Abstract:This work presents a physics-guided machine-learning framework for carbon monoxide concentration inference from experimentally measured resistance transients of a mixed-phase SnO-SnO$_2$ material gas sensor exhibiting temperature-dependent p-n switching behavior. Cycle-level transient responses are represented through physically interpretable descriptors and complemented by compact fast Fourier transform (FFT) and discrete wavelet transform (DWT)-based summaries. Using leakage-aware grouped cross-validation, we study both multi-class concentration classification and continuous concentration regression for the p-type and n-type sensing regimes separately. Across both regimes, fused features provide the strongest overall performance, while the physics-guided descriptor block remains highly competitive, indicating that the dominant concentration information is already encoded in physically meaningful transient dynamics. The p-type branch shows the best concentration-class discrimination, with the fused Random Forest classifier reaching approximately $96.5\%$ accuracy, whereas the n-type branch yields the best quantitative concentration estimation, with the fused Random Forest regressor achieving an MAE$\approx 1.48$ ppm and an R$^2$ $\approx 0.992$. These results reveal a clear dual-regime behavior: p-type sensing is particularly favorable for classification, whereas n-type sensing is more favorable for high-fidelity regression. More broadly, the study demonstrates that leakage-aware, cycle-level, physics-guided machine learning can extend conventional gas-sensing analysis beyond single-response metrics while preserving physical interpretability
| Comments: | 15 pages, 14 figures |
| Subjects: | Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Applied Physics (physics.app-ph) |
| Cite as: | arXiv:2605.23971 [physics.chem-ph] |
| (or arXiv:2605.23971v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23971 arXiv-issued DOI via DataCite |
From: Sani Biswas [view email]
[v1]
Wed, 13 May 2026 10:37:32 UTC (2,273 KB)
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