AI‐Integrated Optoelectronic Platform for Low‐Latency Classification of Toxic Industrial Chemicals

Abstract This study introduces an optoelectronic platform designed for high‐accuracy detection and classification of Toxic Industrial Chemicals (TICs), addressing key limitations of conventional Leak Detection and Repair (LDAR) systems. The system integrates colorimetric sensor membranes (CSMs) with a 3 × 3 IGZO phototransistor array, enabling the conversion of gas‐induced color variations into electrical signals for reliable TIC identification. Applying a multi‐power sensing approach with three distinct laser intensities (0.3, 0.75, and 1.9 mW), 100% classification accuracy is achieved through K‐means clustering, demonstrating the robustness of the sensing mechanism. In addition, a gas detection framework based on GRU modeling and vector quantization maintained 100% accuracy with reduced input conditions while reducing model size by 66.14%, supporting efficient, low‐latency processing. The proposed system offers scalability, compactness, and compatibility with resource‐constrained environments, representing a promising pathway for next‐generation fugitive emissions management and LDAR implementation.