Carbon black is the most frequently applied conductive additive in rubber and polymer composites. In this work, we show how a carbon black microstructure in a polymer matrix can be conclusively modeled based on carbon black aggregation as well as an agglomeration mechanism using a state-of-the-art mathematical model. This novel and flexible microstructural modeling method enables us to virtually investigate the morphology of conductive additives within a polymer matrix and can be adapted to many conductive polymer combinations used for different applications. Furthermore, we calculate the electrical conductivity of the composite using a finite volume-based as well as a discrete element-based simulation technique and validate the results with experimental data. Utilizing a novel discrete element method (DEM) modeling technique, we were able to improve calculation times by a factor of 12.2 compared to finite volume method (FVM) simulations while maintaining high accuracy. Using this approach, we are able to predict the required carbon black content and minimize the amount of additive to create a polymer composite with a designated target conductivity.

Commercial Co/WC/diamond composites are hard metals and very useful as a kind of tool material, for which both ductile and quasi-brittle behaviors are possible. This work experimentally investigates their damage evolution dependence on microstructural features. The current study investigates a different type of Co/WC-type tool material which contains 90 vol.% Co instead of the usual 50 vol.% Co. The material properties for such tool materials (>50 vol.% Co) is rarely published until now. Efforts made in the ML part contribute to the realization of autonomous processing procedures in big-data-driven science applied in materials science.

Commercial Co/WC/diamond composites are hard metals and very useful as tool materials. This work studies their damage evolution in dependence of microstructural features. With 90vol.% Co, our material is also a kind of tool material, which is much less studied than the same type of composites with more than 50vol.% WC. The mean size ratio of diamond:WC particles is no less than 10:1. Another special characteristic is that the material behavior can be both ductile and quasi-brittle depending on the portion and local morphology of the diamond. Measurements are performed to achieve real microstructures, the local hardness, the local strain field, the local damage propagation, as well as the macro stress flow behavior. For the finite element (FE) simulations, different methods and techniques are applied. Both real and Poisson Voronoi microstructures are used in 2D and 3D, where residual stresses are taken into account before loading. For submicro-sized WC particles, which are too fine distributed to be meshed with an acceptable number of elements, their strengthening and size effect are taken into consideration through a homogenized matrix (Co-WC). FE results show that the WC phase has a higher mean stress value than the diamond phase, which is proved by the hardness test. The experimental and numerical results are compared on micro and macro scales. For the local strain distribution and the damage development, FE models well predict the reality, even in morphological details. An optimized microstructure should avoid diamond clusters and its local volume concentrations, which easily provoke fatal fracture.

Conductive polymer composites (CPCs) combine the high electrical conductivity of nanofillers with the lightweight, processability, and corrosion resistance of polymers, making them promising materials for electrochemical applications. This work extends a previously developed discrete element method (DEM)-based conductivity calculation approach from point-like conductive particles to one-dimensional linear fillers, with carbon nanotubes (CNTs) used as the representative filler system. CNTs are represented as chains of connected spherical computational elements, enabling filler curvature, inter-filler contacts, and conductive network topology to be incorporated into the conductivity calculation. A multiscale modeling framework combining the extended DEM framework, molecular dynamics (MD)-generated morphologies, and equivalent circuit modeling is employed to analyze filler morphology, percolation behavior, and processing-induced electrical anisotropy. The results demonstrate the strong influence of CNT connectivity, curvature, orientation, hybridization with carbon black (CB), and network percolation on the macroscopic electrical conductivity. The proposed framework supports the predictive design of CPCs containing fibrous conductive fillers for applications in energy storage, electrochemical sensors, and flexible bioelectronics.