journal article·2026-03

3D hybrid skeleton of graphene nanoplatelet/boron nitride for enhancing thermal conductivity of flexible epoxy composites

Xiaochong Guan, Kai Liu, Fei Xie, Junting Li, Hui Yang, Carla Bittencourt, Wenjiang Li

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Abstract

The progressive miniaturization and integration within the electronics industry demand advanced thermal management strategies for electronic devices. Two-dimensional materials, notably single-layer hexagonal boron nitride nanosheets (BNNS) and graphene nanoplatelets (GNPs), are recognized for their exceptional thermal properties, with intrinsic thermal conductivity of approximately 400 W·m−1·K−1 for BNNS and exceeding 5000 W·m−1·K−1 for graphene. Here, a biomimetic honeycomb architecture is reported, fabricated via physical foaming and freeze-drying with surface-hydroxylated GNPs (m-GNPs) and dopamine-functionalized h-BN (m-BN) as functional building blocks. The lightweight 3D GNP-BN hybrid skeleton supports 3000 × its own weight and enables continuous thermal conduction pathways in an epoxy matrix (EP). The 15 wt% GNP-BN/EP composite exhibits a through-plane thermal conductivity of 1.21 W·m−1·K−1, which is 537 % higher than that of pure EP, while maintaining excellent electrical insulation (>108 Ω·cm). This arises from the hierarchical honeycomb architecture: strong hydrogen bond interfaces between m-GNPs and m-BN, reducing interfacial thermal resistance (ITR), whereas the wide bandgap of h-BN (∼6 eV) effectively hindering electron transport despite GNP's conductivity. The electrical insulation of h-BN and ultra-high TC of GNP are synergistically integrated, along with a lightweight honeycomb structure, endowing this composite material with outstanding performance for heat dissipation in miniaturized electronics.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Thermal properties of materials · Thermal Expansion and Ionic Conductivity · Aerogels and thermal insulation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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