Gas Sensing with Nanoporous In<sub>2</sub>O<sub>3</sub> under Cyclic Optical Activation: Machine Learning-Aided Classification of H<sub>2</sub> and H<sub>2</sub>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 In<sub>2</sub>O<sub>3</sub> can monitor gas concentrat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
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| Series: | Chemosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9040/12/9/178 |
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| Summary: | 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 In<sub>2</sub>O<sub>3</sub> can monitor gas concentrations down to the ppm range, they often exhibit cross-sensitivity to other gases like H<sub>2</sub>O. In this study, we investigated whether cyclic optical illumination of a gas-sensitive In<sub>2</sub>O<sub>3</sub> layer creates identifiable changes in a gas sensor’s electronic resistance that can be linked to H<sub>2</sub> and H<sub>2</sub>O concentrations via machine learning. We exposed nanostructured In<sub>2</sub>O<sub>3</sub> with a large surface area of 95 m<sup>2</sup> g<sup>−1</sup> to H<sub>2</sub> 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 H<sub>2</sub> and H<sub>2</sub>O concentrations. |
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| ISSN: | 2227-9040 |