Anisotropic thermal conductivity and associated heat transport mechanism in roll-to-roll graphene reinforced copper matrix composites
Source abstract
Abstract
Abstract Owing to the high strength of copper (Cu) and high in-plane thermal conductivity (Kr) of graphene (Gr), Gr/Cu composites are increasingly demanded as the advanced thermal management materials to ensure the heat dissipation. The heat transport performance is primarily influenced by two aspects: (1) Intrinsic parameters of Gr, including its crystallinity, layer number (N), coverage and spatial distribution and (2) Gr/Cu interface related properties, such as interface bonding, residual strain and defects near the interface. In this work, by combining roll-to-roll (R2R) chemical vapor deposition (CVD) and subsequent hot isostatic pressing (HIP) techniques, highly paralleled Gr reinforced Cu matrix composites with controllable N were fabricated. Experimental results show that ~5–6L Gr/Cu composites manifest the highest degree of anisotropy, including (1) the highest Kr (394 ± 5 W/mK, ~22% higher than pure Cu counterpart), and (2) the lowest through-plane thermal conductivity (Kz) (257 ± 4 W/mK, ~25% lower than pure Cu counterpart). When N varies from 1 to 5, the continuously increased Kr and decreased Kz are majority influenced by intrinsic properties of Gr, which is also validated by multiscale simulations and time-domain thermoreflectance analysis. When N increases to ~10, both Kr and Kz exhibit opposite trend, that may attribute to the reduced Gr coverage and large volume fraction of amorphous carbon. Moreover, the residual strain and defects at the interface could lower both Kr and Kz as well. This study suggests that advanced synthesis of high-crystallinity thick Gr may be promising to obtain superb Kr in Cu matrix composites.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Aluminum Alloys Composites Properties · Thermal properties of materials · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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