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

Area of Science:

• Materials Science
• Artificial Intelligence
• Sensor Technology

Background:

• Traditional gas sensors lack memory function, hindering long-term risk assessment for hazardous chemicals like formaldehyde.
• High operating temperatures of conventional sensors limit integration with synaptic devices for artificial olfactory systems.

Purpose of the Study:

• To develop a novel artificial olfactory synaptic device for obtaining and memorizing formaldehyde information.
• To create a system capable of simulating formaldehyde exposure effects and providing early warnings.
• To integrate gas sensing with synaptic transistor functionality for enhanced artificial olfaction.

Main Methods:

• An ion gel synaptic transistor was integrated with a silver-zinc oxide (Ag-ZnO) gas sensor.
• The Ag-ZnO sensor detected formaldehyde at room temperature using ultraviolet irradiation, converting concentrations into chemiresistive signals.
• Formaldehyde-induced resistance changes modulated the transistor's gate voltage, influencing postsynaptic currents and enabling information storage.

Main Results:

• The device successfully obtained and memorized formaldehyde information at room temperature.
• The system simulated adverse effects of formaldehyde exposure and provided early warnings.
• Support vector machine (SVM) analysis of postsynaptic currents enabled accurate identification of different formaldehyde concentrations.

Conclusions:

• The proposed bionic olfactory system integrates gas sensing and synaptic memory functions.
• This novel device offers a promising strategy for developing advanced artificial intelligence and danger warning systems.
• The room-temperature operation and memory capability address limitations of traditional gas sensors.