快速检测CO,H2S和NO2混合物使用集成的SnO2基于传感器阵列与机器学习相结合
Weiqi Wang1, Jiamu Cao1,2, Rongji Zhang1
1School of Astronautics, Harbin Institute of Technology, Harbin 150001, China.
ACS sensors
|December 19, 2025
概括
本研究介绍了一种低成本的方法,用于快速检测混合气体,如一氧化碳 (CO),硫化 (H2S) 和二氧化 (NO2),使用传感器阵列和机器学习. 该方法在20秒内达到高精度,改善了工业气体传感.
科学领域:
- 材料科学 材料科学 材料科学
- 化学传感器 化学传感器
- 传感器技术 传感器技术
背景情况:
- 工业气体传感要求精确检测复杂的混合物,超过单个传感器的能力,因为交叉灵敏度.
- 现有的方法难以达到对多种气体实时监测所需的速度和准确性.
研究的目的:
- 使用集成的传感器阵列开发一种低成本,快速检测混合气体 (CO,H2S,NO2) 的系统.
- 通过时间和频率域分析利用机器学习来增强气体识别.
主要方法:
- 设计了一个具有可辨别响应的金属氧化物半导体传感器阵列.
- 将快速切换的加热信号应用于微热器.
- 提取的时间和频率域特征用于机器学习模型训练.
主要成果:
- 成功实现了CO,H2S和NO2混合物的分类和度预测.
- 证明高精度 (96.30%) 和确定系数 (R^2=0.97) 仅使用吸附数据的前20秒.
- 显著减少了快速检测混合气体所需的数据集大小.
结论:
- 集成的传感器阵列与机器学习提供了一个可行的解决方案,用于低成本,快速检测三元气体混合物.
- 这种方法推进了金属氧化物半导体传感器阵列在工业气体传感中的应用.
- 该方法可实现高效的气体分析,减少数据需求并提高检测速度.
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