Thermal conductivity of polydisperse hexagonal BN/polyimide composites: Iterative EMT model and machine learning based on first principles investigation

Dongliang Ding, Minhao Zou, Xu Wang, Guangzhao Qin, Shiyu Zhang, Siew Yin Chan, Qingyong Meng, Zhenguo Liu, Qiuyu Zhang, Yanhui Chen

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

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.

Original languageEnglish
Article number135438
JournalChemical Engineering Journal
Volume437
DOIs
StatePublished - 1 Jun 2022

Keywords

  • First principles
  • Hexagonal boron nitride
  • Iterative EMT model
  • Machine learning
  • Polymer composite
  • Thermal conductivity

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