Integrating Humidity‐Resistant and Colorimetric COF‐on‐MOF Sensors with Artificial Intelligence Assisted Data Analysis for Visualization of Volatile Organic Compounds Sensing

Abstract 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.