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基于深度学习的钻孔辐射压离子加速建模

张普渡 王伟权 李哲民 张资旋 王叶晨 周泓宇 银燕

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基于深度学习的钻孔辐射压离子加速建模

张普渡, 王伟权, 李哲民, 张资旋, 王叶晨, 周泓宇, 银燕

Modeling of ion accelerated by borehole radiation pressure based on deep learning

Zhang Pu-Du, Wang Wei-Quan, Li Zhe-Min, Zhang Zi-Xuan, Wang Ye-Chen, Zhou Hong-Yu, Yin Yan
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  • 超短超强激光脉冲与固体靶相互作用可通过钻孔辐射压加速机制产生百MeV量级的离子束, 离子束的品质强烈依赖于激光和靶的作用参量. 本文以近400组激光驱动固体靶的粒子模拟结果作为数据集, 以激光强度、靶密度、靶厚和离子质量作为输入参量, 基于全连接神经网络建立了一个离子峰值能量和截止能量连续映射模型. 该模型用较为稀疏的参量取值获得了较大参量范围内的分析结果, 大大减少了多维参量大范围扫参的计算量. 基于连续映射模型的结果, 得到了钻孔辐射压加速机制下离子峰值能量的修正公式和截止能量的拟合公式, 可为激光离子加速的实验设计提供重要参考.
    Laser-driven ion acceleration has potential applications in high energy density matter, ion beam-driven fast ignition, beam target neutron source, warm dense matter heating, etc. Ultrashort relativistic laser interacting with solid target can generate ion beam with several hundreds of MeV in energy, and the quality of the ion beam depends strongly on the interaction parameters between the laser and the target. Development in deep learning can provide new method of analyzing the relationship between parameters in physics system, which can significantly reduce the computational and experimental cost. In this paper, a continuous mapping model of ion peak and cutoff energy is developed based on a fully connected neural network (FCNN). In the model, the dataset is composed of nearly 400 sets of particle simulations of laser-driven solid targets, and the input parameters are laser intensity, target density, target thickness, and ion mass. The model uses sparse parameter values to obtain the analysis results in a large range of parameters, which greatly reduces the computational amount of multi-dimensional parameters sweeping in a wide range. Based on the results of this model mapping, the correction formula for the ion peak energy is obtained. Furthermore, the ratio of ion cutoff energy to peak energy of each set of particle simulation is calculated. Repeating the same training process of ion peak energy and cutoff energy, the continuous mapping model of energy ratio is developed. According to the energy ratio model mapping results, the quantitative description of the relationship between ion cutoff energy and peak energy is realized, and the fitting formula for the cutoff energy of the hole-boring radiation pressure acceleration (HB-RPA) mechanism is obtained, which can provide an important reference for designing the laser-driven ion acceleration experiments.
      通信作者: 王伟权, weiquan.wang@nudt.edu.cn ; 银燕, yyin@nudt.edu.cn
    • 基金项目: 国家自然科学基金青年基金(批准号: 12005298)、国家自然科学基金联合项目“叶企孙”科学基金(批准号: U2241281)、湖南省自然科学基金 (批准号: 2022JJ30656)、湖南省自然科学基金青年基金(批准号: 2021JJ40661)和国防科技大学科研计划(批准号: ZK19-25)资助的课题.
      Corresponding author: Wang Wei-Quan, weiquan.wang@nudt.edu.cn ; Yin Yan, yyin@nudt.edu.cn
    • Funds: Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 12005298), the “Ye Qisun” Science Fund of the National Natural Science Foundation of China (Grant No. U2241281), the Natural Science Foundation of Hunan Province, China (Grant No. 2022JJ30656), the Young Scientists Fund of the National Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ40661), and the Research Programm of NUDT (Grant No. ZK 19-25).
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    Fernandez J C, Honrubia J J, Albright B J, Flippo K A, Gautier D C, Hegelich B M, Schmitt M J, Temporal M, Yin L 2009 Nucl. Fusion 49 065004Google Scholar

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    Roth M, Jung D, Falk K, et al. 2013 Phys. Rev. Lett. 110 044802.Google Scholar

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    Jiang X R, Shao F Q, Zou D B, Yu M Y, Hu L X, Guo X Y, Huang T W, Zhang H, Wu S Z, Zhang G B 2020 Nucl. Fusion 60 076019Google Scholar

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    Mancic A, Levy A, Harmand M, Nakatsutsumi M, Antici P, Audebert P, Combis P, Fourmaux S, Mazevet S, Peyrusse O, Recoules V, Renaudin P, Robiche J, Dorchies F, Fuchs J 2010 Phys. Rev. Lett. 104 035002Google Scholar

