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

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 H 2 , C 2 H 4 , and C 2 H 2 , often coexist, which makes recognition more difficult. To address this challenge, we propose a gas sensor based on a ZnO–Au–SnO 2 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 SnO 2 . Experimental results demonstrate that modifying Au and SnO 2 significantly enhances the sensor's response to H 2 , C 2 H 4 , and C 2 H 2 . 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.