使用机器学习来克服在石墨烯挥发性有机化合物传感器中的干扰氧气效应
Nyssa S S Capman1,2, V R Saran Kumar Chaganti1, Laura E Simms3,4
1Department of Electrical and Computer Engineering, University of Minnesota, 200 Union Street SE, Minneapolis, Minnesota 55455, United States.
ACS applied materials & interfaces
|January 31, 2024
概括
石墨烯传感器可以准确地识别挥发性有机化合物 (VOC),即使有氧干扰. 机器学习,特别是长期短期记忆网络,可以精确检测VOC和度测量.
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
- 机器学习 机器学习
背景情况:
- 石墨烯传感器为检测挥发性有机化合物 (VOC) 提供高灵敏度.
- 氧干扰使VOC检测复杂化,需要了解结合气体效应.
- 石墨烯对氧气的固有敏感性对选择性VOC传感构成了挑战.
研究的目的:
- 为了研究石墨烯 varactor 传感器对氧气和各种VOC的交叉选择性.
- 开发一种机器学习方法来对气体混合物和VOC度进行分类.
- 评估在存在氧气和传感器漂移的情况下精确的VOC传感的可行性.
主要方法:
- 使用的石墨烯变电容 (varactor) 传感器.
- 将传感器暴露在受控的氧 (3度) 和挥发性有机化合物 (乙醇,甲醇,甲基乙基;每个度为5) 的混合物中.
- 采用长期短期记忆 (LSTM) 网络来对传感器响应数据进行分类.
主要成果:
- 传感器响应显示出基于混合物中相对气体度的不同形状.
- 该LSTM模型在分类VOC类型时实现了100%的准确性,无论氧含量如何.
- 证明VOC度分辨率大约在200ppm以内.
- 尽管典型的石墨烯传感器漂移模式,但成功归类了气体混合物.
结论:
- 石墨烯 varactor 传感器与 LSTM 网络相结合,可以在氧气存在时有效地区分VOC.
- 开发的机器学习方法克服了氧气干扰和传感器漂移带来的挑战.
- 这种方法提高了使用基于石墨烯的传感器可靠诊断疾病和环境监测的潜力.
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