Designing high performance polymer nanocomposites by incorporating robustness-controlled polymeric nanoparticles: Insights from molecular dynamics

Introducing polymeric nanoparticles into polymer matrices is an interesting topic, and the robustness of the polymeric nanoparticles is crucial for the properties of the polymer nanocomposites (PNCs). In this study, by incorporating star-shaped polymeric nanoparticles (SSPNs) into the polymer, the effect of the sphericity (η) and arm length (L) of the SSPNs on the mechanical properties of PNCs is systematically investigated, using a coarse-grained molecular dynamics simulation. In addition, the linear and spherical nanoparticles (NPs) are compared with SSPNs by fixing the approximate diameter and mass fraction of the NPs. The radial distribution function, the second virial coefficient, mean-squared displacement, bond autocorrelation function, and primitive path analysis are employed to systematically characterize the structure and dynamics of these new PNCs. It is found that the dispersion of the NPs is enhanced with the increase of η, and the entanglement density reaches maximum, which both contribute to the greatest mechanical reinforcing effect. More significantly, it is found that the classical Payne effect, namely the storage as a function of the strain amplitude, decreases remarkably, and with a much smaller loss factor for these SSPN filled polymer nanocomposites, compared to conventional PNCs filled with rigid NPs. Furthermore, the change of the arm length of the SSPNs is found to exhibit the same effect on the mechanical and viscoelastic properties, as the variation of the number of the arms. In general, this work shows that these new SSPN filled polymer nanocomposites can exceed conventional PNCs, by manipulating the robustness of the SSPNs using, for example, the number and length of the arms. This research may provide guidelines for the investigation of the structure-property relationships of the topological structure of polymeric nanoparticles.