Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor
Incheol Cho, Kichul Lee, Young Chul Sim, Jaeseok Jeong +7
Reported parameters
No values extracted from this paper yet.
No 3D model is mapped to this paper yet. Parameter ranges above still place reported values on the literature distribution.
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 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.
Keywords
Identifiers
- PubMed
- 37072383
- Journal
- Light Science & Applications
- Year
- 2023