4.7 Article

Response-Surface-Methodology-Based Increasing of the Isotropic Thermal Conductivity of Polyethylene Composites Containing Multiple Fillers

Journal

POLYMERS
Volume 15, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/polym15010039

Keywords

thermal conductivity; carbon nanotubes; response surface methodology; hybrid filler composite; polymer composites; aluminum oxide; graphite

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This study investigates 15 hybrid filler composites containing aluminum oxide, graphite, expanded graphite, carbon nanotubes, or a combination of these fillers, using extrusion-compression processing. The experimental density of the specimens is lower than the theoretical density, especially for composites with high filler contents, due to the presence of air inclusions. The addition of fillers increases both the melt viscosity and the thermal conductivity.
To optimize the thermal conductivity of high-density polyethylene, 15 hybrid filler composites containing either aluminum oxide, graphite, expanded graphite, carbon nanotubes or a combination of the former, have been studied using an extrusion-compression processing tandem. The experimental density of the cube-shaped specimens is substantially lower than the theoretical density calculated by the linear mixing rule, mainly for the composites with high filler contents. The morphology of the composites, as studied by scanning electron microscopy (SEM), highlighted a good dispersion quality and random orientation of the fillers in the test specimens but also revealed air inclusions in the composites, explaining the density results. It is shown that the addition of filler(s) increases both the melt viscosity (up to ca. 270%) and the thermal conductivity (up to ca. 1000%). Hence, a very strong increase of TC can be practically hampered by a too high viscosity to enable processing. Supported by ANOVA analysis, the application of response surface methodology (RSM), assuming a perfect compression, indicates that all fillers have a significant effect on the thermal conductivity and synergistic effects can be achieved. The regression model obtained can adequately predict the thermal conductivity of composites of various compositions, as already confirmed based on three validation experiments in the present work.

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