Characterization of Carbon-Black-Based Nanocomposite Mixtures of Varying Dispersion for Improving Stochastic Model Fidelity

Carbon black nanocomposites are complex systems that show potential for engineering applications. Understanding the influence of preparation methods on the engineering properties of these materials is critical for widespread deployment. In this study, the fidelity of a stochastic fractal aggregate placement algorithm is explored. A high-speed spin-coater is deployed for the creation of nanocomposite thin films of varying dispersion characteristics, which are imaged via light microscopy. Statistical analysis is performed and compared to 2D image statistics of stochastically generated RVEs with comparable volumetric properties. Correlations between simulation variables and image statistics are examined. Future and current works are discussed.

Automated summary

This study explores the accuracy of a stochastic fractal aggregate placement algorithm. Nanocomposite thin films with varying dispersion characteristics are created using a high-speed spin-coater and imaged via light microscopy. Statistical analysis is compared to 2D image statistics of stochastically generated RVEs. Correlations between simulation variables and image statistics are examined. Future and current works are discussed.