Adsorption and sensing of dissolved gas molecules in transformer oil on Rh-doped MoTe2 monolayer: A first-principles study

被引:4
|
作者
Liu, Bo [1 ,2 ]
Yuan, Ye [3 ]
Gong, Yong [4 ]
Zhou, Rong [3 ]
Li, Peng [3 ]
Cui, Hao [5 ]
机构
[1] NARI Grp Corp, State Grid Elect Power Res Inst, Nanjing 211106, Peoples R China
[2] Wuhan NARI Ltd Co, State Grid Elect Power Res Inst, Wuhan 430074, Peoples R China
[3] Three Gorges Univ, Coll Elect Engn & New Energy, Yichang 443002, Peoples R China
[4] Gansu Hongxing Construct Engn Co LTD, Jiuquan 435008, Peoples R China
[5] Southwest Univ, Coll Artificial Intelligence, Chongqing 400715, Peoples R China
基金
中国国家自然科学基金;
关键词
Oil-immersed transformer; Rh-MoTe; 2; monolayer; DFT; DGA; MOS2; MONOLAYER; AU; PERFORMANCE; NANOSHEETS; SO2; DGA; O-3; PD;
D O I
10.1016/j.comptc.2023.114149
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Oil-immersed transformer plays a significant role in power systems, whose insulation failure will threaten the stable operation of the system, thus the requirements for its operational stability are higher. In this paper, the Rh-doped MoTe2 (Rh-MoTe2) monolayer is proposed for the detection of several typically dissolved gas molecules (CO, H2 and CH4) based on the first-principles density functional theory (DFT). It is found that the Rh dopant preferred to be adsorbed through the TMo site with maximum binding energy (Eb) of-3.43 eV. Then, the adsorption of Rh-MoTe2 on CO, H2 and CH4 molecules is investigated with adsorption energy (Ead). Furthermore, the band gaps of CO and H2 systems have significantly reduced, showing their excellent potential as resistance-type gas sensors; while CH4 is not suitable for detection due to the weak interaction. This study illustrates the physicochemical properties of Rh-MoTe2 monolayer and reveals its promising applications in dissolved gas analysis (DGA).
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Gas sensing mechanism of dissolved gases in transformer oil on Ag-MoS2 monolayer: A DFT study
    Wang, Jingxuan
    Zhou, Qu
    Xu, Lingna
    Gao, Xin
    Zeng, Wen
    PHYSICA E-LOW-DIMENSIONAL SYSTEMS & NANOSTRUCTURES, 2020, 118 (118)
  • [22] Rh-Doped ZnO Monolayer as a Potential Gas Sensor for Air Decomposed Species in a Ring Main Unit: A First-Principles Study
    Wang, Yan
    Yang, Xin
    Hu, Cong
    Wu, Tian
    ACS OMEGA, 2021, 6 (24): : 15878 - 15884
  • [23] First-principles investigation of Pd nanoclusters on MoTe2 for sensing SF6 decomposition gases
    Kushwaha, Aditya
    Goel, Neeraj
    SURFACES AND INTERFACES, 2025, 59
  • [24] First-Principles Insight into a Ru-Doped SnS2 Monolayer as a Promising Biosensor for Exhale Gas Analysis
    Wan, Qianqian
    Chen, Xiaoqi
    Gui, Yingang
    ACS OMEGA, 2020, 5 (15): : 8919 - 8926
  • [25] Theoretical study of dissolved gas molecules in transformer oil adsorbed on Agn (n=1-3) cluster doped PtO2 monolayer
    Jiang, Tianyan
    Zhang, Wentao
    Zhang, Tao
    Yuan, Haoxiang
    Chen, Xi
    Bi, Maoqiang
    CHEMICAL PHYSICS LETTERS, 2022, 806
  • [26] Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe2 monolayer: A first-principles theory
    Cui, Hao
    Chen, Dachang
    Zhang, Ying
    Zhang, Xiaoxing
    SUSTAINABLE MATERIALS AND TECHNOLOGIES, 2019, 20
  • [27] Fault signature gas sensing by the Ni-dispersed SnS2 monolayer for operation status evaluation of dry-type transformer: a first-principles study
    Chen, Zhigang
    Qi, Chaoliang
    Chen, Xin
    Bao, Yuzhe
    Yang, Yanan
    Zhou, Fan
    MOLECULAR PHYSICS, 2024,
  • [28] Adsorption and sensing performances of transition metal (Pd, Pt, Ag and Au) doped MoTe2 monolayer upon NO2: A DFT study
    Liu, Yun
    Shi, Ting
    Si, Quanlong
    Liu, Tun
    PHYSICS LETTERS A, 2021, 391
  • [29] Adsorption of gas molecules on buckled GaAs monolayer: a first-principles study
    Shahriar, Rifat
    Hassan, Orchi
    Alam, Md Kawsar
    RSC ADVANCES, 2022, 12 (26) : 16732 - 16744
  • [30] Outstanding sensing property of Cu-substituted MoTe2 monolayer upon SF6 decomposed species from first-principles calculations
    Zhou, Xiu
    Bai, Jin
    Cui, Hao
    Tian, Tian
    Luo, Yan
    Tian, Lu
    COMPUTATIONAL AND THEORETICAL CHEMISTRY, 2023, 1228