August 16, 2025

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

Key Points

High-accuracy classification of toxic industrial chemicals was achieved with a novel sensing platform, enhancing safety.
Using a multi-power approach, the system maintained 100% accuracy while optimizing input conditions and model size.
An integration of colorimetric sensors and phototransistors allowed gas-induced color changes to be effectively converted into electrical signals.
With compact design and scalability, this solution signifies progress in managing fugitive emissions in resource-constrained settings.

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.

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