An Electronic Tongue Colorimetric Sensor Array for Discrimination and

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An Electronic Tongue Colorimetric Sensor Array for Discrimination and Quantitation of Metal Ions Based on Gold Nanoparticle Aggregation Xin Li, Siqun Li, Qing-Yun Liu, and Zhengbo Chen Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b01139 • Publication Date (Web): 11 Apr 2019 Downloaded from http://pubs.acs.org on April 11, 2019

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Analytical Chemistry

An Electronic Tongue Colorimetric Sensor Array for Discrimination and Quantitation of Metal Ions Based on Gold Nanoparticle Aggregation Xin Li,1 Siqun Li,1 Qingyun Liu,2 Zhengbo Chen1* 1Department 2College

of Chemistry, Capital Normal University, Beijing, 100048, China

of Chemical and Environmental Engineering, Shandong University of Science and Technology,

Qingdao, 266590, China * Corresponding author. Tel.: 010-68903047 E-mail: [email protected] ABSTRACT: The sensor arrays, called as “electronic tongues”, provide an alternative to time-consuming detection approaches. In this work, a colorimetric sensor array composed of three recognition receptors (cysteine, L-glutathione, and melamine) was developed for fast discrimination of toxic metal ions. Different recognition receptors exhibited different binding affinities toward metal ions, causing diverse gold nanoparticle (AuNP) aggregation behaviors and generating distinct colorimetric response patterns. These response patterns as “fingerprints” can be quantitatively analyzed by linear discriminant analysis (LDA). The sensor array achieved well discrimination of 6 kinds of metal ions (Ti4+, Cr3+, Mn2+, Fe3+, Pb2+, and Sn4+) in deionized water and real samples. It possessed good reproducibility and exhibited a linear range of 100-900 nM (R2=0.97) for Ti4+, 100-900 nM (R2=0.97) for Cr3+, 100900 nM (R2=0.98) for Mn2+, 100-1000 nM (R2=0.92) for Sn4+, 100-800 nM (R2=0.94) for Fe3+, and 100-900 nM (R2=0.97) for Pb2+. The sensor array shows feasible potential in environmental monitoring and simplification of water quality analysis.

sensing elements-based sensor arrays, rather than specific receptor-target binding interactions, imitate mammalian olfactory and gustatory systems26 to produce composite responses unique to a target, the unique responses can be quantified and differentiated by digital imaging and linear discriminant analysis (LDA).27 Researchers have developed various kinds of colorimetric sensor arrays for the discrimination of metal ions. For example, Du et al. reported a colorimetric sensor array for discrimination of 10 metal ions based on 1-(2-pyridinylazo)-2-naphthaleno (PAN)- and bromothymol blue (BTB)-functionalized ionic microgels.28 Reza Hormozi-Nezhad et al. presented anti-aggregation of gold nanoparticles (AuNPs)-based sensor arrays for colorimetric discrimination of 4 metal ions.29 Our research group reported a colorimetric sensor array for metal ion identification based on the competition between thiols and urease for binding with the metal ions.30 The primary goal of this work is to develop a simple colorimetric sensor array for discrimination of metal ions. As depicted in Scheme 1, the sensor array consists of three receptor units (cysteine (Cys), L-glutathione reduced (GSH), and melamine (Mel)), which were used to create different binding affinities with various metal ions. The six metal ions

INTRODUCTION Toxic metal accumulation can cause serious damage to environment and human beings.1-3 The harmful impact of toxic metal ions has urged wide research on their detection and identification.4-6 Current approaches, such as inductively coupled plasma mass spectroscopies (ICP-MS) and atomic absorption spectrometry (AAS), are regarded as the gold standard for detection of metal ions.7,8 However, the requirements for expensive device, complex procedure, and inflexibility for on-site analysis hampered their applications in point-of-care testing and resource-poor settings. By contrast, colorimetric sensors have emerged as an effective approach for metal ion detection because of their advantages lying in simplicity, high sensitivity, high selectivity, real-time, and naked eye observation.9-14 However, most of these methods often rely on the “lock and key” to the target metal ions, seldom satisfy the need of detecting diverse metal ions or identifying complex samples containing the mixture of metal ions.15 To address the issue, colorimetric sensor array system16-18 has emerged as a powerful tool toward the detection and discrimination of diverse analytes.19-25 The cross-responsive

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metal ion was added to each tube, and incubated for another 10 min, the absorbance intensity of AuNPs at 520 and 620 nm was recorded. This process was repeated for the six kinds of metal ions to generate six replicates of each.

