-Based Colorimetric Sensor Array for Discrimination of Organophosphate Pesticides Nafiseh Fahimi-Kashani, and Mohammad Reza Hormozi-Nezhad Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01616 • Publication Date (Web): 14 Jul 2016 Downloaded from http://pubs.acs.org on July 14, 2016

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Analytical Chemistry

Gold Nanoparticle-Based Colorimetric Sensor Array for Discrimination of Organophosphate Pesticides Nafiseh Fahimi-Kashani†, M. Reza Hormozi-Nezhad*,†,‡ † ‡

Department of Chemistry, Sharif University of Technology, Tehran 11155-9516, Iran

Institute of Nanoscience and Nanotechnology, Sharif University of Technology, Tehran, Iran

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Abstract There is a growing interest in developing high-performance sensors monitoring organophosphate pesticides, primarily due to their broad usage and harmful effects on mammals. In the present study, a colorimetric sensor array consisting of citrate-capped 13 nm gold nanoparticles (AuNPs) has been proposed for the detection and discrimination of several organophosphate pesticides (OPs). The aggregation induced spectral changes of AuNPs upon OP addition has been analyzed with pattern recognition techniques, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). In addition, the proposed sensor array has the capability to identify individual OPs or mixtures of them in real samples.

Keywords: Colorimetric sensor array; Organophosphorus pesticides; Gold nanoparticles (AuNPs); Aggregation


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Introduction Organophosphate pesticides (OPs) have been widely used to manage pest problems1,2. Consequently, OP residues may enter into the food chain through air, water and soil, causing several health problems to animals and humans3. OPs have been categorized as toxicological class I (extremely toxic) by the U.S Environmental Protection Agency (EPA)4. The high toxicity of OPs is attributed to their irreversible phosphorylation and inactivation of acetylcholinesterase (AChE) in the central and peripheral nervous system5,6. This leads to accumulation of in vivo acetylcholine (ACh) in body, and thus inflicting serious clinical complications including respiratory tract injuries, paralysis or even death7,8. Therefore, development of simple, sensitive, rapid, efficient and reliable methods for determination of residual amounts of OPs in food and environmental samples is of great importance to public health concerns. Several analytical methods, including liquid/gas chromatography-mass spectrometry (LCMS/GC-MS) 9,10, immunoassay 11,12, chemiluminescence 13, fluorescence 14,15, spectrophotometry 16,17

, surface enhanced Raman spectroscopy 18,19 and electrochemical methods

20,21

have been so

far developed for accurate determination of OP pesticides, offering high sensitivity and selectivity. However, they are time-consuming, costly and rely on complicated instrumentation and highly trained manpower, which limits their application for on-site screening of OPs. Hence, it is a need of the hour to develop sensitive, yet rapid and cost-effective systems for detection and discrimination of OPs. Colorimetric sensor array technology has proven to be a powerful analytical approach for the discrimination of a wide variety of analytes, such as explosives and foods

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, biomolecules, pathogenic bacteria and fungi

22-24

30-32

, toxic gases

25-27

, beverages

. This approach utilizes cross-

reactive (rather than specific) sensing elements to produce composite, olfactory-like responses 3 ACS Paragon Plus Environment


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which is an optical fingerprint of each analyte33. Array based sensing approaches have been recently emerged for pesticide detection and identification as well34-36. However, in order to enhance the discrimination ability of these sensor arrays, a large number of chemically responsive sensing elements is necessary which are usually provided from instable and expensive reagents. So, tedious and taxing sensing probe synthesis implores new technique for simple, cost-effective, and rapid, yet sensitive measurements for practical applications. Plasmonic nanomaterials, mainly gold and silver nanoparticles, have been recently exploited as powerful sensing materials in pesticide colorimetric assays

37,38

. These assays are based on

plasmon resonance wavelength changes of NPs triggered directly or indirectly by the analyte and can be colorimetrically detected. Colorimetric sensor arrays based on AuNPs are very rare39-41; notwithstanding, many successful colorimetric probes have been reported for single analyte detection. Moreover, AuNPs are usually required to be modified with receptors42,43 in these sensors. Accordingly, there have been great strides in terms of utilizing unmodified noble metal NPs as simple sensing elements in the fabrication of colorimetric sensor arrays. Herein, we have developed a colorimetric sensor array, based on aggregation of citrate-capped 13 nm AuNPs at different pHs and ionic strengths, capable of identifying five Organophosphate pesticides. In the presence of target pesticides, AuNPs at different pHs/ionic strengths exhibit different aggregation behaviors, leading to diverse color changes. Distinct absorbance response patterns can be used as fingerprints to identify OPs with the help of multivariate analysis methods, such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). Finally, the proposed colorimetric sensor array was successfully employed to evaluate its applicability in the determination of OPs in rice and paddy water.


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Experimental Section Materials Hydrogen tetrachloroaurate (HAuCl4.3H2O) (99.5%), tri-sodium citrate, sodium hydroxide (NaOH), hydrochloric acid (HCl 37%), and Ethanol were purchased from Merck. Analytical standards of azinphos-methyl (AM), chlorpyrifos (CP), fenamiphos (FP), pirimiphos-methyl (PM), phosalone (PS), carbaryl (CB), carbofuran (CF), methiocarb (MC), pirimicarb (PC), imidacloprid (IC), thiamethoxam (TM), tebuconazol (TB) and propiconazol (PP) were obtained from Sigma-Aldrich Corporation. The stock solutions of pesticides were prepared in ethanol (0.1 g. L-1). Milli-Q grade water, with resistivity of 18.2 MΩ, was used in all the experiments. Instrumentation Absorbance spectra were recorded using a PerkinElmer (Lambda25) spectrophotometer with the use of 1.0 cm glass cell. Measurements of pH were performed with a Denver Instrument Model of 270 pH meter equipped with a Metrohm glass electrode. Transmission electron microscopy (TEM) images were recorded with a Zeiss EM900 microscope (Germany) at an accelerating voltage of 200 kV. Preparation of citrate-capped gold nanoparticles (AuNPs) Unmodified AuNPs were prepared according to the modified Turkevich method described previously44,45. Briefly, 50 mL solution containing 1mM of HAuCl4 was prepared and heated under reflux. At the boiling point, 5 mL of 38.8 mM of trisodium citrate was added to this solution under vigorous stirring and the mixture was heated under reflux for an additional 30 min during which the color changed to deep red, indicating the formation of AuNPs. The solution was allowed to cool at room temperature and stored at 40C for further use. The as-prepared 5 ACS Paragon Plus Environment


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AuNPs with an average diameter of about 13 nm exhibited a characteristic surface plasmon peak centered at 520 nm (Fig. S1) Fabrication of colorimetric sensor array and detection of OPs Three ionic strength levels (0, 5, 15 mM NaCl) and three pHs (4.5, 6.5 and 9.0) were selected as the sensing array conditions for the discrimination of OPs. To this end, various amounts of 5×10-2 M NaCl and 2.5×10-3 M HCl or NaOH solutions were added to 2.45 mL AuNPs solution with final concentration of 2.4×10-9 M. Then 5×10-2 mL of water (blank) or different concentrations of OPs were added to each NP. All UV-Vis spectra were recorded after 10 min incubation till aggregation of AuNPs reaches equilibrium. All the analyses were conducted in triplicates. Chemometric analysis were carried out on data matrix using Matlab R2014b (version 8.4) and SYSTAT (version 13.0). Real sample analysis In order to investigate the potential applicability of the proposed array in real samples, detection of OP residues was carried out in paddy water and rice samples. Paddy water sample was filtered using filter paper (11 µm), diluted 5-fold with DI water, and then spiked with 160 ng. mL-1 of OPs and 4000 ng. mL-1 of other pesticides. Spiked rice sample was prepared according to previous report 46. Briefly, 5 g of rice sample was grounded to fine powder, spiked with 320 ng. ml-1 of OPs and 4000 ng. mL-1 of other pesticides. The samples were thoroughly mixed with 10 mL acetonitrile and 2 mL water, followed by ultrasonic extraction for 30 min and centrifugation for 10 min at 4000 rpm. Subsequently, the supernatant was evaporated at 40 0C and the dried residue was diluted with 2 mL of ethanol. Then, the analyses were performed on


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both paddy water and rice samples (in triplicate trials) the same as the above procedure for OPs detection. Result and discussion Fabrication and principle of the array OPs induced aggregation of AuNPs through chemical interaction or electrostatic attractive force mechanisms has been reported over the past years

