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

作者：Junshuai Dai, Yuan Li, Yiwen Wei, Jixing Zhou, Longwei Xue, Xudong Zhang, Jianhua Zhang, Longlong Chen, Xingwei Ding, Hai Liu, Tingting Zhao · 发表于：ACS Sensors · 年份：2025 · DOI：10.1021/acssensors.5c00400 · 被引用次数：7 · 研究领域：Advanced Chemical Sensor Technologies、Advanced Memory and Neural Computing、Photoreceptor and optogenetics research

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 ...