Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching
arXiv:2605.23971v1 Announce Type: cross 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 concentrat
This work is a contemporary application of advanced AI/ML techniques to improve the performance and reliability of gas sensors, reflecting the ongoing convergence of AI with material science and chemical engineering.
A strategic reader should care because improved gas sensing technology, particularly for hazardous gases like carbon monoxide, has implications for industrial safety, environmental monitoring, and smart infrastructure.
The integration of physics-guided machine learning allows for more accurate and reliable concentration inference from complex sensor data, potentially reducing false positives and enhancing safety protocols.
- · Industrial safety sectors
- · Environmental monitoring companies
- · AI/ML in materials science
- · Smart home and building technology
- · Manufacturers of less reliable traditional gas sensors
- · Sectors reliant on manual gas detection methods
More accurate and rapid detection of toxic gases becomes possible.
This could lead to stricter safety regulations and the broader adoption of AI-enhanced sensor systems.
The methodology could be extended to other challenging sensing applications, accelerating the development of novel smart sensors across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG