Selective distribution of BaTiO3 and graphene in PS/PVDF blends: Molecular dynamics simulations

The selective distribution of inorganic particles in polymer blends is of great significance in designing the composites with excellent properties such as dielectric properties. Molecular dynamics (MD) was applied in this paper to simulate the distribution of BaTiO3 (BT) and graphene nanoplatelets (GNP) in polystyrene/poly-vinylidene fluoride (PS/PVDF) blends. The models of PS and PVDF were built and the phase structure of the blends was simulated at the different PS/PVDF mass ratios. According to the results of solubility parameter and Flory-Huggins interaction parameter (chi), the immiscibility between PS and PVDF was illustrated and the phase transition phenomena were predicted. Combined with the SEM observations, the co-continuous structure of the blend at the PS/PVDF ratio of 6/4 was obtained at the maximal chi value. The binding energy and radial distri-bution function were calculated to predict the selective distribution of BT or GNP in PS/PVDF blends. According to the results of the MD simulations, the distribution of BT in the PVDF phase and GNP in the PS phase were successfully predicted, which were in good agreement with the experimental results by SEM observations and EDS element mapping. The MD simulations would be effective in building the co-continuous immiscible blends and predicting the selective distribution of functional fillers, which will be helpful for the investigation of functional composites with high performance.

Automated summary

In this study, molecular dynamics (MD) was used to simulate the distribution of BaTiO3 (BT) and graphene nanoplatelets (GNP) in polystyrene/poly-vinylidene fluoride (PS/PVDF) blends and predicted the phase transition phenomena. Combined with SEM observations and EDS element mapping, the selective distribution of BT in the PVDF phase and GNP in the PS phase was successfully predicted. MD simulations showed effectiveness in building co-continuous immiscible blends and predicting the selective distribution of functional fillers.