Silver Nanoparticle-Based Chemiluminescent Sensor Array for Pesticide Discrimination Yi He,* Bo Xu, Wenhao Li, and Haili Yu School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621010, People’s Republic of China S Supporting Information *

ABSTRACT: In this work, we developed a simple, facile, and highly sensitive nanoparticle-based chemiluminescent (CL) sensor array for the discrimination of organophosphate and carbamate pesticides. This CL sensor array is based on simultaneous utilization of the triple-channel properties of the luminol-functionalized silver nanoparticle (Lum-AgNP) and H2O2 CL system containing CL intensity, the time for CL emissions to appear, and the time to reach the CL peak value, which are able to be measured via a single experiment. The triple-channel properties can be simultaneously altered after interaction with pesticides, producing distinct CL response patterns as “fingerprints” related to each specific pesticide, which was subjected to principal component analysis (PCA) to generate a clustering map. Using this sensor array, five organophosphate and carbamate pesticides, including dimethoate, dipterex, carbaryl, chlorpyrifos, and carbofuran, have been well-distinguished at a concentration of 24 μg/ mL. A total of 20 unknown pesticide samples have been successfully identified with an accuracy of 95%. The simple strategy of this study is expected to promote the development of functionalized nanomaterial-based sensor arrays. KEYWORDS: sensor array, nanoparticles, pesticide discrimination, single sensing unit, triple-channel information

INTRODUCTION Pesticides are widely used as herbicides in agriculture to protect crops.1 Organophosphorus compounds and carbamates with high toxicity are the most used pesticides, whose overuse has generated a serious environmental concern and public health risk.2 It is estimated that more than 200 000 people in developing countries die from organophosphate and carbamate pesticide poisoning every year.3 Accordingly, intense research efforts have been directed to develop sensitive and reliable assays for organophosphate and carbamate pesticides in the past few years. To date, various analytical methods have been developed for organophosphate and carbamate pesticide determination. Traditional analytical techniques, such as high-performance liquid chromatography, gas chromatography, and mass spectrometry are commonly used for the determination of these pesticides.4 However, these approaches are timeconsuming and require expensive instruments, complicated preliminary treatment, and highly trained technicians, which are not suitable for low-cost and rapid analysis. In addition, several enzymatic methods and immunoassays have been performed to determine organophosphate and carbamate pesticides via optical and electrochemical readout approaches.5 Although these methods have good sensitivity, the chemical stability of enzymes or antibodies is poor, which greatly restricts their widespread use. With the emergence and fast development of nanotechnology, novel nanomaterial-based sensors for pesticide detection have gained more and more attention because of their low cost and high sensitivity. Metal nanomaterials, such as gold nanoparticles6 and silver nanoparticles (AgNPs),7 semiconductor quantum dots,8 and carbon nanomaterials9 have been successfully employed for design and development of various sensors for organophosphate and carbamate determination with © 2015 American Chemical Society

ultrahigh sensitivity by virtue of their unique surface plasmon resonance, fluorescent properties, and electrochemical activity. Despite the significant progress, these sensors are based on the “lock−key” sensing mode, which relies on a highly selective receptor for each pesticide determination. The limited supply of specific receptors hampers their further applicability. Recently, an array sensing method provides an alternative technology to overcome this problem. In the sensor array, the differential interactions of pesticides with a sensor array generate a distinct response pattern of each pesticide that is used for classification and identification. Very few scaffolds have been employed for array sensing of pesticides, such as dye− poly(amidoamine) dendrimer systems10 and Fe3O4 nanoparticles.11 However, the systems have high detection limits (generally from 1 μM to 1 mM) and need a number of specialized probes as the sensing elements, resulting in complicated operation and bad reproducibility. Therefore, it is highly desirable to develop simple, highly sensitive, and reproducible sensor arrays with limited sensing elements for the detection of pesticides. Chemiluminescent (CL) assays have inherent advantages, such as a low detection limit, wide linearity range, and simple operation.12 Moreover, the CL reagents, such as luminol, can reduce silver nitrate to form luminol-functionalized silver nanoparticles (Lum-AgNPs).13 Luminol molecules are immobilized on the surface of Lum-AgNPs via Ag−N covalent interaction. The Lum-AgNPs can react with hydrogen peroxide (H2O2), producing a CL emission. Interestingly, in comparison Received: Revised: Accepted: Published: 2930

February 4, 2015 March 4, 2015 March 9, 2015 March 9, 2015 DOI: 10.1021/acs.jafc.5b00671 J. Agric. Food Chem. 2015, 63, 2930−2934


