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

ABSTRACT: 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 sens

SUBMITTER: Cho I 

PROVIDER: S-EPMC10113244 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature