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Zinc oxide nanoparticles-based electrochemical sensor for the detection of nitrate ions in water with a low detection limit-a chemometric approach

机译:基于氧化锌纳米颗粒的电化学传感器,用于检测水中的硝酸根离子,具有低检测极限 - 化学计量方法

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摘要

We report for the first time a cyclic voltammetric nitrate sensor with a low detection limit based on the immobilization of zinc oxide nanoparticles on the surface of the platinum working electrode using chitosan membrane. Cyclic voltammetric data demonstrated that zinc oxide nanoparticles can electrochemically reduce nitrate ions to ammonium ions with high conductivity. In order to estimate electroanalytical parameters for each of the nitrate concentrations, Gaussian and Lorentzian curve fitting algorithms were performed on cyclic voltammetric data. Among them, the best analytical performance results were obtained with Gaussian calibration linear model. The zinc oxide modified platinum electrode showed a linear response to nitrate ions over a concentration range from 0.1 to 2.0 mM with a low detection limit and high sensitivity of 10 nM and 39.91 mu A/cm(2) mM, respectively. The nitrate ion concentrations in drinking water samples were determined using Gaussian calibration linear model and the predicted, added nitrate ion concentration values showed good correlation.
机译:我们在使用壳聚糖膜的表面上,第一次报告具有低检测限的循环伏安硝酸盐传感器,所述循环伏安硝酸盐传感器基于氧化锌纳米颗粒在铂膜的表面上的固定化。循环伏安数据证明氧化锌纳米颗粒可以用高导电性将氧化锌纳米颗粒电化学降低硝酸盐离子。为了估计每个硝酸盐浓度的电分析参数,对循环伏安数据进行高斯和洛伦兹曲线拟合算法。其中,利用高斯校准线性模型获得了最佳分析性能结果。氧化锌改性的铂电极显示出对浓度范围的硝酸根离子的线性反应,其浓度范围为0.1至2.0mm,检测极限低,敏感性为10nm和39.91μA/ cm(2)mm。使用高斯校准线性模型确定饮用水样品中的硝酸根离子浓度,并预测的硝酸盐离子浓度值显示出良好的相关性。

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