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首页> 外文期刊>International Journal of Heat and Mass Transfer >Experimental and atomistic insight on the thermal transport properties of h-BN/high density polyethylene nanocomposite
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Experimental and atomistic insight on the thermal transport properties of h-BN/high density polyethylene nanocomposite

机译：H-BN /高密度聚乙烯纳米复合材料热传输性能的实验和原子洞察

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The article investigates the effect of hexagonal boron nitride nanosheet (BNNS) on the thermal transport properties of high density polyethylene (PE) based nanocomposites. An experimental approach was adopted to investigate the effect of the concentration of boron nitride nanoplatelets (BNNP) on thermal conductivity, whereas an atomistic approach was utilized to study the governing mechanism for in-terfacial thermal conductance (1TC) in BNNS/PE nanocomposites. It was reported from experiments performed using a hot disc analyzer that PE's thermal conductivity improves up to 29.6% with 7wt.% of BNNP. Large dimension nanosheet grown using chemical vapor deposition based techniques are polycrystalline. In continuation with experimental characterization, ITC in mono-crystal and bi-crystal BNNS reinforced PE nanocomposites was predicted using atomistic simulations. It was concluded from the reverse non equilibrium molecular dynamics based simulations that bi-crystalline BNNS with higher mis-orientation angle have superior reinforcing capabilities to improve thermal transport properties. Based on structures of dislocations at the grain boundaries (GB), ITC values were predicted. Depending on the two crystals' alignment, GB configurations can be either nitrogen-rich (N-N) or boron-rich (B-B). It was predicted from the simulations that higher energy nitrogen-rich GBs are better aligned with the PE chains that enhance ITC values.
机译：本文研究了六边形氮化硼纳米片（BNN）对高密度聚乙烯（PE）基纳米复合材料的热传输性能的影响。采用一种实验方法来研究氮化硼纳米片（BNNP）浓度对导热率的影响，而使用原子方法研究了BNNS / PE纳米复合材料中的特异性热传导（1Tc）的控制机制。从使用热盘分析仪进行的实验报告，PE的导热率可提高高达29.6％，7wt％。占BNNP的％。使用基于化学气相沉积的技术生长的大维纳米片是多晶的。在实验表征的延续中，使用原子模拟预测ITC在单晶和双晶BNNS增强PE纳米复合材料中。从基于逆转的非平衡分子动力学的模拟结论，具有较高的错误取向角的双晶BNN具有优异的增强能力以改善热传输性能。基于晶界（GB）的脱位结构，预测ITC值。取决于两个晶体的对准，GB配置可以是富含氮（N-N）或富含硼（B-B）。从模拟预测，较高的能量氮的GBS与增强ITC值的PE链更好地对齐。

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