数据驱动的方法对复杂混合物中的气体量化使用非选择性单一金属氧化物气体传感器
K T Savio1, Amisha Mishra2, Aniket K Pandey3
1School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram 695551, India.
ACS sensors
|March 11, 2026
概括
机器学习增强了单金属氧化物半导体传感器,以准确检测挥发性有机化合物 (VOC). 这种方法提高了实时空气质量监测的选择性和度预测.
科学领域:
- 材料科学 材料科学 材料科学
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 金属氧化物半导体 (MOS) 传感器为挥发性有机化合物 (VOC) 检测提供高灵敏度.
- 然而,选择性差限制了它们在诊断和空气质量控制等现实应用中的性能.
研究的目的:
- 为单个,非选择性的MOS传感器开发一个机器学习 (ML) 框架.
- 为了实现准确的VOC分类和度预测,克服选择性限制.
主要方法:
- 使用射频喷射的氧化薄膜与黄金接触器作为MOS传感器.
- 评估了使用整体方法,ANN,LSTM和GRU的时间独立和时间依赖特征.
- 应用回归分析用于度预测.
主要成果:
- 与集成模型的时间独立特征实现了基线分类的98%准确性.
- 顺序模型的时间依赖特征通过捕捉吸附-脱吸动力学达到>94%的准确性.
- 回归技术提高了预测能力,显示出更高的R2和更低的RMSE.
结论:
- 基于ML的分析补充了材料创新,以提高MOS传感器对VOC的选择性.
- 这种方法可以在复杂的气体环境中实现可扩展的实时监控.
- 该框架可适应检测其他有毒气体,污染物和生物标志物.
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