(Ti4+, Cr3+, Mn2+, Fe3+, Pb2+, and Sn4+) were tested as a proof of concept. In the absence of metal ions, recognition receptorfunctionalized AuNPs remain in a dispersion state (Scheme 1A). Whereas the addition of metal ions triggers the receptor unit-dispersed AuNPs to tend to aggregate (Scheme 1B). The degrees of AuNP aggregation are directly related to the binding ability between receptor units and metal ions. Array’s distinct colorimetric response fingerprint for each analyte is captured by monitoring absorbance changes. The data matrix containing the response and replicates of each metal ion was processed by LDA. The sensor array exhibited a substantial promise for the discrimination of individual metal ion with various concentrations and also the mixture of two, three, or even four metal ions.

RESULTS AND DISCUSSION Optimization of the Detection Conditions. In order to obtain the best discrimination performance, detection conditions were optimized. Since signal output of the sensor array depends on different AuNP aggregation behaviors, the volume and pH of phosphate buffer saline (PBS) for making AuNP aggregation are crucial. Thus, experiments of AuNPs in PBS of different volumes in a range of 0-400 μL again colorimetric responses of AuNPs were conducted. As shown in Fig. S1(A,B), the maximum absorbance at 525 nm was obtained from 160 μL of PBS. Considering that the more dispersed the AuNPs are in the absence of metal ions, the more sensitive the detection will be after adding metal ions, 160 μL was selected as the optimum volume of PBS. Similarly, when pH was 7.38, the colorimetric response at 525 nm reached a maximum (Fig. S1(C,D)). So, a suitable pH for this assay was 7.38. Moreover, the effects of Cys, GSH, and Mel concentrations on the array’s performance were investigated. As shown in Fig. S2, when Cys, GSH, and Mel concentrations were 400 nM, 650 μM, and 20 nM, resepectively, the absorbances at 525 nm reached the maximum. This allows for a greater degree of recognition receptors-metal ion interaction, resulting in an increase in the level of AuNP aggregation. Metal Ion Discrimination by Linear Discriminant Analysis (LDA). To confirm the discrimination ability of the proposed sensor array, 6 metal ions, including Ti4+, Cr3+, Mn2+, Fe3+, Pb2+, and Sn4+, were selected as models for the assay. The absorption responses of the sensor array in the presence of these metal ions with different concentrations (300, 400, 500, 600, 700, 800, 900, 1000, 5000, 10000 nM) were investigated (Table S1-S10). The responses of the sensor array (Δ0-Δi), where Δi andΔ0 were the absorption of AuNP solution (Δ=OD520 nm- OD620 nm) in the presence and absence of the metal ions, respectively, to each metal ion were measured six times in parallel, generating a matrix of 6 metal ions×3 recognition receptors× 6 replicates, LDA analysis converts the matrix into canonical factors and the three factors can be visualized in a three-dimensional (3D) plot under the 95% confidence ellipses (Fig. 1(A-J)). As seen from LDA scores for each concentration of the six metal ions, 6 metal ions were clustered into 6 different clusters by LDA, indicating that they can be effectively discriminated by LDA. In order to visualize the distribution of these metal ions in the LDA scores more intuitively, we utilized the centroid diagram to localize the position of each metal ion at the same concentration (Fig. 1(A1-J1)). On the basis of these figures, it is possible to discriminate these metal ions in a concentration range from 300 to 10000 nM. In comparasion with other metal ion sensor arrays (Table S11), our sensor array possessed comparable or more satisfactory sensitivity. In addition, the heat map shows that the addition of metal ions resulted in a variety of colorimetric responses. Ti4+, Cr3+, Sn4+ Mn2+, and Pb2+ were preferentially captured by the Cys and Mel, respectively. The lower the color key, that is, the smaller the value (Δ0-Δi), the smaller the binding capacity of recognition receptors to metal ions (Fig. 2A). Obviously, among the three

Scheme 1 (A) The Formation of Cys/GSH/Mel-Functionalized AuNPs. (B) The Structural Sketch of Cys-Metal Ion Bbinding. (C) Schematic of the Colorimetric Sensor Array Based on AuNP Aggregation for Discrimination of Metal Ions.

