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

Authors

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

Published in

Microsystems & nanoengineering. Volume 11. Issue 1. Pages 72. Apr 23, 2025. Epub Apr 23, 2025.

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.

PMID:
40268887
Bibliographic data and abstract were imported from PubMed on 24 Apr 2025.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.