Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose
Abstract
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.
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Multiplexer data acquisition from 108 sensors was achieved in tens of seconds, allowing rapid measurement of VOCs.
“Multiplexer data acquisition from 108 sensors was achieved in tens of seconds, allowing for rapid measurement of the VOCs.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
Functionalization of graphene with different chemical acceptors enabled the development of arrays containing 108 sensors with 36 chemical receptors for cross-selectivity.
“The functionalization of graphene with different chemical acceptors enabled the development of arrays containing 108 sensors with 36 chemical receptors for cross-selectivity.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
Graphene-based varactor arrays have potential for selective and rapid detection of multiple VOCs at different concentrations.
“These findings suggest the potential of graphene-based varactor arrays for the selective and rapid detection of multiple VOCs at different concentrations.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
Capman et al. demonstrated selective and rapid detection of multiple VOCs using graphene-based variable capacitor arrays.
“Capman et al. (Capman et al. 2022) demonstrated the selective and rapid detection of multiple VOCs using graphene-based variable capacitor arrays (Fig. 7c).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
Despite reduced signal amplitude associated with rapid measurement, the method demonstrated 98% accuracy in detection of the five tested analytes ethanol, hexanal, methyl ethyl ketone, toluene, and octane.
“Despite the reduced signal amplitude associated with the rapid measurement, the method demonstrated a significant 98% accuracy in the detection of the five tested analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
Addition of 1-octene, an analyte with a structure highly like octane, resulted in a slightly reduced accuracy of 89%.
“The addition of 1-octene, an analyte with a structure highly like octane, resulted in a slightly reduced accuracy of 89%.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Capman et al. 2022
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- Authors
- Capman, N. S. S., Zhen, X. V., Nelson, J. T., Chaganti, V. R. S. K., Finc, R. C., Lyden, M. J., Williams, T. L., Freking, M., Sherwood, G. J., Bühlmann, P., Hogan, C. J., & Koester, S. J.
- Affiliations
- University of Minnesota (US)
- Boston Scientific (United States) (US)
- Corresponding author
- Koester, Steven J.
- ORCID
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-11-11
- Volume
- 16
- Issue
- 11
- Pages
- 19567-19583
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W4308770207
- Cited by
- 115
- References
- 58
- Citations by year
- 2026: 30 · 2025: 37 · 2024: 30 · 2023: 16 · 2022: 2
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Nano (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Boston Scientific Corporation
- Division of Materials Research (DMR-2011401)
- Minnesota Lions Diabetes Foundation
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Gas Sensing Nanomaterials and Sensors
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrochemical sensors and biosensors
- Keywords
- volatile organic compounds, breath analysis, machine learning classification, sensor selectivity, environmental monitoring
- MeSH terms
- Machine Learning, Gases, Graphite, Humans, Octanes, Volatile Organic Compounds, Electronic Nose
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