EXPERIMENTAL SECTION Materials. Sodium citrate (Na3C6H5O7·2H2O), chloroauric acid (HAuCl4), cysteine (Cys), L-glutathione reduced (GSH), and melamine (Mel) were purchased from Sigma-Aldrich. FeCl3·6H2O, MnCl2·4H2O, CrCl3·6H2O, Pb(NO3)2, SnCl4, and TiCl4 were obtained from Sinopharm Chemical Reagent Beijing Co., Ltd. The purity of all chemical compound of metal ions is 99.9%. Other chemicals were of analytical reagent grade. Milli-Q purified water (18.2 MΩ at 25 °C) was used as a solvent in all experiments. Instrumentation and Software. Ultraviolet-visible (UVVis) absorption spectroscopy was conducted by a UV-2550 spectrophotometer (Hitachi, Tokyo, Japan). The matrix of absorbance data was processed by LDA in SPSS for the discrimination (version 13). Heat plot was drawn by MeV-4-90 based on the absorbance of AuNPs at 520 and 620 nm. The data of centroid diagram were processed by IBM SPSS Statistics 19. Response charts, contour profiles of colors, and centroid diagram of the three recognition receptors to different concentrations of metal ions were drawn by Origin 9. Colorimetric Assay of Metal Ions by the Sensor Array. The metal ion detection study was conducted by the following procedure: 340 μL of deionized water, 200 μL of 0.4324 nM AuNP solution, and 160 μL of PBS (pH=7.38) were loaded into a 1.5 mL tube to form solution I. Then, 200 μL of 400 nM Cys, 200 μL of 650 μM GSH, and 200 μL of 20 nM Mel were separately added into the three copies of the same solution I, and each solution I tube contains one type of receptor. After incubation for 10 min at room temperature, 100 μL of 300 nM


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Analytical Chemistry sensing receptors (Cys, Mel, and GSH), Cys and metal ions had the strongest binding ability. Furthermore, the jackknifed classification matrix shows that the accuracy of discrimination of the six metal ions was improved from 75% for Cys, 72.2% for GSH, and 72.2% for Mel, to 94.4% for Cys and GSH, 91.7% for Mel and GSH, and 91.7% for Mel and Cys, eventually to100% for Cys, Mel, and GSH. It is obvious that the sensor array can identify the six metal ions with 100% accuracy by the use of the mixture of the Cys, Mel, and GSH (Fig. 2B).

differently to diverse concentrations of metal ions. Among these concentrations, the sensor array achieved the maximum response signal to 800 nM Ti4+, 800 nM Cr3+, 200 nM Mn2+, 400 nM Fe3+, 200 nM Sn4+, and 200 nM Pb2+, respectively. In addition, as seen from Fig. 3 (A1-F1), as long as the response values (Δ0-Δi) of any two recognition receptors to the six metal ions are known, the response value (Δ0-Δi) of the third recognition receptor to the six metal ions can be found. Our sensor array was highly sensitive for the discrimination of the six metal ions at nanomolar level (Fig. 4 (A-F), Table S12S17). The linear responses of the six metal ions are shown in Fig. 4 (A1-F1), i.e., 100-900 nM for Ti4+, 100-900 nM for Cr3+, 100-900 nM for Mn2+, 100-1000 nM for Sn4+, 100-800 nM for Fe3+, and 100-900 nM for Pb2+. The linearity of the doseresponse curve indicates that the interactions between the recognition receptors and metal ions were homogeneous and stable.

Figure 3. (A-F) Spidergrams of the three recognition receptors (GSH+Mel+Cys) to different concentrations of metal ions. Vertical axis scale represents the response value (Δ0-Δi), the circumferential coordinate axis represents the different concentrations of metal ion. The black, green, and red loops represent Cys, Mel, and GSH, respectively. (A1-F1) Contour profiles of colors corresponding to (A-F). X-axis scale represents the response value (Δ0-Δi) of GSH, Y-axis scale represents the response value (Δ0-Δi) of Mel, and Z-axis scale represents the response value (Δ0-Δi) of Cys.

Figure 1. Canonical score plots for the first three factors of the colorimetric response pattern analyzed by LDA with the sensor array toward different concentrations of metal ions. (A) 300, (B) 400, (C) 500, (D) 600, (E) 700, (F) 800, (G) 900, (H) 1000, (I) 5000, and (J) 10000 nM. (A1-J1) Centroid diagram to localize the position of each metal ion at the same concentration corresponding to Fig. 1 (A-J). In the figure, red, dark blue, light blue, green, light green, and yellow dots represent Ti4+, Pb2+, Sn4+, Fe3+, Mn2+, and Cr3+, respectively.

