Highly Sensitive Gas Sensor for Detection of Air Decomposition Pollutant (CO, NOx): Popular Metal Oxide (ZnO, TiO2)-Doped MoS2 Surface.

Mingxiang Wang, Qingbin Zeng, Jianjun Cao,Dachang Chen,Yiyi Zhang,Jiefeng Liu,Pengfei Jia

ACS applied materials & interfaces(2024)

引用 0|浏览0
暂无评分
摘要
When partial discharges occur in air-insulated equipment, the air decomposes to produce a variety of contamination products, resulting in a reduction in the insulation performance of the insulated equipment. By monitoring the concentration of typical decomposition products (CO, NO, and NO2) within the insulated equipment, potential insulation faults can be diagnosed. MoS2 has shown promising applications as a gas-sensitive semiconductor material, and doping metal oxides can improve the gas-sensitive properties of the material. Therefore, in this work, MoS2 has been doped using the popular metal oxides (ZnO, TiO2) of the day, and its gas-sensitive properties to the typical decomposition products of air have been analyzed and compared using density functional theory (DFT) calculations. The stability of the doped system was investigated using molecular dynamics methods. The related adsorption mechanism was analyzed by adsorption configuration, energy band structure, density of states (DOS) analysis, total electron density (TED) analysis, and differential charge density (DCD) analysis. Finally, the practical application of related sensing performance is evaluated. The results show that the doping of metal oxide nanoparticles greatly improves the conductivity, gas sensitivity, and adsorption selectivity of MoS2 monolayer to air decomposition products. The sensing response of ZnO-MoS2 for CO at room temperature (25 °C) reaches 161.86 with a good recovery time (0.046 s). TiO2-MoS2 sensing response to NO2 reaches 3.5 × 106 at 25 °C with a good recovery time (0.108 s). This study theoretically solves the industrial challenge of recycling sensing materials and provides theoretical value for the application of resistive chemical sensors in air-insulated equipment.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要