Real-Time Gas Identification at Room Temperature Using UV-Modulated Sb-Doped SnO₂ Sensors via Machine Learning

This study presents a novel approach for real-time gas identification at room temperature. We use UV-modulated Sb-doped SnO₂ sensors combined with machine learning. Our method exclusively employs the gas response (R) as the sole metric. This eliminates the need for time-dependent parameters such as response and recovery times. By modulating the UV light intensity at five distinct levels (5, 10, 15, 20, and 30 mW/cm²), we generate a five-dimensional optical fingerprint. This fingerprint captures subtle variations in sensor response under different illumination conditions. Gas discrimination was evaluated for both oxidizing gases (O₃ and NO₂) and reducing gases (NH₃ and H₂). Our machine learning results show that Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) achieve nearly 100% accuracy when four UV intensity levels are used. Using R as the sole input metric allows for instantaneous response detection, which is essential for real-time gas identification. This approach addresses the limitations of conventional thermally activated sensors that require multiple parameters and paves the way for the development of rapid-response monitoring systems.