基于碳量子点可视化识别TNT、PA、Tetryl的 荧光比色传感阵列
基金项目:
辽宁省民生科技计划项目(No. 2021JH2/10300007)，辽宁省自然科学基金项目(2020-MS-131) ，辽宁“百千万 人才工程”培养经费资助
Fluorescent Colorimetric Sensing Array for Visualization of TNT, PA and Tetryl Based on Carbon Quantum Dots
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摘要:为对涉爆案件中的梯恩梯（TNT）、特屈儿（Tetryl）和苦味酸（PA）进行快速、可视化分析研究，结合碳量子点和统计学方法构建了荧光比色传感阵列。通过水热合成法，以水杨酸和盐酸氨基脲为原材料得到的碳量子点A溶液和以植物绿萝为原材料得到的碳量子点B溶液作为敏感点，通过智能手机收集到RGB值，结合主成分分析、层次聚类分析、线性判别分析等模型进行定性分析，通过荧光光谱仪收集数据，得出浓度与荧光强度的线性拟合模型并进行半定量分析，构建出荧光比色传感器阵列。结果表明：3种模型能够对3种炸药共15个平行样正确的归类，准确率达100%；TNT在碳量子点B溶液中拟合度为0.977 3，Tetryl和PA在碳量子点A溶液中，拟合度分别为0.990 2和0.950 9。
Abstract:For a rapid and visual analysis study of TNT, Tetryl and picric acid (PA) in explosive-related cases, a fluorescent colorimetric sensing array was constructed by combining carbon quantum dots and statistical methods. By hydrothermal synthesis method, carbon quantum dot A solution was obtained from salicylic acid and aminourea hydrochloride as raw materials, and carbon quantum dot B solution was obtained from plant green as raw materials, which were used as sensitive points. RGB values were collected by smart phones, combined with models such as principal component analysis, hierarchical cluster analysis and linear discriminant analysis for qualitative analysis, and data were collected by fluorescence spectrometer to derive a linear fitting model of concentration and fluorescence intensity, and a semi-quantitative analysis was performed, which was summarized to construct a fluorescent colorimetric sensor array. The results show that the three models are able to correctly categorize a total of 15 parallel samples of the three explosives with 100% accuracy; the fit of TNT in carbon quantum dot B solution is 0.977 3, and the fit of Tetryl and PA in carbon quantum dot A solution is 0.990 2 and 0.950 9, respectively.
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