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基于离子液体电解质的电化学方法对挥发性有机化合物的物种选择性检测。

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods.

机构信息

Department of Mechanical Engineering, George Mason University, Fairfax, Virginia 22030 United States.

Department of Chemistry & Biochemistry, George Mason University, Manassas, Virginia 20110, United States.

出版信息

ACS Sens. 2023 Sep 22;8(9):3389-3399. doi: 10.1021/acssensors.3c00578. Epub 2023 Aug 17.

Abstract

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CHO solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CHO solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. It is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

摘要

挥发性有机化合物(VOCs)的检测是环境安全和公共健康的重要课题。然而,当前的商业 VOC 探测器存在交叉灵敏度和低重现性的问题。在这项工作中,我们提出了一种基于离子液体(IL)电解质的电化学池,用于对 VOC 进行选择性检测,该方法具有高选择性和可靠性的特点。用基于 IL 的电解质吸收不同 VOC 时测量的伏安图表现出物种选择性特征,这些特征通过线性判别分析(LDA)进行提取和分类。该检测系统能够识别多达四种类型的 VOC,包括甲醇、乙醇、丙酮、甲醛和额外的水。甲醇和甲醛的混合物也可以被检测到。VOC 分类系统所需的样品量为 50 μL,或平均 1.164 mmol。每个 VOC 测量的响应时间快达 24 s。通过 LDA 和电化学阻抗谱技术,也可以定量检测溶液中如甲醛等 VOC 的体积。通过调整电解质的组成,该系统显示出可调节的检测范围,适用于 1.6%和 16%(w/v)CHO 溶液。检测限低至 1 μL。对于 1.6%CHO 溶液,线性校准范围确定为 5.30 至 53.00 μmol,检测限为 0.53 μmol。还深入讨论了 VOC 测定和定量的机制。预计这项工作将为使用机器学习分析电化学检测 VOC 的概念提供新的见解,并应用于 VOC 气体监测和流体检测。

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