Ningbo Institute of Materials Technology & Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, China; School of Materials Science and Engineering, Shanghai University, Shanghai 200444, PR China.
Ningbo Institute of Materials Technology & Engineering (NIMTE), Chinese Academy of Sciences, Ningbo 315201, China.
Talanta. 2018 May 1;181:305-310. doi: 10.1016/j.talanta.2018.01.029. Epub 2018 Jan 11.
Pesticides play a critical role in improving crop yield in modern agriculture, but their residues significantly harm the environment and human health. Herein, a novel and simple colorimetric sensor array built on sulfuric acid assisted KMnO fading strategy has been developed for pesticides detection and discrimination. This sensor array is facilely fabricated by KMnO and sulfuric acid through simply adjusting their concentrations and ratios. Hierarchical clustering analysis (HCA) demonstrates that the as-fabricated colorimetric sensor array has a high dimensionality, and shows excellent capability to recognize common kinds of pesticides from potential interferants. Semi-quantitative detection was achieved through combining HCA and corresponding fitting curves. Moreover, the proposed sensor array was successfully applied to detect pesticide residues (e.g. carbaryl) in real samples. The strategy described herein will not only "maximally" simplify the design and fabrication approach, but expand the application fields of colorimetric sensor array methodology towards weak-reactive analytes.
农药在现代农业提高作物产量方面发挥着关键作用，但它们的残留会对环境和人类健康造成严重危害。在此，我们开发了一种新颖而简单的比色传感器阵列，该传感器阵列基于硫酸辅助的 KMnO 褪色策略，可用于农药检测和识别。该传感器阵列通过简单地调整其浓度和比例，由 KMnO 和硫酸制备而成。层次聚类分析（HCA）表明，所制备的比色传感器阵列具有高维度，并且能够出色地区分常见的农药和潜在的干扰物。通过结合 HCA 和相应的拟合曲线，实现了半定量检测。此外，该传感器阵列还成功地应用于实际样品中农药残留（例如，西维因）的检测。本文所描述的策略不仅“最大限度”地简化了设计和制造方法，而且还扩展了比色传感器阵列方法在弱反应性分析物方面的应用领域。
