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Graphene nanomesh (GNM) is a single-layer graphene material that has a periodic distribution of nanoscale pores. GNM shows great potential applications in various fields such as thermoelectric energy conversion, energy storage, and field-effect transistors. In this study we utilize non-equilibrium molecular dynamics and lattice dynamics method to investigate the thermal transport mechanism of GNM. The thermal conductivity of GNM is mainly affected by the number of nanoscale pores and their horizontal and vertical spacing. Our study finds that as the number of nanoscale pores increases, the thermal conductivity of GNM decreases significantly. Additionally, the increase of the number of nanoscale pores causes phonon branch to be folded and confined, which results in a flatter dispersion curve, wider bandgap, and slower phonon group velocity. Moreover, the horizontal and vertical spacing of the nanoscale pores jointly affect the thermal transport process of GNM. When the horizontal spacing is small, the thermal conductivity of GNM decreases monotonically with the increase of vertical spacing, and increases monotonically with an increase of horizontal spacing. However, as the horizontal spacing increases, the interference effect caused jointly by phonon reflection and superposition leads to significant fluctuations in thermal conductivity. The analysis of the spectral heat flow, density of states, participation rate, and group velocity of GNM indicate that the variation in vertical spacing leads to different phonon contributions to heat flow, resulting in fluctuations in the thermal conductivity of GNM. These findings could serve as a reference for controlling the thermal transport of graphene nanomesh, and are of great significance in regulating the thermal conductivity and designing nanoscale pores in GNM.
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Keywords:
- graphene nanomesh /
- non-equilibrium molecular dynamics /
- heat transport /
- interference effect
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图 1 石墨烯纳米网的模型结构示意图
Figure 1. Schematic diagram of the model structure of GNM.
图 2 NEMD模拟计算导热系数的原理图
Figure 2. Schematic diagram of thermal conductivity calculated by NEMD simulation.
图 3 GNM的温度分布
Figure 3. Temperature distribution of GNM.
图 4 GNM的能量分布
Figure 4. Energy distribution of GNM.
图 5 几何参数R对GNM的导热系数的影响
Figure 5. Effect of the geometric parameter R on the thermal conductivity of GNM.
图 6 几何参数R对GNM色散曲线影响
Figure 6. Effect of geometric parameter R on dispersion curve of GNM.
图 7 几何参数R对GNM群速度影响
Figure 7. Effect of geometric parameter R on phonon group velocities of GNM.
图 8 几何参数Wx和 Wy对GNM导热系数的影响
Figure 8. Effects of geometric parameter Wx and Wy on thermal conductivity of GNM.
图 9 几何参数Wx和Wy对GNM色散曲线的影响
Figure 9. Effects of geometric parameter Wx and Wy on dispersion curve of GNM.
图 10 几何参数Wx和Wy对GNM谱热流的影响
Figure 10. Effects of geometric parameter Wx and Wy on spectral heat flux of GNM.
图 11 几何参数Wx和 Wy对GNM声子群速度的影响
Figure 11. Effects of geometric parameter Wx and Wy on phonon group velocities of GNM.
