{
  "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",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: IEEE Sensors Journal, 2026",
  "arxiv": "2605.23971",
  "author_total": 3,
  "authors": [
    {
      "name": "Sani Biswas"
    },
    {
      "name": "Preetam Singh"
    },
    {
      "name": "Amit Kumar Gangwar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9nw0ejfhkvrdf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.23971",
  "fulltext_url": null,
  "id": "9nw0ejfhkvrdf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.23971",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.app-ph"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.23971",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9nw0ejfhkvrdf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:41Z",
      "native_id": "2605.23971",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.23971"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.app-ph",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching",
  "title_text": "Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO_2 Carbon Monoxide Sensor with p-n Switching",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:41Z",
  "url": "https://research.draconitic.com/paper/9nw0ejfhkvrdf",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1109/jsen.2026.3723678",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}