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Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.
Bibliographical noteFunding Information:
N.S.S.C. was supported by the National Science Foundation (NSF) through the Graduate Research Fellowship Program (GRFP) program. Portions of this work were also supported by Boston Scientific and the Minnesota Lions Diabetes Foundation. Parts of this work were carried out in the Characterization Facility, University of Minnesota, which has received capital equipment funding from the NSF through the MRSEC program under award no. DMR-2011401. Portions of this work were also conducted in the Minnesota Nano Center, which is supported by the NSF through the National Nanotechnology Coordinated Infrastructure (NNCI) under award no. ECCS-2025124. The authors wish to thank three anonymous reviewers for their insightful comments and suggestions and Laura E. Simms for helpful discussions. The authors acknowledge Lun Jin for performing the Raman characterization.
© 2022 American Chemical Society.
- gas sensor
- machine learning
- surface functionalization
- volatile organic compound
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.
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- 1 Active
9/1/20 → 8/31/26
Project: Research project