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    Faenov A Y, Pikuz T A, Fukuda Y, et al. 2009 JETP Lett. 89 485Google Scholar

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    Faenov A Y, Pikuz T A, Fukuda Y, et al. 2009 Appl. Phys. Lett. 95 101107Google Scholar

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    Esirkepov T, Borghesi M, Bulanov S V, Mourou G, Tajima T 2004 Phys. Rev. Lett. 92 175003Google Scholar

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    Pegoraro F, Bulanov S V. 2007 Phys. Rev. Lett. 99 065002Google Scholar

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    Mackinnon A J, Borghesi M, Hatchett S, KeyM H, Patel P K, Campbell H, Schiavi A, Snavely R, Wilks S C, Willi O 2001 Phys. Rev. Lett. 86 1769Google Scholar

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    Yin L, Albright B J, Hegelich B M 2006 Laser Part. Beams 24 291Google Scholar

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    Yin L, Albright B J, Hegelich B M, Bowers K J, Flippo K A, Kwan T J T, Fernández J C 2007 Phys. Plasmas 14 056706Google Scholar

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    Silva L O, Marti M, Davies J R, Fonseca R A, Ren C, Tsung F S, Mori W B 2004 Phys. Rev. Lett. 92 015002Google Scholar

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    Chen M, Sheng Z M, Dong Q L, He M, Li Y T, Bari M A, Zhang J 2007 Phys. Plasmas 14 053102Google Scholar

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    Macchi A, Veghini S, Pegoraro F 2009 Phys. Rev. Lett. 103 085003Google Scholar

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    Robinson A P L, Kwon D H, Lancaster K 2009 Plasma. Phys. Controlled Fusion 51 095006Google Scholar

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    Volkova T M, Nerush E N, Kostyukov I Y 2021 Quantum Electron. 51 854Google Scholar

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    Miyatake T, Shiokawa K, Sakaki H, Dover N P, Nishiuchi M, Lowe H F, Kondo K, Kon A, Kando M, Kondo K, Watanabe Y 2021 Nucl. Instrum. Methods in Phys. Res. Sect. A 999 165227Google Scholar

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    Djordjević B Z, Kemp A J, J Kim 2021 Phys. Plasmas 28 043105Google Scholar

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    Djordjević B Z, Kemp A J, J Kim Ludwig J, Simpson R A, Wilks S C, Ma T, Mariscal D A 2021 Plasma Phys. Controlled Fusion 63 094005Google Scholar

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    Qiao B, Zepf M, Borghesi M, Dromey B, Geissler M, Karmakar A, Gibbon P 2010 Phys. Rev. Lett. 105 155002Google Scholar

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    Arber T D, Bennett K, Brady C S, Lawrence D A, Ramsay M G, Sircombe N J, Gillies P, Evans R G, Schmitz H, Bell A R, Ridgers C P 2015 Plasma Phys. Controlled Fusion 57 113001Google Scholar

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  • 图 1  $t = 70{T_0}$时刻, (a) He2+离子的$x{\text{-}}v$相空间分布, (b) He2+离子的能谱分布, (c) C6+离子的$x{\text{-}}v$相空间分布, (d) C6+离子的能谱分布. 其中, 激光强度$a = 100$, 靶密度$n = 25{n_{\text{c}}}$, 靶厚 $d = 25{\lambda _0}$; 红色虚线框内为峰值能量所对应的离子, 蓝色虚线框内为截止能量所对应的离子

    Fig. 1.  At $t = 70{T_0}$, (a) the $x{\text{-}}v$ phase space diagram of He2+, (b) the energy spectrum of He2+, (c) the $x{\text{-}}v$ phase space diagram of C6+, (d) the energy spectrum of C6+, with $ d = 25{\lambda _0} $, $n = 25{n_{\text{c}}}$ and $a = 100$. The red dashed box circles the ions with peak energy, and the blue dashed box circles the ions with cutoff energy.

    图 2  离子峰值能量随输入参数$d$, $n$$a$分布的三维散点图 (a) 质子; (b) He2+ 离子; (c) C6+ 离子; (d) O8+离子

    Fig. 2.  Scatter plot depiction of ion peak energy data ensembles as a function of input parameters d, n and a: (a) Proton case; (b) He2+, (c) C6+; (d) O8+.

    图 3  训练所使用的神经网络结构

    Fig. 3.  Neural network architecture used in the following training.

