Affiliation:
1. School of Engineering Science Simon Fraser University Burnaby British Columbia V5A 1S6 Canada
2. Department of Computer Science and Software Engineering Concordia University Montreal Quebec H3G 1M8 Canada
3. Department of Physics Simon Fraser University Burnaby British Columbia V5A 1S6 Canada
Abstract
AbstractVolatile organic compounds (VOCs) sensors have a broad range of applications including healthcare, process control, and air quality analysis. There are a variety of techniques for detecting VOCs such as optical, acoustic, electrochemical, and chemiresistive sensors. However, existing commercial VOC detectors have drawbacks such as high cost, large size, or lack of selectivity. Herein, a new sensing mechanism is demonstrated based on surface interactions between VOC and UV‐excited 2D germanium sulfide (GeS), which provides an effective solution to distinguish VOCs. The GeS sensor shows a unique time‐resolved electrical response to different VOC species, facilitating identification and qualitative measurement of VOCs. Moreover, machine learning is utilized to distinguish VOC species from their dynamic response via visualization with high accuracy. The proposed approach demonstrates the potential of 2D GeS as a promising candidate for selective miniature VOCs sensors in critical applications such as non‐invasive diagnosis of diseases and health monitoring.
Funder
Natural Sciences and Engineering Research Council of Canada
Western Economic Diversification Canada
Canada Foundation for Innovation
Subject
General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)
Cited by
11 articles.
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