热导率
材料科学
声子
堆积
石墨烯
凝聚态物理
散射
声子散射
双层
双层石墨烯
玻尔兹曼方程
热电材料
热电效应
图层(电子)
复合材料
纳米技术
光学
热力学
化学
核磁共振
膜
物理
生物化学
作者
Jun-Nan Liang,Hua Tong,Yu‐Jia Zeng,Wu‐Xing Zhou
标识
DOI:10.1088/1361-648x/ad6050
摘要
Abstract Manipulating thermal conductivity ( κ ) plays vital role in high-performance thermoelectric conversion, thermal insulation and thermal management devices. In this work, we using the machine learning-based interatomic potential and the phonon Boltzmann transport equation to systematically investigate layer thickness dependent κ of fluorinated graphene (FG). We show that the lattice κ of FG can be significantly decreased with Bernal bilayer stacking. Surprisingly, the further increasing of stacking layer can no longer affect the κ , however, the κ is increased in the bulk configuration. The variation of κ can be attributed to the crystal symmetry change from P-3m1 (164) at single layer to P3m1 (156) at multilayer. The decreasing crystal symmetry from single layer to bilayer resulting stronger phonon scattering and thus leading a lower κ . Moreover, we also show that the contribution of acoustic mode to κ decreases with the increase of layers, while the contribution of optical mode to κ is increased with increasing layers. These results provide a further understanding for the phonon scattering mechanism of layer thickness dependent κ .
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