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Nanodielectric materials, consisting of nanoparticle-filled polymers, have the potential to become the dielectrics of the future. Although computational design approaches have been proposed for optimizing microstructure, they need to be tailored to suit the special features of nanodielectrics such as low volume fraction, local aggregation, and irregularly shaped large clusters. Furthermore, key independent structural features need to be identified as design variables. To represent the microstructure in a physically meaningful way, we implement a descriptor-based characterization and reconstruction algorithm and propose a new decomposition and reassembly strategy to improve the reconstruction accuracy for microstructures with low volume fraction and uneven distribution of aggregates. In addition, a touching cell splitting algorithm is employed to handle irregularly shaped clusters. To identify key nanodielectric material design variables, we propose a Structural Equation Modeling approach to identify significant microstructure descriptors with the least dependency. The method addresses descriptor redundancy in the existing approach and provides insight into the underlying latent factors for categorizing microstructure. Four descriptors, i.e., volume fraction, cluster size, nearest neighbor distance, and cluster roundness, are identified as important based on the microstructure correlation functions (CF) derived from images. The sufficiency of these four key descriptors is validated through confirmation of the reconstructed images and simulated material properties of the epoxy-nanosilica system. Among the four key descriptors, volume fraction and cluster size are dominant in determining the dielectric constant and dielectric loss.

Commercially available TiO 2 and BaSO 4 nanoparticles were incorporated in polyamide 6 (PA 6) via twin screw extrusion. The primary particle size of these two nanoparticles was 15 nm and 20 nm, respectively. The compounds were manufactured via multiple extrusion and dilution processing steps. The dispersion of the nanoparticles in the matrix was investigated by scanning electron microscopy and image analysis. The mechanical properties were determined via tensile and bending tests. The toughness was investigated by the Charpy V-notch impact strength test. It was found that for TiO 2 fillers a threefold extrusion process is sufficient to realize a dispersion index of 94.4%. BaSO 4 fillers were hardly dispersible, ending up with a maximum dispersion index of 71%. Tensile and bending properties are maintained constant, or can be improved with the number of performed extrusion steps. Despite good deagglomeration, impact strength at low concentrations was not improved in case of TiO 2 , which is probably due to remaining microagglomerates.

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