Ultrafast Hydrogen Detection System Using Vertical Thermal Conduction Structure and Neural Network Prediction Algorithm Based on Sensor Response Process.

Journal: ACS sensors
PMID: 40068112

Abstract

Hydrogen detection plays a crucial role in various scenes of hydrogen energy such as hydrogen vehicles, hydrogen transportation and hydrogen storage. It is essential to develop a hydrogen detection system with ultrafast response times (<1 s) for the timely detection of hydrogen leaks. Here we report an ultrafast (0.4 s) hydrogen detection system based on a wafer-scale fabrication process. It consists of a low power (20.2 mW) hydrogen sensor based on vertical thermal conduction structure and a signal processing circuit introduced with a neural network prediction algorithm based on sensor response process. The fabricated sensor exhibits rapid response, wide detection range, and wide operating temperature, while showing good long-term stability and excellent selectivity. Meanwhile, the model significantly enhanced the detection speed by enabling hydrogen concentration prediction using only the initial 40 data points (sampling frequency of 100 Hz) from the sensor response before the sensor completes the entire response process. This work introduces a novel approach to achieve an ultrafast hydrogen detection system, which demonstrates significant application promise in the fields of low-power sensors and rapid gas detection.

Authors

  • Ruilin Yang
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
  • Zhen Yuan
    Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China.
  • Changrong Jiang
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
  • Xinjie Zhang
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
  • Zilong Qiao
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
  • Jianping Zhang
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zhangjianping@fudan.edu.cn.
  • Junge Liang
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
  • Si Wang
    State Key Laboratory of Optical Technologies on Nano-Fabrication and Micro-Engineering Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
  • Zaihua Duan
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, P. R. China.
  • Yuanming Wu
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, P. R. China.
  • Weizhi Li
    Spectral MD, Inc., 2515 McKinney Avenue, Suite 1000, Dallas, Texas 75201, United States.
  • Yadong Jiang
    Business School of Hohai University, Changzhou, 213002, China.
  • Huiling Tai
    State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, P. R. China.

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