Quasi-honeycomb graphene architectures enabling geometry-adaptive thermal regulation for high-density electronics

Abstract

The relentless pursuit of higher power density and miniaturization of modern electronics demand have exposed the limitations of conventional passive cooling systems. This study presents an innovative quasi-honeycomb architecture composed of vertically aligned and interconnected graphene nanosheet arrays (VIG) synthesized via plasma-enhanced chemical vapor deposition (PECVD) on copper substrates, achieving dual-mode heat dissipation through synergistic radiative and convective enhancement. The engineered graphene–copper hybrid interface demonstrates exceptional thermal performance, achieving an enhanced heat transfer coefficient of 35.6 W m−2 K−1 through synergistic optimization of infrared emissivity and specific surface area. Systematic evaluations reveal a 21.6% improvement in cooling efficiency compared to pristine copper substrates. Practical implementation as a conformal passive heat sink effectively suppresses temperature rise in high-power LED arrays (ΔT reduction: 28.1 °C at 2.7 W) and lithium-ion battery modules (thermal mitigation: 7.0 °C under 3C discharge). Notably, the ultrathin (≈2.5 μm) and ultralight (≈0.073 mg cm−2) structure enables spontaneous self-assembly on sub-100 μm metallic foils, providing geometrically adaptive heat dissipation for irregular surfaces. This work establishes a universal paradigm for developing conformal thermal management solutions compatible with geometrically complex surfaces in next-generation compact electronics.

Graphical abstract: Quasi-honeycomb graphene architectures enabling geometry-adaptive thermal regulation for high-density electronics

Supplementary files

Article information

Article type
Paper
Submitted
12 Sep 2025
Accepted
01 Nov 2025
First published
10 Dec 2025

Nanoscale, 2026, Advance Article

Quasi-honeycomb graphene architectures enabling geometry-adaptive thermal regulation for high-density electronics

Q. Zhao, Y. Wang, X. Zheng, X. Cai, J. Li, Y. Zhang and X. Xia, Nanoscale, 2026, Advance Article , DOI: 10.1039/D5NR03864B

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