LAPSE:2023.1929
Published Article

LAPSE:2023.1929
A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions
February 21, 2023
Abstract
Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.
Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.
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Keywords
chemosensor array, gold nanoparticles, linear discrimination analysis (LDA), multicomponent analysis, organic oxyanions, support vector machine (SVM)
Subject
Suggested Citation
Wang J, Jiang J, Zyryanov GV, Liu Y. A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions. (2023). LAPSE:2023.1929
Author Affiliations
Wang J: Guangxi Key Laboratory of Optical and Electronic Materials and Devices, College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China
Jiang J: Guangxi Key Laboratory of Optical and Electronic Materials and Devices, College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China
Zyryanov GV: Department of Organic and Biomolecular Chemistry, Ural Federal University, Mira 19, 620002 Ekaterinburg, Russia; Institute of Organic Synthesis, Ural Branch of the Russian Academy of Sciences, Kovalevskoy 22, 620219 Ekaterinburg, Russia
Liu Y: Guangxi Key Laboratory of Optical and Electronic Materials and Devices, College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China [ORCID]
Jiang J: Guangxi Key Laboratory of Optical and Electronic Materials and Devices, College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China
Zyryanov GV: Department of Organic and Biomolecular Chemistry, Ural Federal University, Mira 19, 620002 Ekaterinburg, Russia; Institute of Organic Synthesis, Ural Branch of the Russian Academy of Sciences, Kovalevskoy 22, 620219 Ekaterinburg, Russia
Liu Y: Guangxi Key Laboratory of Optical and Electronic Materials and Devices, College of Materials Science and Engineering, Guilin University of Technology, Guilin 541004, China [ORCID]
Journal Name
Processes
Volume
10
Issue
7
First Page
1251
Year
2022
Publication Date
2022-06-23
ISSN
2227-9717
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Original Submission
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PII: pr10071251, Publication Type: Journal Article
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LAPSE:2023.1929
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https://doi.org/10.3390/pr10071251
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Feb 21, 2023
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