Quantitative characterization and modeling strategy of nanoparticle dispersion in polymer composites

Abstract

Nanoparticle dispersion plays a crucial role in the mechanical properties of polymer nanocomposites. Transmission Electron Microscope/Scanning Electron Microscope (TEM/SEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its properties. Therefore, there is a strong need to develop a quantitative measure to effectively describe nanoparticle dispersion from a TEM/SEM image. This article reports an effective modeling strategy to characterize nanoparticle dispersion states among different locations of a nanocomposite surface. An engineering-driven inhomogenous Poisson random field is proposed to represent the nanoparticle dispersion at the nanoscale. The model parameters are estimated through the Bayesian Markov Chain Monte Carlo technique to overcome the challenge of the limited amount of accessible data due to the time-consuming sample collection process. The TEM images taken from nano-silica/epoxy composites are used to support the proposed methodology. The research strategy and framework are generally applicable to other nanocomposite materials.

Keywords:

• Nanoparticle dispersion
• inhomogeneous Poisson random field
• Bayesian hierarchical modeling

Acknowledgements

Huang's work is partially supported by NSF grant CMMI-1002580. Dr. Zhong Zhang at the National Center for Nanoscience and Technology, China, provided measurement data and process knowledge regarding polymer nanocomposites.