Evidence map›Paper›PMID 40047570›Full record

ArticleMicromachines2025

Near-Infrared Spectral MEMS Gas Sensor for Multi-Component Food Gas Detection.

Xiaojian Yan, Yao Tan, Yi Wang, Gongdai Chen, Weigao Xia, Gang Zhou, Hongliang Luo, Hao Liu, Tianxun Gong, Xiaosheng Zhang

Abstract read
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Xiaojian YanSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yao TanSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0002-4578-4668
Yi WangPanovasic Technology Co., Ltd., Chengdu 610041, China.
Gongdai ChenPanovasic Technology Co., Ltd., Chengdu 610041, China.
Weigao XiaPanovasic Technology Co., Ltd., Chengdu 610041, China.
Gang ZhouPanovasic Technology Co., Ltd., Chengdu 610041, China.
Hongliang LuoPanovasic Technology Co., Ltd., Chengdu 610041, China.
Hao LiuPanovasic Technology Co., Ltd., Chengdu 610041, China.
Tianxun GongSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Xiaosheng ZhangSchool of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.ORCID 0000-0001-9719-0573

Funding

Key R&D Program of Mianyang No. 2023ZYDF019Key R&D Program of Sichuan Province No. 2020ZHCG0038Key R&D Program of Sichuan Province No. 2022JDTD0020Key R&D Program of Sichuan Province No. 2023YFQ0110National Key Research and Development Program of China No. 2022YFB3206100National Natural Science Foundation of China No. 62074029Natural Science Foundation of Sichuan No. 2022NSFSC1941
6 · The paper itself

Abstract

The complex application environments of gas detection, such as in industrial process monitoring and control, atmospheric and environmental monitoring, and food safety, require real-time and online high-sensitivity gas detection, as well as the accurate identification and quantitative analysis of gas samples. Despite the progress in gas analysis and detection methods, high-precision and high-sensitivity detection requirements for target gases of multiple components in mixed gases are still challenging. Here, we demonstrate a micro-electromechanical system (MEMS) with near-infrared (NIR) spectral gas detection technology and spectral model training, which is used to improve the detection and classification of multi-component gases in food. During blind sample testing, the NIR spectral gas sensor demonstrated over 90% accuracy in identifying mixed gases, as well as achieving the classification of ethanol concentration. We envision that our design strategy of an NIR spectral gas sensor could enhance the gas detection and distinguishing ability under the conditions of background gas interference and cross-interference in multi-component detection.

Indexed as

gas sensorMEMSmicro-electromechanical systemsnear-infrared spectroscopy

Identifiers

PMID40047570
PMCPMC11857828

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.