Room-temperature SnO ₂ /NiO heterojunction sensor for low-ppm CO and CH ₄ detection: Implications for natural gas safety and emissions monitoring

The consistent detection of low-level carbon monoxide (CO) and methane (CH 4 ) is important for safety, environmental monitoring, and early leak identification. Semiconductor metal oxides are considered to be the most promising candidates for such applications. However, their performance is usually hindered by limited speed and less sensitivity. Herein, three heterojunction systems—ZnO/SnO 2 (Z/S) (n–n), NiO/ZnO (N/Z) (p–n), and SnO 2 /NiO (S/N) (p–n)—were synthesized through a controlled hydrothermal route. A systematic comparative study of these n–n and p–n interfaces is carried out to determine the most effective heterojunction structure for improved room-temperature CO and CH 4 sensing performance. Structural and morphological studies have proved a well-defined interface with homogenous surface features. In fact, the phase purity was well proved by X-ray diffraction, the homogeneous element distribution was identified by field emission scanning microscopy/energy dispersive X-ray spectroscopy, and the surface roughness was decreased, as determined by atomic force microscopy 2D/3D profiles with R a values of 81.547 nm (Z/S), 46.084 nm (N/Z), and 24.272 nm (S/N). Ultraviolet–visible spectroscopy confirmed the sequential bandgap narrowing: 3.2 eV for Z/S, 2.6 eV for N/Z, and 2.4 eV for S/N, indicative of enhanced electron interaction across the interfaces. J–V measurements exhibited diode-like behavior, showing a sequential decrease in cutoff voltage to 0.7, 0.6, and 0.4 V, respectively. Accordingly, electrochemical impedance spectroscopy analysis showed a decrease in charge-transfer resistance from 16.2 to 2.2 and 1.4 kΩ. Improved electron mobility has also been demonstrated from thickness measurements, which revealed that the S/N film exhibited the thinnest layer (0.4 μm) relative to Z/S (2.0 μm) and N/Z films (1.4 μm). This was further emphasized in the gas-sensing experiment, which demonstrated the clear omnipotence of the S/N heterostructure; hence, it enabled a fast response/recovery time of 15/24 s for CO and 18/42 s for CH 4 at low concentrations with high sensitivity and long-term stability in ambient conditions. The improved sensing ability could be ascribed to the strong p–n junction, smallest depletion width, and low interfacial resistance. Moreover, the sensing ability of the sensor was validated using machine learning algorithms, wherein consistently, the Random Forest (RF) algorithm appeared to have a stronger predictive power than Support Vector Machine (SVM). In fact, RF performed better than SVM by achieving higher R-Squared (R 2 ) values for each regression problem, which were generally in the range of 0.80–0.85 compared to the lower and more fluctuating performance of SVM. Likewise, a drift was noticed in the classification performance where RF achieved accuracies of 0.91/0.88 for CO and 0.93/0.89 for CH 4 under A1 and A2 scenarios, respectively, outperforming SVM in all cases. Finally, this study hereby confirms that the concept of heterojunction, specifically the S/N interface, combined with machine learning-assisted analysis, provides a promising route forward for the realization of next-generation room temperature gas sensors with high sensitivity, fast response, and accurate predictive capabilities.