    图 4  He2+离子 (a) 峰值能量${E_{\text{p}}}$与 (b) 截止能量${E_{\text{m}}}$的二维连续映射图, 其中靶厚$d = 15{\lambda _0}$, 参数映射范围为$63.2 \leqslant a \leqslant 173.2$$10{n_{\text{c}}} \leqslant n \leqslant 40{n_{\text{c}}}$

    Fig. 4.  Two-dimensional continuous mapping of peak energy ${E_{\text{p}}}$ (a) and cutoff energy ${E_{\text{m}}}$(b) for He2+ over $63.2 \leqslant a \leqslant 173.2$ and $10{n_{\text{c}}} \leqslant n \leqslant 40{n_{\text{c}}}$ with $d = 15{\lambda _0}$.

    图 5  (a) He2+离子峰值能量${E_{\text{p}}}$关于靶密度$n$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 激光强度$a = 100$; (b) He2+离子峰值能量${E_{\text{p}}}$关于激光强度$a$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 靶密度$n = 20{n_{\text{c}}}$; (c) He2+离子峰值能量${E_{\text{p}}}$关于靶厚$d$的映射曲线, 其中靶密度$n = 20{n_{\text{c}}}$, 激光强度$a = 100$; (d)离子峰值能量${E_{\text{p}}}$关于离子质量数$A$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 靶密度$n = 20{n_{\text{c}}}$, 激光强度$a = 100$. 图中红色实线为代理模型的映射曲线, 模型标准差用红色色块填充于代理模型曲线两侧. 蓝色虚线给出的是根据HB-RPA机制理论公式所绘出的离子峰值能量随离子密度变化的曲线, 黑色实心数据点为测试集数据点, 参数取值范围与模型预测范围用黑色虚线分隔

    Fig. 5.  (a) Parameter scan of He2+ peak energy ${E_{\text{p}}}$ over target density $n$ with $d = 15{\lambda _0}$, $a = 100$; (b) parameter scan of He2+ peak energy ${E_{\text{p}}}$ over laser intensity $a$ with $d = 15{\lambda _0}$, $n = 20{n_{\text{c}}}$; (c) parameter scan of He2+ peak energy ${E_{\text{p}}}$over target thickness $d$ with $a = 100$, $n = 20{n_{\text{c}}}$; (d) parameter scan of ion peak energy ${E_{\text{p}}}$ over ion mass number $A$ with $a = 100$, $n = 20{n_{\text{c}}}$ and $d = 15{\lambda _0}$. The SE mapping curves are drawn with red solid, the theoretical curves are drawn with dashed, the red filled region indicates the standard deviation, and the untrained data from test subset are drawn with black dot.

    图 6  $d = 15{\lambda _0}$, $n = 20{n_{\text{c}}}$$a = 100$下, 离子峰值能量随离子质量的变化

    Fig. 6.  Ion peak energy scan with ion mass number $A$ with $d = 15{\lambda _0}$, $n = 20{n_{\text{c}}}$ and $a = 100$.

    图 7  (a) He2+离子截止能量${E_{\text{m}}}$关于靶密度$n$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 激光强度$a = 100$; (b) He2+离子截止能量${E_{\text{m}}}$关于激光强度$a$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 靶密度$n = 20{n_{\text{c}}}$; (c) He2+离子截止能量${E_{\text{m}}}$关于靶厚$d$的映射曲线, 其中靶密度$n = 20{n_{\text{c}}}$, 激光强度$a = 100$; (d) 离子截止能量${E_{\text{m}}}$关于离子质量数$A$的映射曲线, 其中靶厚$d = 15{\lambda _0}$, 靶密度$n = 20{n_{\text{c}}}$, 激光强度$a = 100$

    Fig. 7.  (a) Parameter scan of He2+ cutoff energy ${E_{\text{m}}}$ over target density $n$ with $d = 15{\lambda _0}$, $a = 100$; (b) parameter scan of He2+ cutoff energy ${E_{\text{m}}}$ over laser intensity $a$ with $d = 15{\lambda _0}$, $n = 20{n_{\text{c}}}$; (c) parameter scan of He2+ cutoff energy ${E_{\text{m}}}$ over target thickness $d$ with $a = 100$, $n = 20{n_{\text{c}}}$; (d) parameter scan of ion cutoff energy ${E_{\text{m}}}$ over ion mass number $A$ with $a = 100$, $n = 20{n_{\text{c}}}$ and $d = 15{\lambda _0}$.

