SynopsisA new thermal conduction model is proposed for filled polymer with particles, and predicted values by the new model are compared with experimental data. The model is fundamentally based on a generalization of parallel and series conduction models of composite, and further modified in taking into account that a random dispersion system is isotropic in thermal conduction. The following equation is derived from the new model; log A = V . C, . log A, + (1 -V) . log(C, . Al). Therefore, when thermal conductivities of polymer and particles (Al, A,) are known, thermal conductivity of the filled polymer (A) can be estimated by the equation, with any volume content of particles (V). The new model was proved by experimental data for filled polyethylene, polystyrene and polyamide with graphite, copper, or Al,O,.
SynopsisThermal and electric conductivities of polyethylene and poly(viny1 chloride) filled with carbon materials over a wide range are measured in order to study the effect of formed conductive particle chains on thermal conductivities of the composites. With increase of content of carbon particles, the amount of formed conductive chains exponentially increases and the conductive chains tend largely to increase thermal conductivity of a composite. Some models proposed to predict thermal conductivity of a composite in a two-phase system could not be applied to the system with high volume content of particles. In this study, a new thermal conduction model is proposed to correctly predict thermal conductivity of a composite which contains various amounts of particles ranging from a small content, to the region in which conductive chains largely effect a thermal conductivity of a composite. Thermal conductivity of a polymer filled with high volume content of particles largely decreased with a rise in temperature. This phenomenon can be referred to as a PTC phenomenon in thermal resistance.
SYNOPSISA prediction equation for thermal conductivity of polymer composites reported in our previous papers has been revised in terms of two view points: ( 1) estimation of thermal conductivity of a composite using an idea of reduced thermal conductivity; and ( 2 ) the effect of ease in forming conductive filler chains on thermal conductivity is related to the CVF value in electric conductivity of the composite. The new equation was confirmed to be adaptable to thermal conductivities of varieties of polymer composite systems filled with spherical or irregular fillers. The equation was also considered to explain thermal conductivity of polymer composites filled with fibers. Further, it was found that thermal conductivities of fiber composites can be estimated by introducing a factor of the CVF value or aspect ratio ( L I D ) into the new equation.
SYNOPSISThermal conductivity of polyethylene composites, filled with randolr.ly dispersed and disoriented (oriented at random) carbon fibers with various aspect ratios, were investigated. Orientation of fibers was quantitatively evaluated by Hermans' parameter. In specimens of isotropic composites, i.e., filled with randomly dispersed and disoriented fibers, thermal conductivity increased with an increase in the fiber length. The resub; is discussed in comparison with electric conductivity of the composites and explained by the contact probability of filled fibers. Further, it was confirmed that our model previously proposed could be adopted to predict thermal conductivity of the isotropic composite fillod with carbon fibers. Also, the effect of fiber length of the C2 parameter included in the m3del is discussed and C2 was found to have a linear relation with the aspect ratio of fibers #it a sufficiently large value. In this study, a shape factor of a filler (aspect ratio) could be directly introduced into the equation, which was shown in our previous paper.
SYNOPSISWe measured thermal conductivities as well as electric conductivities of some composites in several types of dispersion systems. The dispersion state, that is, the ease in forming conductive chains in these composites, was estimated by the characteristic electric conductivities and compared with the thermal conductivities. Thus, it became clear that thermal conductivity of a composite was significantly affected by the dispersion state in the composite. Further, it was confirmed that the predictive model proposed in the previous report was adaptable to the thermal conductivity of the composites in several types of dispersion systems. It was made clear that the dispersion state of a composite affected the values C1 and C2 in the previous model.
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