Machine Learning-enabled Biomimetic Electronic Olfaction Using Graphene Single-channel Sensors
International Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Proceedings (2022).
S. Huang, A. Croy, A. Bierling, L. A. Panes-Ruiz, B. Ibarlucea, and G. Cuniberti.
https://doi.org/10.1109/isoen54820.2022.9789605

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.

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Machine Learning-enabled Biomimetic Electronic Olfaction Using Graphene Single-channel Sensors
International Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Proceedings (2022).
S. Huang, A. Croy, A. Bierling, L. A. Panes-Ruiz, B. Ibarlucea, and G. Cuniberti.
https://doi.org/10.1109/isoen54820.2022.9789605

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.

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Involved Scientists