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

. 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.