Figure 2. (A) Heat plot of the absorption signatures of 300 nM metal ions. Six replicates are shown per metal ion. (B) Jackknifed classification matrix obtained using LDA for three recognition receptors for the six metal ions, each at 300 nM. To evaluate the cross-response and quantitative capability of the sensor array, the discrimination assays of the six metal ions with various concentrations (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 nM) were conducted, respectively. As shown in Fig. 3(A-F), each recognition receptor responds

Figure 4. The LDA plots for (A) Ti4+, (B) Cr3+, (C) Mn2+, (D) Sn4+, (E) Fe3+, and (F) Pb2+ at different concentrations (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1000 nM). Plot of the discriminant factor 1 versus the (1A) Ti4+, (B1) Cr3+, (C1) Mn2+, (D1) Sn4+, (E1) Fe3+, and (F1) Pb2+ concentration.



Discrimination of Mixtures of Metal Ions. We also tried to tentatively discriminate mixtures of metal ions consisting of two sets of metal ions like Ti4+/Mn2+, three sets of metal ions like Ti4+/Mn2+/Sn4+, and four sets of metal ions like Ti4+/Cr3+/Mn2+/Sn4+ with different molar ratios (Ti4+/Mn2+=1:9, 2:8, 3:7, 4:6, 1:1, 6:4, 7:3, 8:2, and 9:1, Ti4+/Mn2+/Sn4+=2:2:6, 2:6:2, 6:2:2, 4:4:2, 4:2:4, and 2:4:4, Ti4+/Cr3+/Mn2+/Sn4+=2:2:3:3, 2:3:3:2, 2:2:2:3, 3:2:2:3, 3:3:2:2, and 3:2:3:3, and the total metal ion concentration was 300 nM) as well as pure metal ions were successfully discriminated from each other, according to the diverse color response pattern (Fig. 5, Table S18-S20), which indicates the array’s potential ability to detect metal ions with complex composition.

Figure 6. Canonical score plots for the first three factors of colorimetric response patterns analyzed by LDA upon the addition of six metal ions in river water samples.

Figure 7. Canonical score plots for the discrimination of six metal ions (all at 300 nM) in the presence of human serum based on the colorimetric signal changes of the sensor array.

Figure 5. Canonical score plots for the first three factors of absorbance response patterns obtained against (A) the mixtures of Ti4+/Mn2+, as well as pure Ti4+ and Mn2+, (B) the mixtures of Ti4+, Mn2+, and Sn4+, as well as pure Ti4+, Mn2+, and Sn4+, and (C) the mixtures of Ti4+, Cr3+, Mn2+, and Sn4+, as well as pure Ti4+, Cr3+, Mn2+, and Sn4+ (at 300 nM). Discrimination of Metal Ions in River Water Samples. In order to further investigate the application of the sensor array to real samples, it was used to identify the metal ions in river water samples obtained from Yuyuantan Park (Beijing). No metal ions (Ti4+, Cr3+, Mn2+, Fe3+, Pb2+, and Sn4+) in the river water samples was detected by ICP-MS (Agilent 7500ce). When these metal ions, each at 300 nM, were separately spiked into this sensor array, as shown in Fig. 6 and Table S21, the river water itself (control) generated a unique array’s response, and the six metal ions distinguished from each other. The first three canonical factors contain 87.3, 8.9, and 3.8% of the variation, occupying 100% of total variation. In addition, the identification of the six metal ions in serum samples also was perform, as seen from Fig. 7, the serum itself generated a unique array’s response, and the six samples were clustered into six different groups by LDA. These results validate our array’s ability of metal ion discrimination in real samples.

CONCLUSIONS In summary, a colorimetric sensor array that is comprised of AuNPs and three recognition receptors (Cys, GSH, and Mel) was developed for discrimination of metal ions, where the colorimetric responses were based on the binding affinity between the recognition receptors and the metal ions-triggered AuNP aggregation. Different metal ions exhibited diverse AuNP aggregation behaviors and colorimetric responses to the three recognition receptors. Using this sensor array, 6 metal ions including Ti4+, Cr3+, Mn2+, Fe3+, Pb2+, and Sn4+ were successfully discriminated at 300 nM level. The sensor array can effectively discriminate metal ion mixtures and different metal ions at multiple concentrations. Moreover, the excellent linearity and reproducibility were also demonstrated. In view of the advantages of simple design and high sensitivity, the sensor array shows great promise for environmental monitoring in remote areas.

AUTHOR INFORMATION Corresponding Author * Phone: +86-010-68903047. E-mail: [email protected]

Notes The authors declare no competing financial interest.