    图 8  (a) 质子, (b) He2+, (c) C6+和 (d) O8+的能量比值$k = {E_{\text{m}}}/{E_{\text{p}}}$的二维连续映射图, 其中靶厚$d = 20{\lambda _0}$, 参数映射范围为$63.2 \leqslant a \leqslant 173.2$$10{n_{\text{c}}} \leqslant n \leqslant 30{n_{\text{c}}}$

    Fig. 8.  Two-dimensional continuous mapping of $k = {E_{\text{m}}}/{E_{\text{p}}}$ over $63.2 \leqslant a \leqslant 173.2$ and $10{n_{\text{c}}} \leqslant n \leqslant 30{n_{\text{c}}}$ with $d = 20{\lambda _0}$: (a) Proton case; (b) He2+; (c) C6+; (d) O8+.

    图 9  C6+离子能量比值$k$随(a)激光强度和(b)靶密度的映射曲线(实线)和拟合公式结果(虚线), 其中参数范围分别为$63.2 \leqslant a \leqslant 173.2$, $10{n_{\text{c}}} \leqslant n \leqslant 30{n_{\text{c}}}$, 靶厚$d = 15{\lambda _0}$

    Fig. 9.  Parameter scan (solid) and fitted formula (dashed) of k over (a) $63.2 \leqslant a \leqslant 173.2$ and (b) $10{n_{\text{c}}} \leqslant n \leqslant 30{n_{\text{c}}}$ for C6+ with $d = 15{\lambda _0}$.

    表 1  数值模拟数据集参数取值分布

    Table 1.  Simulation datasets prepared for neural network training.

    离子种类 模拟组数 $d/{\lambda _0}$ $n/{n_{\text{c}}}$ $a$
    Proton 231 [7.5, 35] [12.5, 40] [63.2, 173.2]
    He2+ 49 15, 25 [10, 30] [77.5, 173.2]
    C6+ 50 15, 25 [10, 30] [77.5, 173.2]
    O8+ 50 15, 25 [10, 30] [77.5, 173.2]
    其他 20 独立分布
    下载: 导出CSV
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    Roth M, Cowan T E, Key M H, et al. 2001 Phys. Rev. Lett. 86 436Google Scholar

    [2]

    Fernandez J C, Honrubia J J, Albright B J, Flippo K A, Gautier D C, Hegelich B M, Schmitt M J, Temporal M, Yin L 2009 Nucl. Fusion 49 065004Google Scholar

    [3]

    Roth M, Jung D, Falk K, et al. 2013 Phys. Rev. Lett. 110 044802.Google Scholar

    [4]

    Jiang X R, Shao F Q, Zou D B, Yu M Y, Hu L X, Guo X Y, Huang T W, Zhang H, Wu S Z, Zhang G B 2020 Nucl. Fusion 60 076019Google Scholar

    [5]

    Patel P K, Mackinnon A J, Key M H, Cowan T E, Foord M E, Allen M, Price D F, Ruhl H, Springer P T, Stephens R 2003 Phys. Rev. Lett. 91 125004Google Scholar

    [6]

    Mancic A, Levy A, Harmand M, Nakatsutsumi M, Antici P, Audebert P, Combis P, Fourmaux S, Mazevet S, Peyrusse O, Recoules V, Renaudin P, Robiche J, Dorchies F, Fuchs J 2010 Phys. Rev. Lett. 104 035002Google Scholar

    [7]

    Pelka A, Gregori G, Gericke D O, et al. 2010 Phys. Rev. Lett. 105 265701Google Scholar

    [8]

    Faenov A Y, Pikuz T A, Fukuda Y, et al. 2009 JETP Lett. 89 485Google Scholar

    [9]

    Faenov A Y, Pikuz T A, Fukuda Y, et al. 2009 Appl. Phys. Lett. 95 101107Google Scholar

    [10]

    Esirkepov T, Borghesi M, Bulanov S V, Mourou G, Tajima T 2004 Phys. Rev. Lett. 92 175003Google Scholar

    [11]

    Pegoraro F, Bulanov S V. 2007 Phys. Rev. Lett. 99 065002Google Scholar

    [12]

    Snavely R A, Key M H, Hatchett S P, et al. 2000 Phys. Rev. Lett. 85 2945Google Scholar

    [13]

    Mackinnon A J, Borghesi M, Hatchett S, KeyM H, Patel P K, Campbell H, Schiavi A, Snavely R, Wilks S C, Willi O 2001 Phys. Rev. Lett. 86 1769Google Scholar

    [14]

    Yin L, Albright B J, Hegelich B M 2006 Laser Part. Beams 24 291Google Scholar

    [15]

