Suppr超能文献

对用于检测SO₂、SO₂F₂和SO₂F₂的Ga掺杂MoS₂的第一性原理洞察

First-Principle Insight into Ga-Doped MoS for Sensing SO, SOF and SOF.

作者信息

Hou Wenjun, Mi Hongwan, Peng Ruochen, Peng Shudi, Zeng Wen, Zhou Qu

机构信息

College of Engineering and Technology, Southwest University, Chongqing 400715, China.

Chongqing Electric Power Research Institute, State Grid Chongqing Electric Power Company, Chongqing 401123, China.

出版信息

Nanomaterials (Basel). 2021 Jan 26;11(2):314. doi: 10.3390/nano11020314.

Abstract

First-principle calculations were carried out to simulate the three decomposition gases (SO, SOF, and SOF) of sulfur hexafluoride (SF) on Ga-doped MoS (Ga-MoS) monolayer. Based on density functional theory (DFT), pure MoS and multiple gas molecules (SF, SO, SOF, and SOF) were built and optimized to the most stable structure. Four types of Ga-doped positions were considered and it was found that Ga dopant preferred to be adsorbed by the top of Mo atom (T). For the best adsorption effect, two ways of SO, SOF, and SOF to approach the doping model were compared and the most favorable mode was selected. The adsorption parameters of Ga-MoS and intrinsic MoS were calculated to analyze adsorption properties of Ga-MoS towards three gases. These analyses suggested that Ga-MoS could be a good gas-sensing material for SO and SOF, while it was not suitable for SOF sensing due to its weak adsorption. This work provides a theoretical basis for the development of Ga-MoS materials with the hope that it can be used as a good gas-sensing material for electrical equipment.

摘要

进行第一性原理计算以模拟六氟化硫(SF₆)在Ga掺杂的MoS₂(Ga-MoS₂)单层上的三种分解气体(SO、SOF₂和SO₂F₂)。基于密度泛函理论(DFT),构建了纯MoS₂以及多个气体分子(SF₆、SO、SOF₂和SO₂F₂)并将其优化至最稳定结构。考虑了四种Ga掺杂位置,发现Ga掺杂剂更倾向于吸附在Mo原子顶部(T)。为了获得最佳吸附效果,比较了SO、SOF₂和SO₂F₂接近掺杂模型的两种方式并选择了最有利的模式。计算了Ga-MoS₂和本征MoS₂的吸附参数以分析Ga-MoS₂对三种气体的吸附性能。这些分析表明,Ga-MoS₂可能是用于SO和SOF₂的良好气敏材料,而由于其吸附较弱,它不适合用于SO₂F₂传感。这项工作为Ga-MoS₂材料的开发提供了理论基础,希望它能用作电气设备的良好气敏材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f80/7912144/d61b53cb893b/nanomaterials-11-00314-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验