Offset supper-cell model of polymer composites with oriented anisotropic fillers for thermal conductivity prediction considering shape factor

Thermal conductivity of polymer composites with oriented anisotropic fillers is commonly predicted based on the simplified cuboid unit-cell models. However, the models are too simple to reveal the real thermal conductivity of composites without absolutely aligned fillers. Here, a super-cell model with off-set fillers based on a hexagonal prism primitive-cell after considering shape factor [Chem. Eng. J. 2022, 441, 136104.] is established to predict thermal conductivity as close to reality as possible for polymer composites with oriented fibrous, wirelike, and flake fillers. The impact of shape factor, fillers' intrinsic thermal conductivity, size and loading, polymer's intrinsic thermal conductivity, and thermal interface resistance between polymer and fillers, on the anisotropic thermal conductivity of polymer composites are systematically evaluated by performing finite element numerical simulation. The model is applied to predict the anisotropic thermal conductivity of polymer based highly through-plane thermally conductive thermal interface materials, and highly in-plane thermally conductive thermal diffusion materials, which are in excellent agreement with the experimental ones. This work not only provides a practical means to predict the thermal conductivity of the oriented anisotropic fillers /polymer composites, but also has the guiding significance for designing thermal management materials. & COPY; 2023 Elsevier Ltd. All rights reserved.

Automated summary

Thermal conductivity of polymer composites with oriented anisotropic fillers is accurately predicted using a super-cell model with off-set fillers based on a hexagonal prism primitive-cell. The model systematically evaluates the impact of various factors on the anisotropic thermal conductivity of polymer composites and shows excellent agreement with experimental results. This work provides a practical means to predict thermal conductivity and has guiding significance for designing thermal management materials.