Machine Learning-enabled Biomimetic Electronic Olfaction Using Graphene Single-channel Sensors
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Beitragende
Abstract
Olfaction is an evolutionary old sensory system, yet it provides sophisticated access to information about our surroundings. Inspired by the biological example, electronic noses (e-noses) in combination with efficient machine learning techniques aim to achieve similar performance and thus digitize the sense of smell. Despite the significant progress of e-noses, their development remains challenging due to the complex layout design of sensor arrays with a multitude of receptor types or sensor materials, and the need for high working temperature. In the current work, we present the discriminative recognition of odors utilizing graphene single-channel nanosensor-based electronic olfaction in conjunction with machine learning techniques. Multiple transient features extracted from the sensing response profile are employed to represent each odor and used as a fingerprint of odors. The developed electronic olfaction prototype exhibits excellent odor identification performance at room temperature, maximizing the obtained results from a single nanosensor. The developed platform may facilitate miniaturization of e-nose systems, digitization of odors, and distinction of volatile organic compounds (VOCs) in various emerging applications.
Details
Originalsprache | Englisch |
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Titel | 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) |
Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
ISBN (elektronisch) | 978-1-6654-5860-3 |
ISBN (Print) | 978-1-6654-5861-0 |
Publikationsstatus | Veröffentlicht - 2022 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) |
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Konferenz
Titel | 2022 IEEE International Symposium on Olfaction and Electronic Nose, ISOEN 2022 |
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Dauer | 29 Mai - 1 Juni 2022 |
Stadt | Aveiro |
Land | Portugal |
Externe IDs
ORCID | /0000-0002-4349-793X/work/142245516 |
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ORCID | /0000-0002-3007-8840/work/142247145 |
ORCID | /0000-0002-9899-1409/work/142249211 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- e-nose, graphene, odor identification, Olfaction