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A machine learning feature descriptor approach: Revealing potential adsorption mechanisms for SF6 decomposition product gas-sensitive materials

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成果类型:
期刊论文
作者:
Wang, Mingxiang;Zeng, Qingbin;Chen, Dachang;Zhang, Yiyi;Liu, Jiefeng;...
通讯作者:
Jia, PF
作者机构:
[Zhang, Yiyi; Jia, PF; Wang, Mingxiang; Jia, Pengfei; Zeng, Qingbin; Liu, Jiefeng] Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China.
[Wang, Mingxiang; Jia, Pengfei] Guangxi Univ, Guangxi Key Lab intelligent Control & Maintenance, Nanning 530004, Peoples R China.
[Chen, Dachang] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
[Ma, Changyou] Neijiang Normal Univ, Data Recovery Key Lab Sichuan Prov, Neijiang 641100, Peoples R China.
通讯机构:
[Jia, PF ] G
Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China.
语种:
英文
关键词:
All data in this work were obtained from the Web of Science database. Keywords searched include “SF6;decomposition;gas sensor;adsorption;DFT;etc.”. After an initial manual screening;a total of 52 research efforts from the past decade (from 2014 to 2024) were selected;focusing on the exploration of gas-sensitive materials for SF6 decomposition products;yielding 250 sets of adsorption data [26];[42] With the help of VOS Viewer software;the collected papers on SF6 decomposition products were clustered and analyzed;and the data were visualized by the keyword co-occurrence mapping module;as shown in Fig. S1 (a);(b) and (c). Fig. S1 (a) presents the changes in the research hotspots related to SF6 decomposition products during the five-year period from 2018 to 2022. Obviously;until 2020;some keywords can be observed;such as ‘decomposition’;‘voltage’;‘characteristic’;‘gas mixture’.
期刊:
Journal of Hazardous Materials
ISSN:
0304-3894
年:
2025
卷:
481
页码:
136567
基金类别:
National Natural Science Foundation of China [61906160]; Natural Science Foundation of Hubei Province [2022CFB941]; Sichuan Science and Technology Program [2022NSFSC1632]; Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province [DRN2002]
机构署名:
本校为其他机构
院系归属:
电气与电子工程学院
摘要:
The man-made gas sulfur hexafluoride (SF6) is an excellent and stable insulating medium. However, some insulation defects can cause SF6 to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF6 in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO2, SOF2, SO2F2, H2S, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material ...

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