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 over...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 crossreactivity 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.展开更多
基金supported by the Multi-Ministry Collaborative R&D Program(Development of Techniques for Identification and Analysis of Gas Molecules to Protect against Toxic Substances)through the National Research Foundation of Korea(NRF)funded by KNPA,MSIT,MOTIE,ME,and NFA(Grant No.NRF-2022M3D9A1023618)the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(NRF-2021R1A2C3008742)+1 种基金supported by the National Research Foundation(NRF)grant funded by the Korean government(MIST)(Grant No.NRF-2020M3E4A1080112)supported by Disco Corporation(Japan).
文摘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 crossreactivity 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.