Journal of Agricultural and Food Chemistry

Then, the mixture solution was allowed to cool to room temperature. The Lum-AgNPs were separated from the mixture via centrifugation and washed with ultrapure water. The collected Lum-AgNPs were dispersed into 30 mL of ultrapure water and stored at 4 °C before use. Analyte Pesticide Response. The stock solutions of five organophosphate and carbamate pesticides were prepared in toluene. For each pesticide detection, the as-prepared Lum-AgNPs (100 μL) were added to pesticide solutions (10 μL) with a final concentration of 24 μg/mL and incubated for 10 min at room temperature, followed by successive injection of H2O2 solution (300 μL, 10 mM), and the CL emission was simultaneously measured. Five replicates were tested for each pesticide to give a 3 channel (Ta, Tp, and CL intensity) × 5 pesticide × 5 replicate data matrix. To identify randomly selected pesticide samples, similar procedures were also carried out. Data Analysis. The CL patterns were processed using a classical principal component analysis (PCA) in MATLAB (version 7.0). After the analysis, three principal components were generated, which represented linear combinations of the response matrices obtained from the raw data matrix [3 channels (Ta, Tp, and CL intensity) × 5 pesticides × 5 replicates]. The first two most significant principal components were employed to produce a two-dimensional plot. The identification of unknown samples, which were randomly prepared by three separate researchers, was made by estimating Mahalanobis distance close to known group centroids from the training matrix.

to a classic luminol−H2O2 system, the Lum-AgNP−H2O2 CL system is a relatively slow process and dynamically tunable according to adjusting various reaction conditions, such as pH and the H2O2 concentration.14 A single experimental operation can obtain triple-channel information containing CL intensity, the time for CL emissions to appear (Ta), and the time to reach the CL peak value (Tp).14 It has been demonstrated that various pesticides can influence the luminol CL system with different degrees.15 Additionally, some pesticides have a strong affinity for AgNPs.16 Thus, the addition of different pesticides will cause distinct CL responses. On these bases, we develop a simple and sensitive CL sensor array based on the utilization of triplechannel properties of the Lum-AgNP−H2O2 CL system for the discrimination of organophosphate and carbamate pesticides for the first time. The developed sensor array allows for the differentiation of five organophosphate and carbamate pesticides (Figure 1) with detection limits of 24 μg/mL.

RESULTS AND DISCUSSION Principle of the CL Sensor Array Based on the LumAgNP−H2O2 System. The CL sensor array is based on the triple-channel properties (Ta, Tp, and CL intensity) of the LumAgNP−H2O2 CL system (Figure 2). Lum-AgNPs were prepared via a previously reported method,14 which were characterized by TEM, as shown in Figure S1 of the Supporting Information.

Figure 1. Chemical structures of five selected pesticides.

Moreover, the triple-channel properties are easily simultaneously detected via a single experiment, thereby greatly simplifying the operation procedure.

Figure 2. Schematic diagram of the principle of the CL sensor array based on the triple-channel properties of the Lum-AgNP−H2O2 CL system.


Chemicals and Materials. Silver nitrate, absolute ethanol, sodium hydroxide, and hydrogen peroxide were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Luminol was obtained from Sigma-Aldrich. Dimethoate, dipterex, carbaryl, chlorpyrifos, and carbofuran (Figure 1) were supplied by J&K Scientific, Ltd. (Shanghai, China). All of the reagents are used as received without further purification. Ultrapure water (18.2 MΩ cm−1) was produced from a Millipore Milli-Q system and used throughout the experiment. Instrumentations. All of the CL measurements were carried out on an ultraweak luminescence analyzer (BPCL, China) with a fixed voltage of −700 V. The morphology of the as-prepared Lum-AgNPs was investigated using JEM-2100F field emission transmission electron microscopy (TEM) operating at 200 kV. Synthesis of Lum-AgNPs. Lum-AgNPs were synthesized by the published protocol.14 Typically, a mixture solution of AgNO3 aqueous solution (4 mL, 5 mM) and absolute ethanol (10 mL) was heated to 60 °C under vigorous stirring and a luminol solution (0.5 mL, 10 mM) dissolved in 0.1 M NaOH aqueous solution was rapidly added. The solution was heated at 60 °C with vigorous stirring for another 2 h.

As expected, the Lum-AgNPs can react with H2O2, producing a slow CL kinetic process, as shown in Figure 3. When 0.3 mL of 10 mM H2O2 was injected into 0.1 mL of Lum-AgNP solution, the CL emission did not emerge immediately, which was completely different from the traditional luminol-based CL systems. After 9 s, CL emission gradually appeared and the corresponding intensity increased with the increase of time and reached its maximum at 276 s. Thus, a single experiment could obtain triple-channel properties (Ta, Tp, and CL intensity) from this CL system. The mechanism of this CL system contains the following three processes:14 (1) Lum-AgNPs were partially dissolved by H2O2 to form Ag+ ions, producing HO• radicals, which were adsorbed on the surface of AgNPs. (2) The formed HO• radicals reacted with H2O2 and luminol, generating superoxide anion and luminol radical, respectively. (3) Further reaction between the superoxide anion radical and luminol radical 2931

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(Cbf), were chosen as the sensing targets, as shown in Figure 1. As illustrated in Figure S4 of the Supporting Information, the presence of analyte pesticides (24 μg/mL) caused a variety of CL responses. This result confirmed that different pesticides would influence the CL kinetic process of the Lum-AgNP− H2O2 system with different degrees, leading to distinct response patterns. To better investigate the effect of various analyte pesticides on the CL sensor array, the signal change rate (ΔI/ I0) for each pesticide as the fingerprint was calculated, as shown in Figure 4. Car and Chr are observed to significantly increase

Figure 3. CL kinetic curve of the Lum-AgNP−H2O2 system. Reaction conditions: 0.1 mL of Lum-AgNP solution and 0.3 mL of 10 mM H2O2 aqueous solution.

generated excited-state 3-aminophthalate anions, resulting in a CL emission. The concentration of H2O2 and luminol on the surface of AgNPs was low. Moreover, the CL reaction occurred under static conditions rather than stirring (see the Experimental Section). Therefore, the CL emission was controlled by the diffusion process of H2O2. On the initial stage, no CL emission was observed because of the lack of adequate H2O2 around the Lum-AgNPs. With the reaction proceeding, the amount of H2O2 around the Lum-AgNPs increased, leading to the increase of HO• radicals and appearance of CL emission (Ta). When the consumption and diffusion of H2O2 reached a state of equilibrium around the Lum-AgNPs, the CL intensity attained its maximum, corresponding to Tp. The diffusion process of H2O2 was demonstrated by investigating the effect of the H 2 O 2 concentration on Ta and Tp. As shown in Figure S2 of the Supporting Information, with the decrease of the H2O2 concentration, Ta and Tp became longer. Besides, with the decrease of the AgNP concentration, Ta and Tp also became longer, which further confirmed the presence of the diffusion process (see Figure S3 of the Supporting Information). The dissolution of AgNPs to form Ag+ ions was confirmed by inductively coupled plasma−atomic emission spectroscopy. The supernatant prepared by centrifugation of the CL reaction solution was found to possess Ag element, whose content was 13.05 μg/mL, which was evidently over that of the control sample (2.03 μg/mL). Furthermore, the effect of various radical scavengers on the CL system was investigated (see Table S1 of the Supporting Information), which demonstrated the presence of various radicals in the CL emission process. On the other hand, as seen from Figure 1, the structures of organophosphate and carbamate pesticides contain sulfur, phosphorus, or nitrogen groups, which are easy to adsorb on the surface of AgNPs via covalent interaction.16 Additionally, the reducing group of −OH or −NH− in pesticides reacted readily with the oxygen-containing intermediate radicals.17 The differential interactions between the Lum-AgNP−H2O2 CL system and various pesticides generate distinct CL responses related to each specific pesticide. Fingerprints of Pesticides Generated via the TripleChannel Properties (Ta, Tp, and CL Intensity) of the LumAgNP−H2O2 System. As a proof-of-concept system, five organophosphate and carbamate pesticides with different molecular structures, including dimethoate (Dim), dipterex (Dip), carbaryl (Car), chlorpyrifos (Chl), and carbofuran

Figure 4. Fingerprints of five selected pesticides based on the CL response patterns (ΔI/I0) at 24 μg/mL analyte concentration. ΔI = I − I0, where I and I0 are the values of Ta, Tp, or CL intensity in the presence and absence of various pesticides, respectively.

Ta by 140.2 and 180.5%, respectively, while Dim and Dip decreased Ta by 27 and 100%, respectively. Cbf made a mild impact on Ta, and the signal was increased by 29.9%. The effect of analyte pesticides on Tp of the sensor array was also studied (olive-colored column diagram in Figure 4). All of the pesticides decreased Tp with different degrees. Car, Chl, and Cbf decreased Tp by 28.1, 21.1, and 32%, respectively. However, Dim and Dip decreased Tp more effectively, because they decreased the signal by 65.9 and 100%. The influence of various pesticides on CL intensity was further investigated (blue-colored column diagram in Figure 4). Dim and Dip were found to apparently decrease the CL intensity by 96.5 and 100%, while Chl slightly enhanced the CL intensity by 9.4%. Besides, Cbf and Car evidently enhanced the CL intensity by 135.7 and 93.9%. The distinct CL responses after adding different pesticides could be explained as follows. The presence of a reductive hydroxyl group (−OH) and amino group (−NH−) for Dip and Dim could consume the oxidants in the CL system, such as H2O2, HO• radicals,17 and superoxide anion radical, leading to the decrease of the CL intensity and increase of Ta and Tp. It was reported that Chl, Car, and Cbf could react with oxidants, such as H2O2, to form unstable intermediates, which accelerated the oxidation of luminol,18−20 leading to the increase of the CL intensity and Ta and decrease of Tp. Owing to the difference of molecular structures for Chl, Car, and Cbf, the corresponding triple-channel information (Ta, Tp, and CL intensity) was not all exactly the same. CL Sensor Array for Discrimination of Pesticides. These differential CL responses, like “fingerprints”, were further subjected to PCA; PCA converts the patterns of the training matrix (3 channels × 5 pesticides × 5 replicates) to canonical scores, as shown in Figure 5. The first two most significant principal components contain 87.5 and 9.8% of the variation, 2932

