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Shulei ZHENG, Qiuyue NIE, Tao HUANG, Chunfeng HOU, Xiaogang WANG. Improvement of atmospheric jet-array plasma uniformity assisted by artificial neural networks[J]. Plasma Science and Technology, 2023, 25(2): 025403. DOI: 10.1088/2058-6272/ac8dd6
Citation: Shulei ZHENG, Qiuyue NIE, Tao HUANG, Chunfeng HOU, Xiaogang WANG. Improvement of atmospheric jet-array plasma uniformity assisted by artificial neural networks[J]. Plasma Science and Technology, 2023, 25(2): 025403. DOI: 10.1088/2058-6272/ac8dd6

Improvement of atmospheric jet-array plasma uniformity assisted by artificial neural networks

More Information
  • Corresponding author:

    Qiuyue NIE, E-mail: nieqiuyue@hit.edu.cn

  • Received Date: February 18, 2022
  • Revised Date: August 23, 2022
  • Accepted Date: August 29, 2022
  • Available Online: December 05, 2023
  • Published Date: January 05, 2023
  • Atmospheric pressure plasma jet (APPJ) arrays have shown a potential in a wide range of applications ranging from material processing to biomedicine. In these applications, targets with complex three-dimensional structures often easily affect plasma uniformity. However, the uniformity is usually crucially important in application areas such as biomedicine, etc. In this work, the flow and electric field collaborative modulations are used to improve the uniformity of the plasma downstream. Taking a two-dimensional sloped metallic substrate with a 10° inclined angle as an example, the influences of both flow and electric field on the electron and typical active species distributions downstream are studied based on a multi-field coupling model. The electric and flow fields modulations are first separately applied to test the influence. Results show that the electric field modulation has an obvious improvement on the uniformity of plasma while the flow field modulation effect is limited. Based on such outputs, a collaborative modulation of both fields is then applied, and shows a much better effect on the uniformity. To make further advances, a basic strategy of uniformity improvement is thus acquired. To achieve the goal, an artificial neural network method with reasonable accuracy is then used to predict the correlation between plasma processing parameters and downstream uniformity properties for further improvement of the plasma uniformity. An optional scheme taking advantage of the flexibility of APPJ arrays is then developed for practical demands.

  • This work was supported by National Natural Science Foundation of China (Nos. 51577044 and 52022026).

    Data availability

    The data that support the findings of this study are available from the corresponding author upon reasonable request.

