Multi-gas recognition based on ZnO-Au-SnO₂ heterostructure gas sensors and one-dimensional convolutional neural...

Monitoring and identification of combustible gases are crucial tasks in fields such as industrial safety, environmental protection. In real-world scenarios, different types of combustible gases, such as H₂, C₂H₄, and C₂H₂, often coexist, which makes recognition more difficult. To address this challenge, we propose a gas sensor based on a ZnO-Au-SnO₂ heterostructure. The morphology and chemical state of the material were characterized using scanning electron microscopy and X-ray photoelectron spectroscopy, which revealed a uniform distribution of Au and binding-energy shifts indicative of electron transfer between ZnO and SnO₂. Experimental results demonstrate that modifying Au and SnO₂ significantly enhances the sensor's response to H₂, C₂H₄, and C₂H₂. By applying a one-dimensional convolutional neural network (1DCNN) to a sensor array, efficient feature extraction of response signals and high-precision gas classification were achieved. The recognition accuracy for gas mixtures exceeds 96%. Furthermore, the lightweight 1DCNN model has been deployed on a Raspberry Pi edge computing platform, verifying its feasibility on small-sized, low-power devices. The system achieves millisecond-level inference and high-confidence predictions, indicating its potential for real-time on-site gas detection. This study synergistically optimizes the gas sensing system from both material and algorithm aspects, providing a feasible way to the intelligent recognition of multi-component combustible gases.