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[1] Harzheim A, Koenemann F, Gotsmann B, van der Zant H, Gehring P 2020 Adv. Funct. Mater. 30 2000574
Google Scholar
[2] Gayner C, Sharma R, Malkik I, et al. 2022 Nano Energy 94 106943
Google Scholar
[3] Mukherjee D, Das P, Kundu S, Mandal B 2022 Chemosphere 300 134432
Google Scholar
[4] Zang W, Liu Z, Kulkarni G S, Zhu H 2021 Nano Lett. 21 10301
Google Scholar
[5] Ruse E, Larboni M, Lavi A, Pyrikov M, Leibovitch Y, Ohayon-Lavi A, Vradman L, Regev O 2021 Carbon 176 168
Google Scholar
[6] Lin Y C, Mutlu Z, Borin Barin G, et al. 2023 Carbon 205 519
Google Scholar
[7] Ruan X L, Feng T L 2016 Carbon 101 107
Google Scholar
[8] Oh J, Yoo H, Choi J, Kim J Y, Lee D S, Kim M J, Lee J C, Kim W N, Grossman J C, Park J C 2017 Nano Energy 35 26
Google Scholar
[9] Dollfus P, Viet H N, Saint-Martin J 2015 J. Phys. Condens. Matter 27 133204
Google Scholar
[10] Evans W J, Hu L, Keblinski P 2010 Appl. Phys. Lett. 96 203112
Google Scholar
[11] Xie G F, Shen Y L 2015 Phys. Chem. Chem. Phys. 17 8822
Google Scholar
[12] Wang Y, Qiu B, Ruan X 2012 Appl. Phys. Lett. 101 013101
Google Scholar
[13] Polanco C A, Lindsay L 2018 Phys. Rev. B 97 014303
Google Scholar
[14] Hu S Q, Chen J, Yang N, Li B W 2017 Carbon 116 139
Google Scholar
[15] Feng T L, Ruan X L, Ye Z Q, Cao B Y 2015 Phys. Rev. B 91 224301
Google Scholar
[16] Li M, Deng T, Zheng B 2019 J. Nanomater. 9 347
Google Scholar
[17] Felix I M, Pereira L F C 2020 Carbon 160 335
Google Scholar
[18] Maldovan M 2015 Nat. Mater. 14 667
Google Scholar
[19] Wang Z Y, Hao Z Y, Yu Y Y, et al. 2021 Adv. Mater. 33 2170129
Google Scholar
[20] Hu S, Zhang Z, Jiang P 2018 J. Phys. Chem. Lett. 9 3959
Google Scholar
[21] Liu Y, Ren W, An M, Dong L, Gao L, Shai X, Wei T, Nie L, Hu S, Zeng C 2022 Front. Mater. 9 913764
Google Scholar
[22] Lee J, Lee W, Wehmeyer G 2017 Nat. Commun. 8 14054
Google Scholar
[23] Cui L, Wei G S, Li Z, Du X Z 2021 Int. J. Heat Mass Tran. 165 120685
Google Scholar
[24] Hu S, Zhang Z, Jiang P 2018 J. Phys. Chem. Lett 9 3959
Google Scholar
[25] Yang L, Chen J, Yang N, Li B W 2015 Int. J. Heat Mass Tran. 91 428
Google Scholar
[26] Wang X Y, Wang M, Hong Y, Wang Z R, Zhang J C 2017 Phys. Chem. Chem. Phys. 19 24240
Google Scholar
[27] Plimpton S 1995 J. Comput. Phys. 117 1
Google Scholar
[28] Kinaci A, Haskins J B, Sevik C 2012 Phys. Rev. B 86 115410
Google Scholar
[29] Song J R, Xu Z H, He X D, Bai Y J, Miao L L, Cai C C, Wang R G 2019 Phys. Chem. Chem. Phys. 21 12977
Google Scholar
[30] 徐贤达, 赵磊, 孙伟峰 2020 69 047101
Google Scholar
Xu X D, Zhao L, Sun W F 2020 Acta. Phys. Sin. 69 047101
Google Scholar
[31] Wei N, Chen Y, Cai K, Zhao J H, Wang H Q, Zheng J C 2016 Carbon 104 203
Google Scholar
[32] 汤建, 刘爱萍, 李培刚, 沈静琴, 唐为华 2014 63 107801
Google Scholar
Tang J, Liu A P, Li P G, Shen J Q, Tang H W 2014 Acta. Phys. Sin. 63 107801
Google Scholar
[33] Yarifard M, Davoodi J, Rafii-tabar H 2017 Comp. Mater. Sci. 126 29
Google Scholar
[34] Anufriev R, Maire J, Nomura M 2021 APL Mater. 9 070701
Google Scholar
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