  • [1]
    Xu Z M et al 2020 Plasma Sci. Technol. 22 103001 doi: 10.1088/2058-6272/ab9ddd
    [2]
    Kaushik N K et al 2019 Nanomaterials 9 98 doi: 10.3390/nano9010098
    [3]
    Liu D W et al 2020 Plasma Process. Polym. 17 e1900218 doi: 10.1002/ppap.201900218
    [4]
    Boehm D and Bourke P 2019 Biol. Chem. 400 3 doi: 10.1515/hsz-2018-0222
    [5]
    Walsh J L, Cao Z and Kong M G 2008 IEEE Trans. Plasma Sci. 36 1314 doi: 10.1109/TPS.2008.924518
    [6]
    Li X M et al 2013 Appl. Phys. Lett. 103 033519 doi: 10.1063/1.4816061
    [7]
    Cao Z, Walsh J L and Kong M G 2009 Appl. Phys. Lett. 94 021501 doi: 10.1063/1.3069276
    [8]
    Nie Q et al 2009 New J. Phys. 11 115015 doi: 10.1088/1367-2630/11/11/115015
    [9]
    Ghasemi M et al 2013 J. Phys. D: Appl. Phys. 46 052001 doi: 10.1088/0022-3727/46/5/052001
    [10]
    Zhang C et al 2014 Appl. Phys. Lett. 105 044102 doi: 10.1063/1.4887992
    [11]
    Li X C et al 2020 Appl. Phys. Lett. 117 134102 doi: 10.1063/5.0027061
    [12]
    Cheng H et al 2020 Phys. Plasmas 27 063514 doi: 10.1063/5.0008881
    [13]
    Bao P et al 2016 IEEE Trans. Plasma Sci. 44 2673 doi: 10.1109/TPS.2016.2578955
    [14]
    Mohades S, Barekzi N and Laroussi M 2014 Plasma Process. Polym. 11 1150 doi: 10.1002/ppap.201400108
    [15]
    Zhang B et al 2018 Phys. Plasmas 25 063506 doi: 10.1063/1.5024013
    [16]
    Wan M et al 2017 Phys. Plasmas 24 093514 doi: 10.1063/1.4991531
    [17]
    Zhang B et al 2017 Plasma Sci. Technol. 19 064001 doi: 10.1088/2058-6272/aa629f
    [18]
    Zheng S L et al 2021 AIP Adv. 11 085219 doi: 10.1063/5.0060545
    [19]
    Babaeva N Y and Kushner M J 2014 Plasma Sources Sci. Technol. 23 015007 doi: 10.1088/0963-0252/23/1/015007
    [20]
    Kim J Y and Kim S O 2011 IEEE Trans. Plasma Sci. 39 2278 doi: 10.1109/TPS.2011.2157836
    [21]
    Zheng S L et al 2019 IEEE Trans. Plasma Sci. 47 4840 doi: 10.1109/TPS.2019.2926157
    [22]
    Xu G M et al 2021 Plasma Sci. Technol. 23 095401 doi: 10.1088/2058-6272/ac071a
    [23]
    Wang Z B and Nie Q Y 2015 AIP Adv. 5 097123 doi: 10.1063/1.4930835
    [24]
    Norberg S A, Johnsen E and Kushner M J 2015 J. Appl. Phys. 118 013301 doi: 10.1063/1.4923345
    [25]
    Zaplotnik R et al 2015 Spectrochim. Acta B 103–104 124 doi: 10.1016/j.sab.2014.12.004
    [26]
    Cheng H et al 2016 Phys. Plasmas 23 073517 doi: 10.1063/1.4955323
    [27]
    Choudhury T A, Hosseinzadeh N and Berndt C C 2011 Surf. Coat. Technol. 205 4886 doi: 10.1016/j.surfcoat.2011.04.099
    [28]
    Gidon D et al 2019 IEEE Trans. Radiat. Plasma Med. Sci. 3 597 doi: 10.1109/TRPMS.2019.2910220
    [29]
    Krüger F, Gergs T and Trieschmann J 2019 Plasma Sources Sci. Technol. 28 035002 doi: 10.1088/1361-6595/ab0246
    [30]
    Ding Z et al 2021 Plasma Sci. Technol. 23 095403 doi: 10.1088/2058-6272/ac125d
    [31]
    Breden D, Miki K and Raja L L 2012 Plasma Sources Sci. Technol. 21 034011 doi: 10.1088/0963-0252/21/3/034011
    [32]
    Liu X Y et al 2014 Plasma Sources Sci. Technol. 23 035007 doi: 10.1088/0963-0252/23/3/035007
    [33]
    Liu D X et al 2010 Plasma Sources Sci. Technol. 19 025018 doi: 10.1088/0963-0252/19/2/025018
    [34]
    Murakami T et al 2013 Plasma Sources Sci. Technol. 22 015003 doi: 10.1088/0963-0252/22/1/015003
    [35]
    Liu X Y et al 2014 Phys. Plasmas 21 093513 doi: 10.1063/1.4895496
    [36]
    Hornik K, Stinchcombe M and White H 1989 Neural Netw. 2 359 doi: 10.1016/0893-6080(89)90020-8
    [37]
    Werbos P J 1974 Beyond regression: new tools for prediction and analysis in the behavioral sciences PhD Thesis Harvard University, Cambridge, USA 1974
    [38]
    MacKay D J C 1992 Neural Comput. 4 415 doi: 10.1162/neco.1992.4.3.415
    [39]
    Parsey G, Lietz A M and Kushner M J 2021 J. Phys. D: Appl. Phys. 54 045206 doi: 10.1088/1361-6463/abbf1a
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    2. Liu, S., Ren, Z., Wang, W. et al. The instability prediction of non-resonant energetic particle modes based on machine learning algorithms. Physica Scripta, 2025, 100(3): 036007. DOI:10.1088/1402-4896/adafe4
    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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