Li Meihua, Gu Yunlong, Zhang Yunfan, Gao Xiaodong, Ge Shikun, Wei Guangfen
School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China.
Key Laboratory of Sensing Technology and Control in Universities of Shandong, Shandong Technology and Business University, Yantai 264005, China.
Phys Chem Chem Phys. 2023 Apr 12;25(15):10935-10945. doi: 10.1039/d2cp04396c.
This paper describes a tin oxide and copper doped tin oxide gas sensing material synthesized by a biological template method and simple hydrothermal reaction, which were used for the preparation of a gas sensor array. The sensor array is combined with the Sparrow Search Algorithm optimized BP neural network algorithm (SSA-BP) to predict and analyze the concentration of indoor toxic gases, including ammonia, xylene, and formaldehyde. Granular SnO was prepared by the biological template method and Cu/SnO doped with different copper ion concentrations was prepared by the hydrothermal method. The morphology of the synthesized nanomaterials was characterized by SEM, and the elemental composition and chemical state of the main elements were analyzed by XRD and XPS. The PL emission observed in the visible region is attributed to the defect level gap caused by oxygen. The optimal operating temperature, sensitivity, response/recovery time and the long-term stability of the sensor array have been studied. By combining the sensor array with the neural network algorithm in a simulated indoor environment at four humidity levels, the concentration information of the gas mixtures could be well predicted and the predicted concentration error was less than 0.84 ppm. Therefore, the sensor array prepared in this study combined with the SSA-BP algorithm achieved good results in predicting the concentrations of the three toxic mixtures.
本文描述了一种通过生物模板法和简单水热反应合成的氧化锡和铜掺杂氧化锡气敏材料，用于制备气体传感器阵列。该传感器阵列与麻雀搜索算法优化的BP神经网络算法（SSA-BP）相结合，用于预测和分析室内有毒气体（包括氨气、二甲苯和甲醛）的浓度。通过生物模板法制备了颗粒状SnO，并通过水热法制备了不同铜离子浓度掺杂的Cu/SnO。用扫描电子显微镜（SEM）对合成的纳米材料的形貌进行了表征，用X射线衍射仪（XRD）和X射线光电子能谱仪（XPS）分析了主要元素的组成和化学状态。在可见光区域观察到的光致发光发射归因于由氧引起的缺陷能级间隙。研究了传感器阵列的最佳工作温度、灵敏度、响应/恢复时间和长期稳定性。在四种湿度水平的模拟室内环境中，将传感器阵列与神经网络算法相结合，可以很好地预测气体混合物的浓度信息，预测浓度误差小于0.84 ppm。因此，本研究制备的传感器阵列与SSA-BP算法相结合，在预测三种有毒混合物的浓度方面取得了良好的效果。