47,48

. As a result of aggregation, changes in

UV–vis spectra, and hence color changes of colloidal AuNPs from red to purple or blue, can be readily observed. As shown in Figure 1 different functional groups present in the structure of the selected OPs, and moreover in their anionic, cationic or neutral forms at different pHs, make them having different capabilities in inducing AuNPs aggregation. In addition, ionic strength also plays a crucial role in the aggregation process by reason of salts’ ability to shrink the electrical double-layer on AuNPs surface. Therefore, we designed a sensor array for the detection and identification of OPs by employing AuNPs at different pHs and ionic strengths (Scheme 1). A distinct colorimetric response pattern has been obtained for each target pesticide by using such cross-reactive sensing elements. In order to provide the condition in which the sensor responds only to the target pesticides, the effect of different parameters, including pH and ionic strength, on the aggregation of AuNPs was evaluated. Due to different functional groups in the structure of OPs, controlling pH seems to be critical in the aggregation process. In the pH range of (4.5 to 9), insignificant spectral changes of AuNPs were observed (Fig. S2B). Therefore, three acidic (pH=4.5), neutral (pH=6.5) and basic (pH=9) regions were selected for sensing. In addition, as mentioned earlier, the aggregation process could be affected by different concentrations of the electrolyte49,50. However, as shown



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in Fig. S2A, no spectral and color change were observed in NaCl concentration range of 0-25 mM. Accordingly, three levels of ionic strength 0, 5 and 15 mM were chosen. Finally, a 3×3 array of NPs at different pH/ionic strength levels was fabricated for further experiments (Scheme 2). Colorimetric sensor array responses to OPs Organophosphate pesticides including AM, CP, FP, PM and PS, at concentration range of 40400 ng. mL-1, were exposed to AuNPs at different pH/ionic strengths and the spectral responses were recorded (Fig S3). For instance, the response profiles of various AuNP sensing elements to different pesticides (240 ng. mL-1) are shown in Figure 2. As can be seen, different colorimetric signals were obtained owing to the interactions of AuNPs at different pH/ionic strengths, with the selected OPs. A representative photograph of the array response against 240 ng.mL-1 of the selected OPs indicates that the color change profiles of the array are unique fingerprints for each OP and can be visualized by naked eyes (Figure 2J). Azinphos-methyl (AM) induced the aggregation of AuNPs at neutral and acidic conditions, whereas no spectral changes occurred at basic medium (Figure S3A). It was found that AM hydrolyzes in acidic medium and yields benzotriazine derivatives as hydrolysis products. In contrast, alkaline hydrolysis of AM produces hydroxyethyl derivatives which are unable to cause AuNPs aggregation51. It can be suggested that aggregation take places owing to the presence of protonated forms of nitrogen atoms in the structure of hydrolysis products at acidic and neutral media. In addition, it was found that AM can reaches AuNPs surface, and thus mediates AuNPs aggregation through hydrogen bindings, as a result of increasing NaCl concentration. No significant aggregation was observed upon the addition of chlorpyrifos (CP) in basic medium. Nonetheless, aggregation of AuNPs at acidic and neutral conditions is due to pyridine structure of CP. The aggregation behavior of CP is totally 8 ACS Paragon Plus Environment

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different from other OPs. Moreover, ionic strength has no considerable effect on AuNPs aggregation in the presence of CP (Figure S3B). Similarly, pirimiphos-methyl (PM) reveals the same aggregation behavior as CP, but its numerous functional groups lead to higher aggregation intensity than CP’s (Figure S3D). Fenamiphos (FP) is fairly stable at acidic and neutral media; however, in basic conditions the degradation of FP is found to be very rapid (Figure S3C). The main hydrolysis products at pH 9 are phenolic-sulfoxide derivatives, formed by phosphate ester hydrolysis and oxidation at the methylthio group52. Therefore, FP mediates higher aggregates than other OPs at basic media. Additionally, protonated nitrogen functional groups of FP induce aggregation of AuNPs through electrostatic and hydrogen bond interactions in acidic media. Increasing ionic strength, as a powerful factor, causes AuNPs aggregation even at basic conditions. At high ionic strength, the aggregation of AuNPs was observed in the presence of FP at different pH levels, yet with distinct aggregation behavior. Phosalone (PS) induces AuNPs aggregation at acidic pH values. Additionally, increasing NaCl has little effect on AuNPs aggregation (Figure S3E). Alkaline hydrolysis of PS produces benzoxozolone derivatives51 that cause no aggregation. Therefore, each OP pesticide has a unique response pattern which can be used for identification. Evaluation of the array’s capability to discriminate between OPs Standard chemometric methods were employed in order to examine the potential of the colorimetric sensor array in recognition of OPs in a broad range of concentrations, and in quantitative analysis. Thirteen wavelengths of the response spectra corresponding to each sensing element (450, 500, 520, 550, 580, 600, 620, 650, 680, 700, 720, 750 and 780 nm) were chosen for quantitative comparison of the spectral changes of the array. So, 117-dimentional vectors (13 wavelengths × 9 sensing elements) were defined accordingly based on ∆A values 9 ACS Paragon Plus Environment


