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Jiyuan YAN (闫纪源), Guishu LIANG (梁贵书), Hongliang LIAN (廉洪亮), Yanze SONG (宋岩泽), Haoou RUAN (阮浩鸥), Qijun DUAN (段祺君), Qing XIE (谢庆). Improving the surface flashover performance of epoxy resin by plasma treatment: a comparison of fluorination and silicon deposition under different modes[J]. Plasma Science and Technology, 2021, 23(11): 115501. DOI: 10.1088/2058-6272/ac15ee
Citation: Jiyuan YAN (闫纪源), Guishu LIANG (梁贵书), Hongliang LIAN (廉洪亮), Yanze SONG (宋岩泽), Haoou RUAN (阮浩鸥), Qijun DUAN (段祺君), Qing XIE (谢庆). Improving the surface flashover performance of epoxy resin by plasma treatment: a comparison of fluorination and silicon deposition under different modes[J]. Plasma Science and Technology, 2021, 23(11): 115501. DOI: 10.1088/2058-6272/ac15ee

Improving the surface flashover performance of epoxy resin by plasma treatment: a comparison of fluorination and silicon deposition under different modes

Funds: This work was supported by National Natural Science Foundation of China (No. 51777076) and the Self-topic Fund of the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (No. LAPS2019-21).
More Information
  • Received Date: March 21, 2021
  • Revised Date: July 14, 2021
  • Accepted Date: July 18, 2021
  • This work treats the Al2O3-ER sample surface using dielectric barrier discharge fluorination (DBDF), DBD silicon deposition (DBD-Si), atmospheric-pressure plasma jet fluorination (APPJ-F) and APPJ silicon deposition (APPJ-Si). By comparing the surface morphology, chemical components and electrical parameters, the diverse mechanisms of different plasma modification methods used to improve flashover performance are revealed. The results show that the flashover voltage of the DBDF samples is the largest (increased by 21.2% at most), while the APPJ-F method has the worst promotion effect. The flashover voltage of the APPJ-Si samples decreases sharply when treatment time exceeds 180 s, but the promotion effect outperforms the DBD-Si method during a short modified time. For the mechanism explanation, firstly, plasma fluorination improves the surface roughness and introduces shallow traps by etching the surface and grafting fluorine-containing groups, while plasma silicon deposition reduces the surface roughness and introduces a large number of shallow traps by coating SiOx film. Furthermore, the reaction of the DBD method is more violent, while the homogeneity of the APPJ modification is better. These characteristics influence the effects of fluorination and silicon deposition. Finally, increasing the surface roughness and introducing shallow traps accelerates surface charge dissipation and inhibits flashover, but too many shallow traps greatly increase the dissipated rate and facilitate surface flashover instead
  • [1]
    Ren C Y et al 2020 Plasma Sci. Technol. 22 044002
    [2]
    Li C Y et al 2018 IEEE Trans. Dielectr. Electr. Insul. 25 1238
    [3]
    Huang X W et al 2020 Plasma Sci. Technol. 22 085505
    [4]
    Gao Y et al 2020 IEEE Trans. Dielectr. Electr. Insul. 27 947
    [5]
    Shao T et al 2018 IEEE Trans. Dielectr. Electr. Insul. 25 1267
    [6]
    Cheng G X et al 2013 IEEE Trans. Dielectr. Electr. Insul.20 1942
    [7]
    Xie Q et al 2018 Plasma Sci. Technol. 20 025504
    [8]
    Li C Y et al 2021 J. Phys. D Appl. Phys. 54 015308
    [9]
    Liu Y Q et al 2019 Plasma Sci. Technol. 21 055501
    [10]
    Li C Y et al 2019 Appl. Phys. Lett. 114 202904
    [11]
    Xie Q et al 2017 IEEE Trans. Dielectr. Electr. Insul. 24 3395
    [12]
    Xue J Y et al 2018 J. Appl. Phys. 124 083302
    [13]
    An Z L et al 2015 IEEE Trans. Dielectr. Electr. Insul. 22 526
    [14]
    Li C Y et al 2016 J. Phys. D Appl. Phys. 49 445304
    [15]
    Lei S et al 2019 Surf. Coat. Technol. 363 362
    [16]
    Shao T et al 2016 High Volt. Eng. 42 685 (in Chinese)
    [17]
    Liu W Z et al 2019 Plasma Sci. Technol. 21 074004
    [18]
    Armenise V et al 2019 Surf. Coat. Technol. 379 125017
    [19]
    Zhang P H et al 2020 Surf. Coat. Technol. 387 125511
    [20]
    Zhan Z Y et al 2020 Trans. China Electr. Soc. 35 1787 (in Chinese)
    [21]
    Chen S L et al 2017 Appl. Surf. Sci. 414 107
    [22]
    Lin H F et al 2017 Trans. China Electr. Soc. 32 256 (in Chinese)
    [23]
    Ma Y Y et al 2018 High Volt. Eng. 44 3089 (in Chinese)
    [24]
    Hu D et al 2019 Proc. CSEE 39 4633 (in Chinese)
    [25]
