Computational Materials Science2022,Vol.2066.DOI:10.1016/j.commatsci.2022.111276

Ultrahard BC5 - An efficient nanoscale heat conductor through dominant contribution of optical phonons

Muthaiah, Rajmohan Garg, Jivtesh Arafin, Shamsul
Computational Materials Science2022,Vol.2066.DOI:10.1016/j.commatsci.2022.111276

Ultrahard BC5 - An efficient nanoscale heat conductor through dominant contribution of optical phonons

Muthaiah, Rajmohan 1Garg, Jivtesh 1Arafin, Shamsul2
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作者信息

  • 1. Univ Oklahoma
  • 2. Ohio State Univ
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Abstract

In this work, we study lattice thermal conductivity (k) of BC5, an diamondlike ultra-hard material, using first-principles computations and analyze the effect of both isotopic disorder as well as length scale dependence. k of isotopically pure BC5 is computed to be 169 Wm(-1) K-1 (along a-axis) at 300 K; this high k is found to be due to the high frequencies and phonon group velocities of both acoustic and optical phonons owing to the light atomic mass of Carbon (C) and Boron (B) atoms and strong C-C and B-C bonds. We also observe a dominance of optical phonons (~54%) over acoustic phonons in heat conduction at higher temperatures (~500 K). This unusually high contribution of optical phonons is found to be due to a unique effect in BC5 related to a weaker temperature dependence of optical phonon scattering rates relative to acoustic phonons. The effect is explained in terms of high frequencies of optical phonons causing decay into other high frequency phonons, where low phonon populations cause the decay term to become insensitive to temperature. The effect further leads to high nanoscale thermal conductivity of 77 Wm(-1) K-1 at 100 nm length scale due to optical phonon meanfreepaths being in nanometer regime. These results provide avenues for application of BC5 in nanoscale thermal management.

Key words

Boron/Carbon/Thermal conductivity/Optical phonons/BC5/Ultra-hard materials/Thermal management/Phonons/Nanoelectronics/LATTICE THERMAL-CONDUCTIVITY

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出版年

2022
Computational Materials Science

Computational Materials Science

EISCI
ISSN:0927-0256
被引量1
参考文献量29
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