Abstract
We have performed 3D simulations of complex effective permittivity and permeability for random binary mixtures of cubic particles below the percolation threshold. We compare two topological classes that correspond to different spatial particle arrangements: cermet topology and aggregate topology. At a low filling factor off=10%, where most particles are surrounded by matrix material, the respective effective material parameters are indistinguishable. At higher concentrations, a systematic difference emerges: cermet topology is characterized by lower effective permittivity and permeability values. A distinction between topological classes might thus be a useful concept for the analysis of real systems, especially in cases where no exact effective-medium model is available.
Highlights
Composite materials have a lot of technical applications, and especially magnetic nanocomposites have been studied recently [1,2,3]
We focus on composite materials at filling factors below the percolation threshold, where in both cases the dispersed phase does not form a continuous network
We have performed 3D simulations of loss-free and lossy random binary mixtures on a cubic grid in order to study the transition from aggregate to cermet topology
Summary
Composite materials have a lot of technical applications, and especially magnetic nanocomposites have been studied recently [1,2,3]. In a recent study, experimental data for granular material (pulverized samples, i.e., air-particle mixtures) was compared with 6 effective media formulas, 3 of them belonging to the cermet topology, and 3 to the aggregate topology, and the above Looyenga-equation performed best [24]. We focus on composite materials at filling factors below the percolation threshold, where in both cases the dispersed phase does not form a continuous network We would like to know whether, despite this similarity, the effective properties differ considerably In this case the concept of topological classes might help to discriminate between effective medium models and to select the best approximation for the analysis of experimental data
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