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

检出限 电解质 甲醛 离子液体 甲醇 化学 丙酮 介电谱 色谱法 挥发性有机化合物 电化学 分析化学(期刊) 电极 有机化学 物理化学 催化作用
作者
Xiaozhou Huang,Yaonian Li,Erin Witherspoon,Rui He,Greg Petruncio,Mikell Paige,Matthew Li,Tongchao Liu,Khalil Amine,Zhe Wang,Qiliang Li,Pei Dong
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:8 (9): 3389-3399 被引量:2
标识
DOI:10.1021/acssensors.3c00578
摘要

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) CH2O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH2O 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.
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