通过多变量特征提取和机器学习算法增强氧化物气体传感器的选择性
Wei Ding1, Jun Wang2, Hongxia Zhao3
1School of Chemical Engineering, Shandong Province Higher Education Intelligent Manufacturing Engineering Characteristic Laboratory, Shandong Huayu University of Technology, Dezhou 253000, PR China; College of Materials Science and Engineering, Collaborative Innovation Center for Marine Biomass Fibers, Materials and Textiles of Shandong Province, Qingdao University, Qingdao 266071, PR China.
Journal of colloid and interface science
|August 26, 2025
概括
这项研究引入了一种新的改性氧化 (Pt/WO3) 气体传感器,用于增强挥发性有机化合物 (VOC) 的检测. 该传感器在识别多种VOC方面具有很高的选择性和准确性,即使在潮湿的工业环境中也是如此.
科学领域:
- 材料科学
- 化学传感器
- 人工智能
背景情况:
- 金属氧化物半导体 (MOS) 气体传感器对挥发性有机化合物 (VOC) 具有选择性.
- 在工业环境中准确检测特定的VOC仍然是一个挑战.
- 湿度等环境因素会降低传感器的性能.
研究的目的:
- 开发一种高度选择性和稳定的气体传感器来检测VOC.
- 提高复杂混合物中的气体识别和度预测的准确性.
- 解决传统MOS传感器在工业应用中的局限性.
主要方法:
- 通过现场减少制造改性氧化 (Pt/WO3) 复合材料.
- 对三胺,氨和异醇的气体传感性能评估.
- 使用离散波形变换 (DWT) 进行信号处理以减少噪声.
- 开发用于VOC分类的人工神经网络 (ANN).
- 用于气体度预测的线性回归
主要成果:
- 与原始WO3相比,Pt/WO3传感器对三乙烯胺具有更高的灵敏度和稳定性.
- 通过DWT和ANN方法,在识别多种VOC时获得了95.2%的准确性.
- 证明了强大的耐湿性和长期运行稳定性.
- 使用线性回归模型实现了未知气体度的准确预测.
结论:
- Pt/WO3复合材料为高性能气体传感提供了有前途的解决方案.
- 智能信号处理和机器学习提高了传感器的选择性和准确性.
- 这种方法提供了一种可靠的策略,用于识别混合VOC环境中的气体度.
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