    Yin L, Albright B J, Hegelich B M, Bowers K J, Flippo K A, Kwan T J T, Fernández J C 2007 Phys. Plasmas 14 056706Google Scholar

    [16]

    Silva L O, Marti M, Davies J R, Fonseca R A, Ren C, Tsung F S, Mori W B 2004 Phys. Rev. Lett. 92 015002Google Scholar

    [17]

    Chen M, Sheng Z M, Dong Q L, He M, Li Y T, Bari M A, Zhang J 2007 Phys. Plasmas 14 053102Google Scholar

    [18]

    Simmons J F L, Mclnnes C R 1993 Am. J. Phys. 61 205Google Scholar

    [19]

    Macchi A, Veghini S, Pegoraro F 2009 Phys. Rev. Lett. 103 085003Google Scholar

    [20]

    Robinson A P L, Gibbon P, Zepf M, Kar S, Evans R G, Bellei C 2009 Plasma Phys. Controlled Fusion 51 024004Google Scholar

    [21]

    Robinson A P L, Kwon D H, Lancaster K 2009 Plasma. Phys. Controlled Fusion 51 095006Google Scholar

    [22]

    Yan X Q, Lin C, Sheng Z M, Guo Z Y, Liu B C, Lu Y R, Fang Y R, Fang J X 2008 Phys. Rev. Lett. 100 135003Google Scholar

    [23]

    Kar S, Kakolee K F, Qiao B, et al. 2012 Phys. Rev. Lett. 109 185006Google Scholar

    [24]

    Nishiuchi M, Dover N P, Hata M, Sakaki H, Kondo K, Lowe H F, Miyahara T, Kiriyama H, Koga J K, Iwata N, Alkhimova M A, Pirozhkov A S, Faenov A Y, Pikuz T A, Sagisaka A, Watanabe Y, Kando M, Kondo K, Ditter E J, Ettlinger O C, Hicks G S, Najmudin Z, Ziegler T, Zeil K, Schramm U, Sentoku Y 2020 Phys. Rev. Res. 2 033081Google Scholar

    [25]

    LeCun Y, Bengio, Hinton G 2015 Nature 521 436Google Scholar

    [26]

    Abadi M, Agarwal A, Barham P, et al. 2016 arXiv: 1603.04467 [cs.DC

    [27]

    Degrave J, Felici F, Buchli J, et al. 2022 Nature 602 414Google Scholar

    [28]

    Nakhleh J B, Fernadez G M G, Grosskopf M J, Wilson B M, Kline J, Srinivasan G 2021 IEEE Trans. Plasma Sci. 49 2238Google Scholar

    [29]

    Volkova T M, Nerush E N, Kostyukov I Y 2021 Quantum Electron. 51 854Google Scholar

    [30]

    Miyatake T, Shiokawa K, Sakaki H, Dover N P, Nishiuchi M, Lowe H F, Kondo K, Kon A, Kando M, Kondo K, Watanabe Y 2021 Nucl. Instrum. Methods in Phys. Res. Sect. A 999 165227Google Scholar

    [31]

    Emma C, Edelen A, Hogan M J, Shea B O, White G, Yakimenko V 2018 Phys. Rev. Accel. Beams 21 112802Google Scholar

    [32]

    Djordjević B Z, Kemp A J, J Kim 2021 Phys. Plasmas 28 043105Google Scholar

    [33]

    Djordjević B Z, Kemp A J, J Kim Ludwig J, Simpson R A, Wilks S C, Ma T, Mariscal D A 2021 Plasma Phys. Controlled Fusion 63 094005Google Scholar

    [34]

    Qiao B, Zepf M, Borghesi M, Dromey B, Geissler M, Karmakar A, Gibbon P 2010 Phys. Rev. Lett. 105 155002Google Scholar

    [35]

    Arber T D, Bennett K, Brady C S, Lawrence D A, Ramsay M G, Sircombe N J, Gillies P, Evans R G, Schmitz H, Bell A R, Ridgers C P 2015 Plasma Phys. Controlled Fusion 57 113001Google Scholar

    [36]

    Zhou Z H, Wu J X, Tang W 2002 Artif. Intell. 137 239Google Scholar

    [37]

    Robinson A P L 2011 Phys. Plasmas 18 056701Google Scholar

    [38]

    Weng S M, Murakami M, Mulser P, Sheng Z M 2012 New J. Phys. 14 063026Google Scholar

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计量
  • 文章访问数:  3335
  • PDF下载量:  89
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-04-29
  • 修回日期:  2023-05-31
  • 上网日期:  2023-07-06
  • 刊出日期:  2023-09-20

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