Nanoscale thermal transport in epoxy matrix composite materials reinforced with carbon nanotubes and graphene nanoplatelets
Source abstract
Abstract
The technology related to hybrid filler-reinforced polymer matrix composite materials is growing at a high rate with increasing interest and usage. This study presents a way of increasing the thermal conductivity of polymer matrix composites by the use of a hybrid filler consisting of carbon nanotubes and graphene nanoplatelets and preliminarily clarifies the mechanisms that lead to the synergistic reinforcement of composite thermal conductivity at the nanoscale. The focus of this study was upon the fundamental relationships between nanometer-scale reinforcement structures and macroscopic composite thermal properties. The benefits and limitations associated with the incorporation of the hybrid filler into an epoxy matrix were evaluated. The results indicated that there exists an evident synergistic reinforcing effect between carbon nanotubes and graphene nanoplatelets on composite thermal conductivity. A significant increase has been gained in composite thermal conductivity, but low loading is required in order to exploit the benefits derived from the unique structure of the hybrid filler. Filler loading must be controlled very accurately in order to ensure that a critical threshold is not reached, beyond which there is a decrease in thermal conductivity, compared to that of graphene nanoplatelet-reinforced composites. The synergistic reinforcing benefits to composite thermal conductivity and is derived from effective conducting pathways formed between carbon nanotubes and graphene nanoplatelets within polymer matrices. The results can offer practical guidance on how to improve thermal transport properties for polymer matrix composite materials.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Thermal properties of materials · Graphene research and applications · Carbon Nanotubes in Composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (86)
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