Work

Pulse-driven MEMS gas sensor combined with machine learning for selective gas identification

Year: 2025

Type: article

Abstract: The sensing and identification of trace gases are essential for ensuring chemical safety and protecting human health. This study introduces a low-power electronic nose system that utilizes a single sensor driven by repeated pulsed power inputs, offering a viable alternative to conventional sensor array-based methods. The sensor's compact design and suspended architecture facilitate a rapid thermal response, effectively decoupling the influences of temperature, physisorption, and charge exchange on the conductivity of the sensing material. This mechanism generates distinct gas sensing responses, characterized by alternating dual responses within a single time period. The unique dynamics of the dual signals, which vary with gas type and concentration, enable precise identification of multiple gas species using machine learning (ML) algorithms. Microfabricated through wafer-level batch processing, our innovative electronic nose system holds significant potential for battery-powered mobile devices and IoT-based monitoring applications.

Source: Microsystems & Nanoengineering

DOI: 10.1038/s41378-025-00934-2

Authors: Wenxin Luo, Fa Dai, Yijun Liu, Xin Wang, Mingjie Li

Institution: Guangdong University of Technology

Language: English

Topic: Gas Sensing Nanomaterials and Sensors

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,790

Funders: Guangdong University of Technology, Guangzhou Municipal Science and Technology Bureau, Guangzhou Municipal Science and Technology Project