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(i.e., difference between absorbance after adding analyte and absorbance of AuNPs, as blank). Hierarchical cluster analysis (HCA), an unsupervised method based on the grouping of the analyte vectors according to their spatial distances in their full original vector space53, was performed using the minimum variance (Ward’s) method for different OPs at concentration range of 120-400 ng. mL-1. As seen in Fig. 3, the HCA dendrogram demonstrates a 100% correct classification (in triplicate trials). The collected response patterns at all mentioned concentrations are shown in Fig. S4. Unique barplot response patterns for different OPs (240 ng. mL-1) represents the capability of the array for identification of the selected OPs (Fig. S5). Afterwards, Linear discriminant analysis (LDA)and leave-one-out cross validation methods were employed to quantitatively differentiate among response profiles, fingerprint to each OP. LDA is a supervised method finding the linear combination of variables in order to maximize class discrimination and differentiate two or more classes of objects54. As a result of reducing the training matrix size (9 sensing elements × 5 OPs × 8 concentration × 3 replicates) and transforming it to canonical factors, a well-clustered two-dimensional score plot with a classification accuracy of 100% was obtained. As shown in Figure 4, 162 canonical colorimetric response patterns were clustered into 5 distinct groups. According to LDA results, two canonical factors (71.83% and 24.02%) revealed 95.85% of the variance in the data and all the OPs were clearly clustered into 5 distinct groups in this pattern recognition method. The results show that the colorimetric sensor array has the ability of differentiating OPs with different concentrations. Selectivity of the colorimetric array To assess the applicability of the prepared colorimetric sensor array for discrimination of the selected OPs in practical applications, the response profiles of eight potentially interferent pesticides, belonging to three different classes of carbamates, neonicotinoids and triazoles, were 10 ACS Paragon Plus Environment

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evaluated for selectivity purposes. Since no spectral changes of AuNPs at different conditions was observed in the presence of other pesticides at concentrations of 4000 ng. mL-1 (Fig. S6), thereby no interferences were identified in the presence of the pesticides of interest.



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Color difference map of the array In order to qualitatively visualize the colorimetric sensor array responses, color difference maps were obtained by subtracting of the absorbance before and after exposure to the selected OPs at three visible wavelengths (i.e., 520, 600 and 700 nm). Difference maps presented in Figure 5 display the color-change profiles of the sensing elements after interaction with various OPs at different OP concentrations (40-400ng.mL-1). They provide robust fingerprint patterns for each OP allowing their discrimination even without statistical techniques. In agreement to the selectivity study, the color difference maps show insignificant responses against possible interferents at selected wavelengths. OPs calibration curves The relationship between overall array response and OP concentration was examined (Fig. S7). A calibration curve was observed over linear range of 80-400 ng.mL-1 for AM, 120-280 ng.mL-1 and 320-800 ng.mL-1 for CP, 80-400 ng.mL-1 for FP, 40-320 ng.mL-1 PS, and 40-800 ng.mL-1 for PM based on the largest response among nine sensor elements for each OP (Fig. S8). Subsequently, the limits of detection (LODs) were calculated so as to examine the sensitivity of the array (Table 1). The results demonstrate that the proposed colorimetric sensor array can be employed not only for discrimination but also for quantitative analysis of the selected OPs using HCA, color difference map and calibration curves. Furthermore, all LODs meet the requirements for determination of OPs in real-world applications.


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Discrimination of mixtures Discrimination of OP mixtures is far more challenging than pure OPs, yet critically important due to the mixture use of pesticides for plant protection. In this regard, the response of the sensor for combinations of OPs (five OPs at concentration of 240 ng. mL-1, triplicate measurements) were recorded. It was found that binary mixtures of AM, CP and FP, and also ternary mixture of PM, PS and FP had different responses compared to their pure form (Fig. S9). LDA score-plot for various mixtures demonstrates that all four mixtures are visually separated, and the crossvalidation accuracy of identification was found to be 100% (Fig. 6A). Furthermore, color difference map of mixtures visually shows considerable different response patterns from individual OPs (Fig. 6B). Identification of pesticides in real samples To evaluate the efficacy of the proposed colorimetric array in analyzing complex samples, rice and paddy water were tested. The response profiles of various OPs, spiked to rice (320ng.mL-1) and paddy water (160ng.mL-1) samples are shown in Fig. S10. The data was subjected to LDA analysis and the resulting score-plot reveals that the selected OPs at two different concentrations appeared in well-separated groups with no apparent overlap. Spiked rice and paddy water samples with different concentration of OPs are clustered in five groups, the same as the standards (Fig. 4) which shows the applicability of the sensor array for discrimination of OPs in real-world samples (Fig. 7). Conclusion In summary, nine AuNPs at different pH/ionic strengths were employed as simple plasmonic sensing elements in the development of a colorimetric sensor array for the detection and 13 ACS Paragon Plus Environment


