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Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor

Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas spec...

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Detalles Bibliográficos
Autores principales: Cho, Incheol, Lee, Kichul, Sim, Young Chul, Jeong, Jae-Seok, Cho, Minkyu, Jung, Heechan, Kang, Mingu, Cho, Yong-Hoon, Ha, Seung Chul, Yoon, Kuk-Jin, Park, Inkyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113244/
https://www.ncbi.nlm.nih.gov/pubmed/37072383
http://dx.doi.org/10.1038/s41377-023-01120-7
Descripción
Sumario:Electronic nose (e-nose) technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications, such as smart factory and personal health monitoring. To overcome the cross-reactivity problem of chemoresistive sensors to various gas species, herein, we propose a novel sensing strategy based on a single micro-LED (μLED)-embedded photoactivated (μLP) gas sensor, utilizing the time-variant illumination for identifying the species and concentrations of various target gases. A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses. A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation. The proposed sensor system achieves high classification (~96.99%) and quantification (mean absolute percentage error ~ 31.99%) accuracies for various toxic gases (methanol, ethanol, acetone, and nitrogen dioxide) with a single gas sensor consuming 0.53 mW. The proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.