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distinct patterns related to each pesticide, which were able to be incorporated into a novel pesticide sensor array with a single sensing unit. Benefiting from the high sensitivity and simple operation of CL technology, several organophosphate and carbamate pesticides, such as dimethoate, dipterex, carbaryl, chlorpyrifos, and carbofuran, have been recognized and discriminate with a high sensitivity. To the best of our knowledge, this work is the first case of recognition and discrimination of organophosphate and carbamate pesticides in aqueous solution by virtue of a nanoparticle-based CL sensor array that is fabricated by a simple Lum-AgNP−H2O2 CL system with triple-channel information. In view of the simple preparation method and available surface modification technique of Lum-AgNPs as well as ready dynamically tunable CL process of this system, we believe that the present study opens a new avenue for the development of simple and facile high-throughput assays for the identification of organophosphate and carbamate pesticides, which may hold great potential application in environmental protection and food safety. In our future work, we will further improve the discrimination ability of the nanoparticle-based CL sensor array to extend its application for recognition of other pesticides.

Figure 5. Canonical score plot of the two primary principle components for the discrimination of five selected pesticides (24 μg/mL).

respectively, which occupies 97.3% of total variation. At a pesticide concentration of 24 μg/mL, PCA verifies that the canonical response patterns of the pesticides to the triplechannel properties of the Lum-AgNP−H2O2 CL system were classified into five separate groups that corresponded to each pesticide. Moreover, the five repeated measurements of each pesticide were in a narrow distribution in the PCA plots. These results indicated that the five organophosphate and carbamate pesticides at concentrations of no less than 24 μg/mL could be effectively distinguished. The performance of the CL sensor array was further tested by the discrimination of unknown samples. The identification accuracy of 20 unknown samples at the 24 μg/mL level was found to be 95% (see Table S3 of the Supporting Information). It meant that the CL sensor array could identify pesticides as low as 24 μg/mL. For a sensor array, another challenge is to discriminate different pesticides at different concentration levels. To elucidate the application potential of the CL sensor array, we performed experiments with Chl, Car, and Cbf at three different concentrations (1, 5, and 10 μg/mL). As shown in Figure 6, various concentrations of three pesticides were able to


S Supporting Information *

TEM image of Lum-AgNPs (Figure S1), effect of the H2O2 concentration on (a) Ta and (b) Tp of this CL system (Figure S2), effect of different addition amounts of Lum-AgNPs on this CL system: (a) 50 μL, (b) 100 μL, and (c) 150 μL (Figure S3), CL kinetic curve of the Lum-AgNP−H2O2 system in the absence (first curve) and presence of various pesticides (Figure S4), effects of radical scavengers on CL of the Lum-AgNP− H2O2 system (Table S1), training matrix of the response patterns against pesticides at 24 μg/mL (Table S2), and detection and identification of unknown pesticides at 24 μg/mL (Table S3). This material is available free of charge via the Internet at


Corresponding Author

*E-mail: [email protected]. Funding

This work was supported by the Foundation of Science and Technology Department of Sichuan Province (Grant 2015JY0053) and the Doctoral Program of Southwest University of Science and Technology (Grant 14zx7165). Notes

The authors declare no competing financial interest.

Figure 6. PCA score plot for the discrimination of Chl, Car, and Cbf at three different concentrations (1, 5, and 10 μg/mL).


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be discriminated from each other in the PCA score plot. In addition, the CL responses from different concentrations of one pesticide produced patterns around a center; thus, each pesticide could be differentiated. In summary, we have demonstrated that various analyte pesticides could influence Ta, Tp, and light intensity of the LumAgNP−H2O2 CL system simultaneously. The signal changes in such triple-channel properties of this CL system resulted in 2933

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DOI: 10.1021/acs.jafc.5b00671 J. Agric. Food Chem. 2015, 63, 2930−2934

Silver nanoparticle-based chemiluminescent sensor array for pesticide discrimination.

In this work, we developed a simple, facile, and highly sensitive nanoparticle-based chemiluminescent (CL) sensor array for the discrimination of orga...
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