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discrimination of five organophosphate pesticides, including azinphos-methyl (AM), chlorpyrifos (CP), fenamiphos (FP), pirimiphos-methyl (PM) and phosalone (PS), at concentration ranges of 120-400 ng. mL-1. The variety of functional groups in OP structures leads to aggregation of AuNPs. On the other hand, the aggregation behavior of AuNPs against OPs are completely different at different pH and ionic strength media. The proposed colorimetric array could efficiently discriminate among individual OPs and their mixtures. Finally, it was found that the sensor array can detect various OPs in real samples, successfully.


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Associated content Supporting Information Additional information as noted in text. Optimization of pHs/ionic strength, UV/Vis spectra of sensor elements against different concentration of OPs, Response pattern barplots, Real photograph and UV/Vis spectra of the array against other studied pesticides, Calibration plots, UV/Vis spectra relating to mixture and real sample analysis.



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Acknowledgement Financial support of Sharif University of Technology and Iran National Science Foundation (Grant No.

93028580) is gratefully acknowledged. Also, the authors would like to

acknowledge Dr. Arafeh Bigdeli, who is doing a postdoc in our group for her helpful comments.

Corresponding Author * Email: [email protected]


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Legend to Figures Figure 1 Chemical structure of five OP pesticides. Figure 2 UV–vis spectra of AuNPs and their aggregates induced by AM, CP, FP, PM and PS (at concentration of 240 ng. mL-1) at (A) pH=4.5 and 0 mM NaCl (A0), (B) pH=6.5 and 0 mM NaCl (N0), (C) pH=9.0 and 0 mM NaCl (B0), (D) pH=4.5 and 5 mM NaCl (A5), (E) pH=6.5 and 5 mM NaCl (N5), (F) pH=9.0 and 5 mM NaCl (B5), (G) pH=4.5 and 15 mM NaCl (A15), (H) pH=6.5 and 15 mM NaCl (N15), (I) pH=9.0 and 15 mM NaCl (B15). (J) The color change patterns of nine AuNPs at different pHs/ionic strengths against different OPs. Figure 3 HCA dendrogram with Ward linkage for OPs. No confusions in classification for organophosphates were observed in 162 experiments. All of the experiments were performed in triplicate. The concentration range of OPs were 120-400 ng. mL-1; all other pesticides were at 4000 ng. mL-1. Figure 4 Two-dimensional Canonical score-plot for OPs, other pesticides and a control illustrating nanoparticles-based array’s ability to discriminate various OPs. All of the experiments were performed in triplicate. The concentration ranges of organophosphates and carbamates was 120-400 ng. mL-1; other pesticides were at 4000 ng. mL-1. Figure 5 Color difference maps for various concentrations of AM, CP, FP, PM, PS and eight potentially interferent pesticides. Figure 6 (A) Difference maps (B)Two-dimensional score plot, illustrating discrimination of individual OPs from their mixtures, at OP concentrations of (a) 240 ng. mL-1. Figure 7 Two-dimensional LDA plot, after combining the test set (real sample) with the training set data. Rice sample and paddy water were spiked with OPs at concentrations of 320 and 120 ng. mL-1, respectively in presence of 4000 ng. mL-1 of carbamate pesticides (shown in crossed triangles and circles).



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Scheme 1 Scheme 1 Diagrams of colorimetric sensor array and detection principle of OPs based on unmodified AuNPs


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Scheme 2 Scheme 2 Illustration of fabricated 3×3 array of AuNPs at different ionic strengths/pHs. AuNPs at pH=4.5 and 0 mM NaCl (A0), pH=6.5 and 0 mM NaCl (N0), pH=9.0 and 0 mM NaCl (B0), pH=4.5 and 5 mM NaCl (A5), pH=6.5 and 5 mM NaCl (N5), pH=9.0 and 5 mM NaCl (B5), pH=4.5 and 15 mM NaCl (A15), pH=6.5 and 15 mM NaCl (N15), pH=9.0 and 15 mM NaCl (B15) are represented in the table



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Table 1 Linear range and LOD for OPs based on calibration plots determined using largest response among nine sensor element.


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Figure 1



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Figure 2


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Figure 3



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Figure 4


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Figure 5



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Figure 6


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Figure 7



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Abstract Graphic


Gold-Nanoparticle-Based Colorimetric Sensor ... - ACS Publications

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