通过共同设计的WO3-ZnO传感器阵列和卷积神经网络,用于农业氨气检测的增强选择性电子鼻子系统
Mengying Du1, Mukhtar Iderawumi Abdulraheem2,3, Lulu Xu1
1Henan International Joint Laboratory of Laser Technology in Agriculture Sciences, College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Scientific reports
|November 7, 2025
概括
这项研究通过使用卷积神经网络 (CNN) 来克服传感器交叉敏感性,增强了用于农业中准确监测氨 (NH3) 的电子鼻子. 开发的系统在复杂的气体混合物中实现了高精度,确保可靠的实时测量.
科学领域:
- 材料科学与工程 材料科学与工程
- 环境科学 环境科学
- 化学传感技术 化学传感技术
背景情况:
- 电子鼻子 (e-noses) 在农业中对实时氨 (NH3) 监测至关重要.
- 电子鼻子中的半导体气体传感器面临交叉敏感性问题,在CO2,CH4和H2S等干扰气体的环境中降低精度.
- 准确的NH3检测对于环境管理和农业可持续性至关重要.
研究的目的:
- 调查和减轻电子鼻子系统中氨检测的交叉敏感性.
- 使用卷积神经网络 (CNN) 开发传感器数据融合策略,以改进NH3量化.
- 在各种环境条件下评估增强型电子鼻子系统的性能和稳定性.
主要方法:
- 使用基于WO3和基于SnO2的半导体气体传感器来检测NH3.
- 使用密度函数理论 (DFT) 来分析气体结合能量和传感器选择性.
- 在不同湿度和温度下应用加权最小平方误差传播模型来量化不确定性.
- 开发并验证了用于传感器数据融合和混合气体分析的CNN模型.
主要成果:
- WO3传感器显示出高的NH3选择性 (7.3:1对抗CH4,17.8:1对抗H2S),得到了DFT结合能量分析的支持.
- 采用CNN增强的近二维传感器阵列,将NH3分类准确度提高到96.4%,并将度误差降低了50.8%.
- SnO2传感器显示出优异的长期稳定性,基线漂移率低 (0.18%/天超过180天).
- 通过学习非线性响应模式,CNN模型在混合气体环境中实现了91.7%的准确性.
结论:
- 基于CNN的传感器数据融合有效地解决了NH3监控的e-nose系统中的交叉敏感性挑战.
- 开发的系统在复杂的农业环境中提供精确可靠的实时NH3量化.
- 这种方法显著提高了环境和农业监测e-noses的实用性.
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