Abstract
AbstractNanodielectric 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.
Highlights
Dielectric materials are widely used in mobile electronics, electrical transmission, and pulsed power applications [1]
One example is a new capacitor made from nanodielectrics that can store a large amount of energy and discharge it quickly with highenergy density [2]
Once a microstructure is characterized by descriptors, a typical 2D or 3D reconstruction follows a sequential procedure [12]: (1) dispersion reconstruction: center positions of clusters are adjusted to match dispersion descriptors, e.g., the nearest center distances, using optimization algorithms such as simulated annealing (SA), (2) geometry reconstruction: the geometry is randomly generated for each cluster based on the geometry descriptors and geometry profiles are assigned to each cluster, (3) composition adjustment: the edge of clusters are modified to satisfy the composition descriptors such as the volume fraction
Summary
Dielectric materials are widely used in mobile electronics, electrical transmission, and pulsed power applications [1]. There is an increasing demand for new nanodielectric materials, consisting of nanoparticle-filled polymers, for creating future electrical transmission and storage devices. One example is a new capacitor made from nanodielectrics that can store a large amount of energy and discharge it quickly with highenergy density [2]. The design of nanodielectrics is often multi-objective, for example, a tradeoff between dielectric constant and breakdown strength of dielectric materials. To achieve design requirements under different application scenarios, a systematic computational design approach is needed to quickly explore the microstructure design space of nanodielectrics. We are developing characterization, reconstruction, and key microstructure feature identification techniques to support the computational design of nanodielectric systems
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