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机器学习集成的金属有机框架/多壁碳纳米管传感器阵列用于真实时间检测真菌VOCs

Yelim Choi¹, Seonghwan Kim², Daekeun Kim¹

¹Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Journal of hazardous materials

|February 20, 2026

概括

这项研究开发了一个新的气体传感器平台,使用金属有机框架复合材料和机器学习来检测通过微生物挥发性有机化合物 (MVOC) 检测真菌污染. 该系统对早期的,非侵入性的室内空气质量监测非常有希望.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
分析化学分析化学

背景情况:

室内真菌污染对健康构成风险,由于隐藏的生长,经常被发现迟到.
传统的真菌检测方法是有限的.
微生物挥发性有机化合物 (MVOCs) 为早期检测提供了一个非侵入性的替代方案.

研究的目的:

开发和评估一个多式气体传感器平台,用于实时MVOC检测.
将金属有机框架 (MOF) /多壁碳纳米管 (MWCNT) 复合材料与机器学习算法集成.
通过MVOC分析评估平台在识别真菌污染方面的有效性.

主要方法:

三种MOF/MWCNT复合材料 (ZIF-8/MWCNT,Cu-BTC/MWCNT,UIO-66/MWCNT) 的制造.
在石英晶体微平衡 (QCM),电阻传感器 (RS) 和电化学阻抗光谱 (EIS) 传感器类型上涂层复合材料.
使用机器学习模型 (Random Forest,CatBoost) 评估单个化合物,气体混合物和真菌头部空间排放的传感器阵列.

主要成果:

高精度 (95%) 在单个气体检测使用随机森林.
使用CatBoost检测气体混合物的性能良好 (R2值高达0.93).
使用CatBoost对真菌排放与对照样本 (0.963精度) 的准确分类.

结论:

开发的传感平台展示了使用气相信号间接,数据驱动的真菌检测的可行性.
这种概念验证系统显示了可扩展和自适应的室内空气质量监测的潜力.
未来的工作包括扩大合成规模和开发便携式设备,用于早期警告微生物污染.

关键词:

电化学阻抗光谱学电化学阻抗光谱学金属有机框架的框架.模式识别模式识别模式识别石英晶体微平衡器电阻传感器的电阻感应器挥发性有机化合物挥发性有机化合物

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