通过In2O3基于传感器阵列的混合NOX的抗干扰检测,与室温的神经网络模型相结合
Yupeng Liu1, Zhuang Yang2, Long Huang3
1College of Engineering and Technology, Southwest University, Chongqing 400715, China; State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400030, China.
Journal of hazardous materials
|November 1, 2023
概括
贵金属合氧化传感器与鱼优化算法-回传播神经网络 (WOA-BPNN) 模型准确地检测混合氧化 (NOx). 这种抗干扰方法使得即使有交叉干扰,也能够精确量化NOx成分.
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
- 化学中的人工智能.
背景情况:
- 氧化 (NOx) 是重要的空气污染物,需要精确的检测方法.
- 现有的气体传感器经常受到交叉干扰的影响,这限制了它们在复杂混合物中的可靠性.
- 氧化 (In2O3) 是一种有前途的半导体材料,用于气体传感应用.
研究的目的:
- 开发一种防干扰气体传感器阵列,用于检测混合NOx.
- 研究贵金属兴奋剂 (Pt,Au,Pd) 对In2O3传感器性能的影响.
- 应用WOA-BPNN模型来准确对NOx成分进行定量分析.
主要方法:
- 通过热水方法合成Pt,Au和Pd合的In2O3纳米结构.
- 使用X射线衍射 (XRD),扫描电子显微镜 (SEM),能量分散式X射线光谱 (EDS) 和X射线光电子光谱 (XPS) 的表征.
- 制造一个传感器阵列,并在室温下进行测试;使用鱼优化算法回传神经网络 (WOA-BPNN) 进行数据分析.
主要成果:
- 成功合成和表征了多种形式的高贵金属合物In2O3.3.
- 证明了改进的灵敏度,响应恢复时间,可重复性和杂传感器的选择性.
- 使用WOA-BPNN模型,高准确度地实现混合NOx元件的定量预测,克服交叉干扰问题.
结论:
- 贵金属兴奋剂显著增强了In2O3.3.的气体感应特性.
- WOA-BPNN模型有效地处理传感器阵列数据,以可靠地检测NOx.
- 这种综合方法为混合NOx的抗干扰检测提供了强大的解决方案.
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