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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MDPI AG
2024-09-01
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| author | Dominik Baier Alexander Krüger Thorsten Wagner Michael Tiemann Christian Weinberger |
| author_facet | Dominik Baier Alexander Krüger Thorsten Wagner Michael Tiemann Christian Weinberger |
| author_sort | Dominik Baier |
| collection | DOAJ |
| description | 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. |
| format | Article |
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| language | English |
| publishDate | 2024-09-01 |
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| spelling | doaj-art-fb09547e8f2c4e55ae237426ae0e866a2025-08-20T01:55:27ZengMDPI AGChemosensors2227-90402024-09-0112917810.3390/chemosensors12090178Gas 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>ODominik Baier0Alexander Krüger1Thorsten Wagner2Michael Tiemann3Christian Weinberger4Department of Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, GermanyDepartment of Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, GermanyDepartment of Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, GermanyDepartment of Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, GermanyDepartment of Chemistry, Paderborn University, Warburger Str. 100, D-33098 Paderborn, GermanyClean 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.https://www.mdpi.com/2227-9040/12/9/178resistive gas sensorchemiresistorsemiconductormetal oxideIn<sub>2</sub>O<sub>3</sub>mesoporous |
| spellingShingle | Dominik Baier Alexander Krüger Thorsten Wagner Michael Tiemann Christian Weinberger 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 Chemosensors resistive gas sensor chemiresistor semiconductor metal oxide In<sub>2</sub>O<sub>3</sub> mesoporous |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | resistive gas sensor chemiresistor semiconductor metal oxide In<sub>2</sub>O<sub>3</sub> mesoporous |
| url | https://www.mdpi.com/2227-9040/12/9/178 |
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