Quantum mechanics

Materials science

Boron nitride

Graphene

Layer (electronics)

Thermal conductivity

Thermal resistance

Polyimide

Interfacial thermal resistance

Hexagonal crystal system

Thermodynamics

Physics

Nanotechnology

Hexagonal boron nitride

Thermal

Conductivity

Composite material

Crystallography

Chemistry

Meteorology

Thermal conduction

Hot pressing

Exfoliation joint

Chemical Engineering Journal

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.

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