دورية أكاديمية
Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose.
| العنوان: | Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose. |
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| المؤلفون: | Capman NSS; Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States.; Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, Minnesota 55455, United States., Zhen XV; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Nelson JT; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Chaganti VRSK; Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States., Finc RC; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Lyden MJ; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Williams TL; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Freking M; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Sherwood GJ; Boston Scientific, 4100 Hamline Avenue North, St. Paul, Minnesota 55112, United States., Bühlmann P; Department of Chemistry, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, United States., Hogan CJ; Department of Mechanical Engineering, University of Minnesota, 111 Church Street SE, Minneapolis, Minnesota 55455, United States., Koester SJ; Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States. |
| المصدر: | ACS nano [ACS Nano] 2022 Nov 22; Vol. 16 (11), pp. 19567-19583. Date of Electronic Publication: 2022 Nov 11. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S. |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101313589 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1936-086X (Electronic) Linking ISSN: 19360851 NLM ISO Abbreviation: ACS Nano Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Washington D.C. : American Chemical Society |
| مواضيع طبية MeSH: | Volatile Organic Compounds*/analysis , Graphite*, Humans ; Electronic Nose ; Octanes ; Gases ; Machine Learning |
| مستخلص: | 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. |
| فهرسة مساهمة: | Keywords: gas sensor; graphene; machine learning; surface functionalization; volatile organic compound |
| المشرفين على المادة: | 0 (Volatile Organic Compounds) 7782-42-5 (Graphite) X1RV0B2FJV (octane) 0 (Octanes) 0 (Gases) |
| تواريخ الأحداث: | Date Created: 20221111 Date Completed: 20221129 Latest Revision: 20230125 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1021/acsnano.2c10240 |
| PMID: | 36367841 |
| قاعدة البيانات: | MEDLINE |
Find this issue from the American Chemical Society | تدمد: | 1936-086X |
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| DOI: | 10.1021/acsnano.2c10240 |