物理引导的混合相SnO-SnO$_2$一氧化碳传感器中具有p-n切换的电阻瞬态浓度推断
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO$_2$ Carbon Monoxide Sensor with p-n Switching
AI总结：
提出一种物理引导的机器学习框架，利用混合相SnO-SnO$_2$气体传感器的电阻瞬态信号推断CO浓度，通过物理可解释描述符和频域特征实现p型和n型传感模式下的分类与回归，揭示了p型利于分类、n型利于高保真回归的双模行为。
摘要：
本工作提出一个物理引导的机器学习框架，用于从实验测量的混合相SnO-SnO$_2$材料气体传感器的电阻瞬态信号中推断一氧化碳浓度，该传感器表现出温度依赖的p-n切换行为。周期级瞬态响应通过物理可解释的描述符表示，并辅以紧凑的快速傅里叶变换（FFT）和离散小波变换（DWT）摘要。使用考虑泄漏的分组交叉验证，我们分别研究了p型和n型传感模式下的多类浓度分类和连续浓度回归。在两种模式下，融合特征提供了最强的整体性能，而物理引导的描述符块仍然具有很强的竞争力，表明主要的浓度信息已经编码在物理上有意义的瞬态动力学中。p型分支显示出最佳的浓度类别区分能力，融合随机森林分类器达到约96.5%的准确率，而n型分支产生最佳的定量浓度估计，融合随机森林回归器实现了MAE≈1.48 ppm和R²≈0.992。这些结果揭示了清晰的双模行为：p型传感特别有利于分类，而n型传感更有利于高保真回归。更广泛地说，该研究表明，考虑泄漏的、周期级的、物理引导的机器学习可以将传统的气体传感分析扩展到单一响应指标之外，同时保持物理可解释性。
英文摘要：
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
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