Porous Material-Based Electronic Noses for the Sensing of Volatile Organic Compounds

材料科学 多孔性 多孔介质 电子材料 纳米技术 复合材料
作者
Shu He,Junjie Wen,Bing-Zhen Cao,Guoyue Shi,Min Zhang
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:17 (44): 60055-60103
标识
DOI:10.1021/acsami.5c17506
摘要

Volatile organic compounds (VOCs) serve as key markers in environmental monitoring, healthcare, and food safety, yet their detection remains challenging due to low concentrations and complex matrices. Porous materials, with their tunable pore structures, high specific surface areas, and modifiable surface chemistries, have emerged as ideal sensing materials for electronic nose (e-nose) systems. This comprehensive review systematically explores the classification and synthetic strategies of porous materials─including microporous zeolites, mesoporous silicas, metal-organic frameworks (MOFs), carbon-based materials, and porous polymers─and their integration into e-nose platforms. We provide a detailed account of the working principles, sensor array designs, and pattern-recognition algorithms that underpin e-nose functionality. The review highlights the application of porous materials in enhancing sensor sensitivity, selectivity, and stability through molecular sieving, surface functionalization, and hierarchical pore-structure engineering. Advanced performance-optimization strategies such as pore-structure engineering, surface-chemistry modification, and device-level innovations are discussed. Moreover, we showcase practical applications in medical diagnostics, environmental monitoring, and food-quality control, demonstrating the transition from laboratory research to real-world applications. Despite progress, challenges remain in long-term stability, cost-effective scalability, standardization, and anti-interference capabilities. Future directions point toward miniaturization, AI-driven data analysis, multimodal sensor fusion, and bioinspired design. This review underscores the transformative potential of porous-material-based e-noses in achieving precise, reliable, and scalable VOC detection for next-generation intelligent sensing systems.
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