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

作者：Mingzhi Jiao, Xinyang Chen, Bing Deng, Kaiguo Mao, Chenyu Wen · 发表于：Talanta · 年份：2026 · DOI：10.1016/j.talanta.2026.129904 · 被引用次数：1 · 研究领域：Gas Sensing Nanomaterials and Sensors、Advanced Chemical Sensor Technologies、Advanced Sensor and Energy Harvesting Materials

The 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...