Odor discrimination and identification by graphene-based sensor technologeis

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APS March Meeting | event contribution
Link to conference: https://march.aps.org/
March 4, 2023 | Las Vegas, USA

Olfaction is an evolutionary old sensory system,yet it provides sophisticated access to information about our surroundings. Inspired by the biological example, electronicnoses (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.


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Odor discrimination and identification by graphene-based sensor technologeis

©https://march.aps.org/
©https://cdn.sanity.io/images/w9qr87ve/production/4f4683d6b0c00cb49a0cb17d561c4177d2f740f8-1285x409.svg

APS March Meeting | event contribution
Link to conference: https://march.aps.org/
March 4, 2023 | Las Vegas, USA

Olfaction is an evolutionary old sensory system,yet it provides sophisticated access to information about our surroundings. Inspired by the biological example, electronicnoses (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.


Presenter

Authors

Related publications