ASSOCIATED CONTENT Supporting Information UV-Vis absorption spectra of AuNP solutions, the training matrix of the colorimetric response patterns against 6 metal ions,


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Analytical Chemistry comparison of different sensor arrays for metal ion discrimination, and the training matrix of the colorimetric response patterns against the mixture of metal ions. These data are available free of charge via the Internet at http://pubs.acs.org.

(15) Lin, Z. Y.; Xue, S. F.; Chen, Z. H.; Han, X. Y.; Shi, G. Y.; Zhang, M. Bioinspired Copolymers Based Nose/Tongue-Mimic Chemosensor for Label-Free Fluorescent Pattern Discrimination of Metal Ions in Biofluids. Anal. Chem. 2018, 90, 8248-8253.

ACKNOWLEDGMENTS

(16) Rakow, N. A.; Suslick, K. S. A Colorimetric Sensor Array for Odour Visualization. Nature 2000, 406, 710-713. (17) Askim, J. R.; Mahmoudi, M.; Suslick, K. S. Optical Sensor Arrays for Chemical Sensing: The Optoelectronic Nose. Chem. Soc. Rev. 2013, 42, 8649-8682.

All authors gratefully acknowledge the financial support of Scientific Research Project of Beijing Educational Committee (Grant No. KM201710028009), Youth Innovative Research Team of Capital Normal University (19530050149), and Capacity Building for Sci-Tech Innovation-Fundamental Scientific Research Funds (19530050179).

(18) Diehl, K. L.; Anslyn, E. V. Array Sensing Using Optical Methods for Detection of Chemical and Biological Hazards. Chem. Soc. Rev. 2013, 42, 8596-8611. (19) Zheng, L.; Will, P. B.; Jon, R. A.; Kenneth, S. S. Differentiation among Peroxide Explosives with an Optoelectronic Nose. Chem. Commun. 2015, 51, 15312-15315.

REFERENCES (1) Yuen, L. H.; Franzini, R. M.; Wang, S.; Crisalli, P.; Singh, V.; Jiang, W.; Kool, E. T. Pattern-Based Detection of Toxic Metals in Surface Water with DNA Polyfluorophores. Angew. Chem. Int. Ed. 2014, 53, 5361-5365.

(20) Lin, H.; Suslick, K. S. A Colorimetric Sensor Array for Detection of Triacetone Triperoxide Vapor. J. Am. Chem. Soc. 2010, 132, 15519-15521. (21) Askim, J. R.; Li, Z.; Lagasse, M. K.; Rankin, J. M.; Suslick, K. S. An Optoelectronic Nose for Identification of Explosives. Chem. Sci. 2016, 7, 199-206.

(2) Fu, F.; Wang, Q. Removal of Heavy Metal Ions from Wastewaters: A Review. J. Environ. Manage 2011, 92, 407-418. (3) Nriagu, J. O. A History of Global Metal Pollution. Science 1996, 272, 223-224. (4) Alexander, D. C.; Costanzo, M. A.; Guzzo, J.; Cai, J.; Charoensri, N.; Diorio, C.; Dubow, M. S. Blazing Towards the Next Millennium: Luciferase Fusions to Identify Genes Responsive to Environmental Stress. Water Air Soil Pollut. 2000, 123, 81-94. (5) Lee, J. H.; Wang, Z. D.; Liu, J. W.; Lu, Y. Highly Sensitive and Selective Colorimetric Sensors for Uranyl (UO22+): Development and Comparison of Labeled and Label-Free DNAzyme-Gold Nanoparticle Systems. J. Am. Chem. Soc. 2008, 130, 14217-14226. (6) Li, N.; Zhang, L.; Chen, Y.; Fang, M.; Zhang, J.; Wang, H. Highly Efficient, Irreversible and Selective Ion Exchange Property of Layered Titanate Nanostructures. Adv. Funct. Mater. 2012, 22, 835-841. (7) Anthemidis, A. N.; Zachariadis, G. A.; Stratis, J. A. On-Line Preconcentration and Determination of Copper, Lead and Chromium(VI) Using Unloaded Polyurethane Foam Packed Column by Flame Atomic Absorption Spectrometry in Natural Waters and Biological Samples. Talanta 2002, 58, 831-840. (8) Kosanovic, M.; Adem, A.; Jokanovic, M.; Abdulrazzaq, Y. M. Simultaneous Determination of Cadmium, Mercury, Lead, Arsenic, Copper, and Zinc in Human Breast Milk by ICP ‐ MS/Microwave Digestion. Anal. Lett. 2008, 41, 406-416. (9) Jiang, Y.; Zhao, H.; Lin, Y.; Zhu, N.; Ma, Y.; Mao, L. Colorimetric Detection of Glucose in Rat Brain Using Gold Nanoparticles. Angew. Chem., Int. Ed. 2010, 49, 4800-4804. (10) Guo, Y.; Wang, Z.; Shao, H.; Jiang, X. Stable Fluorescent Gold Nanoparticles for Detection of Cu2+ with Good Sensitivity and Selectivity. Analyst 2012, 137, 301-304.

