Munirathinam Pavithra, Arshi Mohd Farhan, Nazemi Haleh, Antony Raj Gian Carlo, Emadi Arezoo
E-Minds Laboratory, Electrical and Computer Engineering Department, University of Windsor, Windsor, ON N9B 3P4, Canada.
Sensors (Basel). 2025 Jul 2;25(13):4130. doi: 10.3390/s25134130.
Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications.
检测挥发性有机化合物（VOCs）对于健康、环境保护和工业安全至关重要。VOCs会造成空气污染，带来健康风险，并可指示工业中的泄漏或污染情况。其应用包括空气质量监测、疾病诊断和食品安全。本文重点关注利用叉指电极（IDE）几何结构进行VOC检测的聚合物基混合传感器阵列（HSAs）。在气体传感中实现高选择性和高灵敏度仍然是一项挑战，尤其是在复杂环境中。为解决这一问题，我们提出将HSAs作为一种创新解决方案来提高传感器性能。基于IDE的传感器采用多晶硅多用户微机电系统工艺（PolyMUMPs）进行设计和制造。通过在受控条件下将传感器暴露于VOCs中来进行实验评估。传统的多传感器阵列（MSAs）实现了82%的预测准确率，而利用频率依赖性的虚拟传感器阵列（VSAs）提高了性能：聚甲基丙烯酸甲酯-VSA（PMMA-VSA）和聚乙烯吡咯烷酮-VSA（PVP-VSA）分别以100%和98%的准确率预测化合物。所提出的整合了这些VSAs的HSA在化合物识别和浓度估计方面始终实现100%的准确率，超越了MSA和VSA的性能。这些发现表明，所提出的基于聚合物的HSAs和VSAs，特别是具有先进IDE几何结构的，显著提高了选择性和灵敏度，推动了电子鼻技术在各种应用中实现更准确可靠的VOC检测。
