Ouyang Qin, Rong Yanna, Xia Gaofan, Chen Quansheng, Ma Yujie, Liu Zhonghua
School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
Tea Industry Research Institute, Fujian Eight Horses Tea Co., Ltd, Quanzhou, 362442, P. R. China.
Adv Sci (Weinh). 2025 Mar;12(12):e2411621. doi: 10.1002/advs.202411621. Epub 2025 Jan 31.
Direct visualization and monitoring of volatile organic compounds (VOCs) sensing processes via portable colorimetric sensors are highly desired but challenging targets. The key challenge resides in the development of efficient sensing systems with high sensitivity, selectivity, humidity resistance, and profuse color change. Herein, a strategy is reported for the direct visualization of VOCs sensing by mimicking human olfactory function and integrating colorimetric COF-on-MOF sensors with artificial intelligence (AI)-assisted data analysis techniques. The Dye@Zeolitic Imidazolate Framework@Covalent Organic Framework (Dye@ZIF-8@COF) sensor takes advantage of the highly porous structure of MOF core and hydrophobic nature of the COF shell, enabling highly sensitive colorimetric sensing of trace number of VOCs. The Dye@ZIF-8@COF sensor exhibits exceptional sensitivity to VOCs at sub-parts per million levels and demonstrates excellent humidity resistance (under 20-90% relative humidity), showing great promise for practical applications. Importantly, AI-assisted information fusion and perceptual analysis greatly promote the accuracy of the VOCs sensing processes, enabling direct visualization and classification of seven stages of matcha drying processes with a superior accuracy of 95.74%. This work paves the way for the direct visualization of sensing processes of VOCs via the integration of advanced humidity-resistant sensing materials and AI-assisted data analyzing techniques.
通过便携式比色传感器直接可视化和监测挥发性有机化合物（VOCs）的传感过程是非常令人期待但具有挑战性的目标。关键挑战在于开发具有高灵敏度、选择性、耐湿性和丰富颜色变化的高效传感系统。在此，报道了一种通过模拟人类嗅觉功能并将比色COF-on-MOF传感器与人工智能（AI）辅助数据分析技术相结合来直接可视化VOCs传感的策略。染料@沸石咪唑酯骨架@共价有机骨架（Dye@ZIF-8@COF）传感器利用了MOF核的高度多孔结构和COF壳的疏水性质，实现了对痕量VOCs的高灵敏度比色传感。Dye@ZIF-8@COF传感器在百万分率水平下对VOCs表现出卓越的灵敏度，并展示出优异的耐湿性（相对湿度为20-90%），在实际应用中显示出巨大潜力。重要的是，AI辅助的信息融合和感知分析极大地提高了VOCs传感过程的准确性，能够以95.74%的卓越准确率直接可视化和分类抹茶干燥过程的七个阶段。这项工作通过整合先进的耐湿传感材料和AI辅助数据分析技术，为直接可视化VOCs的传感过程铺平了道路。
