Thermal conductivity of polymer composites with close-packed structure of nano and micro fillers

Sanada, Kazuaki; Tada, Yuuki; Shindo, Yasuhide

doi:10.1016/j.compositesa.2009.02.024

Cited by 171 publications

(75 citation statements)

References 13 publications

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“…With the increasing of filler content, the thermal conductivity of composites increased slowly until the filler fraction reached a percolation threshold where a rapid increase in conductivity started [8]. Fillers had to form a random close-packed structure to maximize a pathway or network for heat conduction through the polymer matrix [9,10]. In the past researches, the effects of type, shape and size of fillers and interface thermal resistance on thermal performance of composites have been fully addressed.…”

Section: Introductionmentioning

confidence: 98%

Enhanced thermal-conductive and anti-dripping properties of polyamide composites by 3D graphene structures at low filler content

Shao

Song

et al. 2016

Composites Part A: Applied Science and Manufacturing

120

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Section: Introductionmentioning

confidence: 98%

Enhanced thermal-conductive and anti-dripping properties of polyamide composites by 3D graphene structures at low filler content

Shao

Song

et al. 2016

Composites Part A: Applied Science and Manufacturing

120

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“…), a number of experimental attempts have been made. It was found that the thermal conductivity of polymer-based composites can be efficiently improved by introduction of conductive fillers, and can be influenced by the shape and content of the fillers in matrix [9][10][11][12]. At the same time, various semi-empirical or empirical models have been developed to qualitatively simulate the thermal conductivity of dual-phase polymer matrix composites [13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Wang

Cai

Pei

et al. 2011

Sci. China Phys. Mech. Astron.

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Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.

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“…Other researchers attempted to use carbon nanotubes as an additive for high thermal conductivity of the polymer composites because the fibrous form of nanotubes can allow heat transfer (or dissipation). 1,4,6,8,15 Among the ceramic fillers mentioned above, aluminum nitride (AlN) is a good filler for insulating resins due to the high thermal conductivity, low thermal expansion, and high electric resistivity. 16,17 On the other hand, the production of pure AlN nanofibers by chemical vapor decomposition (CVD) is difficult, and the final product is quite expensive.…”

Section: -9mentioning

confidence: 99%

Effects of Surface Nitrification on Thermal Conductivity of Modified Aluminum Oxide Nanofibers-Reinforced Epoxy Matrix Nanocomposites

Kim¹,

Bae²,

An³

et al. 2012

Bulletin of the Korean Chemical Society

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Aluminum oxide (Al 2 O 3 ) nanofibers were treated thermally under an ammonia (NH 3 ) gas stream balanced by nitrogen to form a thin aluminum nitride (AlN) layer on the nanofibers, resulting in the enhancement of thermal conductivity of Al 2 O 3 /epoxy nanocomposites. The micro-structural and morphological properties of the NH 3 -assisted thermally-treated Al 2 O 3 nanofibers were characterized by X-ray diffraction (XRD) and atomic force microscopy (AEM), respectively. The surface characteristics and pore structures were observed by X-ray photoelectron spectroscopy (XPS), Zeta-potential and N 2 /77 K isothermal adsorptions. From the results, the formation of AlN on Al 2 O 3 nanofibers was confirmed by XRD and XPS. The thermal conductivity (TC) of the modified Al 2 O 3 nanofibers/epoxy composites increased with increasing treated temperatures. On the other hand, the severely treated Al 2 O 3 /epoxy composites showed a decrease in TC, resulting from a decrease in the probability of heat-transfer networks between the filler and matrix in this system due to the aggregation of nanofiber fillers.

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Thermal conductivity of polymer composites with close-packed structure of nano and micro fillers

Cited by 171 publications

References 13 publications

Enhanced thermal-conductive and anti-dripping properties of polyamide composites by 3D graphene structures at low filler content

Enhanced thermal-conductive and anti-dripping properties of polyamide composites by 3D graphene structures at low filler content

Prediction of thermal conductivity of polymer-based composites by using support vector regression

Effects of Surface Nitrification on Thermal Conductivity of Modified Aluminum Oxide Nanofibers-Reinforced Epoxy Matrix Nanocomposites

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