(22) T. Minami, N. A. Esipenko, B. Zhang, L. Isaacs, P. Anzenbacher, Jr, “Turn-on” Fluorescent Sensor Array for Basic Amino Acids in Water. Chem. Commun. 2014, 50, 61-63. (23) Zhang, Y.; Askim, J. R.; Zhong, W.; Orlean, P.; Suslick, K. S. Identification of Pathogenic Fungi with an Optoelectronic Nose. Analyst 2014, 139, 1922-1928. (24) Fahimikashani, N.; Hormozinezhad, M. R. Gold-NanoparticleBased Colorimetric Sensor Array for Discrimination of Organophosphate Pesticides. Anal. Chem. 2016, 88, 8099-8106. (25) Yuen, L. H.; Franzini, R. M.; Tan, S. S.; Kool, E. T. Large-Scale Detection of Metals with a Small Set of Fluorescent DNA-Like Chemosensors. J. Am. Chem. Soc. 2014, 136, 14576-14582. (26) You, C. C.; Miranda, O. R.; Gider, B.; Ghosh, P. S.; Kim, I. B.; Erdogan, B.; Krovi, S. A.; Bunz, U. H.; Rotello, V. M. Detection and Identification of Proteins Using Nanoparticle-Fluorescent Polymer 'Chemical Nose' Sensors. Nat. Nanotechnol. 2007, 2, 318-323. (27) De, M.; Rana, S.; Akpinar, H.; Miranda, O. R.; Arvizo, R. R.; Bunz, U. H. F.; Rotello, V. M. Sensing of Proteins in Human Serum Using Conjugates of Nanoparticles and Green Fluorescent Protein. Nat. Chem. 2009, 1, 461-465. (28) Zhou, X. J.; Nie, J. J.; Du, B. Y. Functionalized Ionic Microgel Sensor Array for Colorimetric Detection and Discrimination of Metal Ions. ACS Appl. Mater. Interfaces 2017, 9, 20913-20921. (29) Najafzadeh, F.; Ghasemi, F.; Reza Hormozi-Nezhad, M. AntiAggregation of Gold Nanoparticles for Metal Ion Discrimination: A Promising Strategy to Design Colorimetric Sensor Arrays. Sens. Actuators B 2018, 270, 545-551. (30) He, W. W.; Luo, L.; Liu, Q. Y.; Chen, Z. B. Colorimetric Sensor Array for Discrimination of Heavy Metal Ions in Aqueous Solution Based on Three Kinds of Thiols as Receptors. Anal. Chem. 2018, 90, 4770-4775.

(11) Liu, D.; Qu, W.; Chen, W.; Zhang, W.; Wang, Z.; Jiang, X. Highly Sensitive, Colorimetric Detection of Mercury(II) in Aqueous Media by Quaternary Ammonium Group-Capped Gold Nanoparticles at Room Temperature. Anal. Chem. 2010, 82, 96069610. (12) Chen, W.; Cao, F.; Zheng, W.; Tian, Y.; Xianyu, Y.; Xu, P.; Zhang, W.; Wang, Z.; Deng, K.; Jiang, X. Detection of the Nanomolar Level of Total Cr[(III) and (VI)] by Functionalized Gold Nanoparticles and A Smartphone with the Assistance of Theoretical Calculation Models. Nanoscale 2015, 7, 2042-2049. (13) Du, J.; Zhu, B.; Chen, X. Urine for Plasmonic NanoparticleBased Colorimetric Detection of Mercury Ion. Small 2013, 9, 4104-4111. (14) Li, S.; Xu, L.; Ma, W.; Kuang, H.; Wang, L.; Xu, C. Triple Raman Label-Encoded Gold Nanoparticle Trimers for Simultaneous Heavy Metal Ion Detection. Small 2015, 11, 34353439.



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An Electronic Tongue Colorimetric Sensor Array for Discrimination and

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