Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
3.1. Characterization of Ordered Mesoporous In2O3
3.2. Resistance Changes during Cyclic Optical Activation and Gas Exposure
3.3. Gas Concentration Classification
Faleh et al. [65] | Tonezzer, Van Duy et al. [27] | Kim, Shin et al. [66] | Ge et al. [50] | Yoon, Park et al. [13] | This Study | |
material | WO3 | SnO2 + Ag, Pt | ZnO | commercial | In2O3 + Au | In2O3 |
no. sensor(s) | 4 | 4 | 1 | 1 | 1 | 5 |
operation mode | static T a | static T a | cyclic T a | cyclic T a | time-variant illumination | cyclic illumination |
data analysis | SVM | SVM | CNN b | SVM | CNN b | SVM |
data preprocessing | yes | no | yes | yes | yes | no |
classification rate/% | 93.03 | 100 | 93.9 | 95.4 | 96.53 | 92.0 |
3.4. Single-Gas Concentration Classification and Cycle Normalization
3.5. Classification Success for Different Stages of the Optical Activation Process
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sample | Pore Size (a) nm | Pore Size (b) nm | Surface Area (c) m2 g−1 | Pore Volume (d) mL g−1 |
---|---|---|---|---|
silica | 5.1 | 6.6 | 550 | 0.83 |
In2O3 | 6.0; 12.3 | 8.1; 14.5 | 95 | 0.62 |
constant H2 concentration [ppm] | 0 | 200 | 400 | 600 | 800 |
H2O classification | 79.8 ± 8.5 | 96.6 ± 1.7 | 96.2 ± 1.5 | 99.0 ± 1.3 | 99.4 ± 0.8 |
constant H2O concentration [%] | 0 | 30 | 50 | 70 |
H2 classification | 97.8 ± 1.7 | 96.4 ± 0.8 | 97.0 ± 1.7 | 95.1 ± 1.0 |
Dataset | LED Status | Duration of ... s | Classification Rate % | ||
---|---|---|---|---|---|
Illumination | Darkness | Dataset | |||
(a) | on-off | 5 | 5 | 10 | 88.4 ± 3.0 |
(b) | on | 5 | 0 | 5 | 80.4 ± 4.5 |
(c) | off | 0 | 5 | 5 | 81.2 ± 6.0 |
(d) | off-on | 2.5 | 2.5 | 5 | 86.6 ± 4.5 |
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Baier, D.; Krüger, A.; Wagner, T.; Tiemann, M.; Weinberger, C. Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. Chemosensors 2024, 12, 178. https://doi.org/10.3390/chemosensors12090178
Baier D, Krüger A, Wagner T, Tiemann M, Weinberger C. Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O. Chemosensors. 2024; 12(9):178. https://doi.org/10.3390/chemosensors12090178
Chicago/Turabian StyleBaier, Dominik, Alexander Krüger, Thorsten Wagner, Michael Tiemann, and Christian Weinberger. 2024. "Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O" Chemosensors 12, no. 9: 178. https://doi.org/10.3390/chemosensors12090178