An Artificial Olfactory System Based on Synaptic Transistors for Precepting Hazardous Gas to Simulate Organ Injury.

Journal: ACS sensors
Published Date:

Abstract

Recent advances in artificial olfactory systems have attracted significant attention for their potential applications in humanoid robots and intelligent nasal devices capable of identifying objects and sensing hazards; however, the memory function is absent in traditional gas sensors, which is crucial to assess the long-term exposure risks. Meanwhile, due to the high operation temperature requirement, the gas sensors are usually difficult to integrate with the synaptic devices to form artificial olfactory systems. Here, we propose a novel artificial olfactory synaptic device to obtain and memorize formaldehyde information. The device is composed of an ion gel synaptic transistor integrated with a Ag-ZnO gas sensor, which can simulate the adverse effects of formaldehyde exposure to the human body and make an early warning. The Ag-ZnO gas sensor can detect and recognize different concentrations of formaldehyde as the chemiresistive signal at room temperature with ultraviolet irradiation instead of at high temperatures. The formaldehyde-induced resistive changes are transmitted to the gate voltage of the synaptic transistor, modulating the channel conductance to generate varying postsynaptic currents and to store gas information to realize the memory function. In addition, the postsynaptic current data of different concentrations can be imported into a support vector machine (SVM) for accurate identification, and early warning of different concentrations can be realized through the system. This bionic olfactory system provides a promising strategy for the development of advanced artificial intelligence and danger warnings.

Authors

  • Junshuai Dai
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Li Yuan
    Research Institute of Natural Gas Technology, Petro China Southwest Oil and Gas Field Company, Chengdu, 610213, China.
  • Yunkuan Wei
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Jixing Zhou
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Longwei Xue
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Xudong Zhang
    The Second Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Jianhua Zhang
  • Longlong Chen
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Xingwei Ding
    Key Laboratory of Advanced Display and System Applications of Ministry of Education, Shanghai University, Shanghai 200072, P. R. China.
  • Hai Liu
    Department of Ophthalmology, The Affiliated Hospital of Yunnan University, Second People's Hospital of Yunnan Province, The Eye Hospital of Yunnan Province, The Eye Disease Clinical Medical Research Center of Yunnan Province, The Eye Disease Clinical Medical Center of Yunnan Province, Kunming, China.
  • Tingting Zhao
    School of Software Engineering, Beihang University, Beijing, China.