Gas Sensing with Nanoporous In 2 O 3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H 2 and H 2 O.

Clean hydrogen is a key aspect of carbon neutrality, necessitating robust methods for monitoring hydrogen concentration in critical infrastructures like pipelines or power plants. While semiconducting metal oxides such as In₂O₃ can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H₂O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In₂O₃ layer creates identifiable changes in a gas sensor's electronic resistance that can be linked to H₂ and H₂O concentrations via machine learning. We exposed nanostructured In₂O₃ with a large surface area of 95 m² g⁻¹ to H₂ concentrations (0–800 ppm) and relative humidity (0–70%) under cyclic activation utilizing blue light. The sensors were tested for 20 classes of gas combinations. A support vector machine achieved classification rates up to 92.0%, with reliable reproducibility (88.2 ± 2.7%) across five individual sensors using 10-fold cross-validation. Our findings suggest that cyclic optical activation can be used as a tool to classify H₂ and H₂O concentrations. [ABSTRACT FROM AUTHOR]