    Zhang C et al 2019 Surf. Coat. Technol. 362 1
    [26]
    Kostov K G et al 2014 Appl. Surf. Sci. 314 367
    [27]
    Yan J Y et al 2021 Plasma Sci. Technol. 23 064012
    [28]
    Matsubara K et al 2013 Surf. Coat. Technol. 236 269
    [29]
    Kong F et al 2018 Appl. Surf. Sci. 459 300
    [30]
    Ramos R et al 2007 Plasma Sources Sci. Technol. 16 711
    [31]
    Riello D et al 2016 Ceramics Inter. 42 9804
    [32]
    Wang R X et al 2017 Plasma Proc. Polym. 14 1600248
    [33]
    Li S T et al 2020 High Volt. 5 122
    [34]
    Simmons J G and Tam M C 1973 Phys. Rev. B 7 3706
    [35]
    Kindersberger J and Lederle C 2008 IEEE Trans. Dielectr.Electr. Insul. 15 941
    [36]
    Shao T et al 2017 IEEE Trans. Dielectr. Electr. Insul. 24 1557
    [37]
    Hu D et al 2019 Trans. China Electr. Soc. 34 3512 (in Chinese)
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    3. Yan, X.-T., Bao, N.-N., Zhao, C.-Y. et al. NTVTOK-ML: Fast surrogate model for neoclassical toroidal viscosity torque calculation in tokamaks based on machine learning methods. Computer Physics Communications, 2025. DOI:10.1016/j.cpc.2024.109413
    4. Li, H., Wang, L., Fu, Y.L. et al. Surrogate model of turbulent transport in fusion plasmas using machine learning. Nuclear Fusion, 2025, 65(1): 016015. DOI:10.1088/1741-4326/ad8b5b
    5. Liu, Z.Y., Qiu, H.R., Fu, G.Y. et al. Prediction of fishbone linear instability in tokamaks with machine learning methods. Nuclear Fusion, 2025, 65(1): 016007. DOI:10.1088/1741-4326/ad8d69
    6. Xu, J., Luan, Q., Li, H. et al. Neural network based fast prediction of double tearing modes in advanced tokamak plasmas. Physics of Plasmas, 2024, 31(12): 122113. DOI:10.1063/5.0229910
    7. Zheng, G.H., Yang, Z.Y., Liu, S.F. et al. Real-time equilibrium reconstruction by multi-task learning neural network based on HL-3 tokamak. Nuclear Fusion, 2024, 64(12): 126041. DOI:10.1088/1741-4326/ad8014
    8. Zhang, H., Zhou, L., Liu, Y. et al. Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad-Shafranov solutions across multiple tokamaks. Plasma Science and Technology, 2024, 26(5): 055101. DOI:10.1088/2058-6272/ad13e3
    9. Li, G., Li, L., Qiao, X. et al. Research on Machine Learning-Assisted Pulse Discharge Plasma Catalysis for Methane-Carbon Dioxide Simulation. 2024. DOI:10.1109/ICCSNT62291.2024.10776678
    10. Liu, Y., Li, J. Gyro-Landau-fluid simulations of impurity effects on ion temperature gradient driven turbulence transport. Plasma Science and Technology, 2024, 26(1): 015101. DOI:10.1088/2058-6272/ad0c9b
    11. Li, H., Fu, Y.-L., Li, J.-Q. et al. Simulation Prediction of Heat Transport with Machine Learning in Tokamak Plasmas. Chinese Physics Letters, 2023, 40(12): 125201. DOI:10.1088/0256-307X/40/12/125201
    12. Li, H., Li, J.-Q., Wang, Z.-X. Global Effects on Drift Wave Microturbulence in Tokamak Plasmas. Chinese Physics Letters, 2023, 40(10): 105201. DOI:10.1088/0256-307X/40/10/105201
    13. Wang, Z., Qiu, Z., Wang, L. et al. Summary of the 10th Conference on Magnetically Confined Fusion Theory and Simulation (CMCFTS). Plasma Science and Technology, 2023, 25(8): 081001. DOI:10.1088/2058-6272/acc14d
    14. Liang, C., Huang, D., Lu, S. et al. Determining global property of dusty plasma from single particle dynamics using machine learning. Physical Review Research, 2023, 5(3): 033086. DOI:10.1103/PhysRevResearch.5.033086
    15. Pan, J., Liu, Y., Zhang, S. et al. Deep learning-assisted pulsed discharge plasma catalysis modeling. Energy Conversion and Management, 2023. DOI:10.1016/j.enconman.2022.116620
    16. Zheng, W., Xue, F., Shen, C. et al. Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak. Plasma Science and Technology, 2022, 24(12): 124003. DOI:10.1088/2058-6272/ac9e46
    17. Wai, J.T., Boyer, M.D., Kolemen, E. Neural net modeling of equilibria in NSTX-U. Nuclear Fusion, 2022, 62(8): 086042. DOI:10.1088/1741-4326/ac77e6
    18. Wang, T., Li, B., Gao, J. et al. Monitoring of two-dimensional tungsten concentration profiles on the HL-2A tokamak. Plasma Physics and Controlled Fusion, 2022, 64(8): 084003. DOI:10.1088/1361-6587/ac77b9
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    20. Ida, K., McDermott, R.M., Holland, C. et al. Joint meeting of 9th Asia Pacific-Transport Working Group (APTWG) & EU-US Transport Task Force (TTF) workshop. Nuclear Fusion, 2022, 62(3): 037001. DOI:10.1088/1741-4326/ac3f19
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