作者：Dong-Liang Ding, Min-Hao Zou, Xu Wang, Guang-Zhao Qin, Shi-Yu Zhang, Siew Yin Chan, Qing-Yong M
关键字：Hexagonal boron nitride, Polymer composite, Thermal conductivity, First principles, Iterative EMT model, Machine learning.
论文来源：期刊
具体来源：Chemical Engineering Journal 2022, 437, 135438.
发表时间：2022年
  Demand for thermal management materials (TMMs) with efficient in-plane heat dissipation has grown with the advancement of intelligent wireless communication equipment. Herein, polydisperse hexagonal boron nitride (ae-BN) in the range of micrometers to nanometers via aqueous-assisted exfoliation. First principles investigation revealed that ae-BN possess high intrinsic thermal conductivity. A series of ae-BN/PI composites were then fabricated through facile methods: vacuum-filtration and hot-pressing. The ae-BN/PI composites with 30 vol% ae-BN loading exhibited superior in-plane thermal conductivity (6.57 W/(m·K) compared to pristine h-BN/PI composite (3.92 W/(m·K)). SEM images and structural modeling of ae-BN/PI composites revealed that thermal conduction pathways constructed in the composites continuously increased with ae-BN content, attributing to an increased contact probability in composites with higher content of ae-BN. Reduction in thermal boundary resistance of ae-BN/PI composites was proved by our iterative EMT model. In-plane thermal conductivity of ae-BN/PI composites with different fillers’ contents at variable temperatures were predicted by machine learning technique, viz. artificial neural network (ANN) model. In brief, ae-BN/PI composites with high thermal conductivity, electrical insulation, thermal stability, and mechanical strength were successfully fabricated. The heat conduction mechanism of ae-BN/PI composites was investigated, serving as an important piece of puzzle for the advancement in TMMs of the advanced electronic devices.