Role of Interface Structure and Chain Dynamics on the Diverging Glass Transition Behavior of SSBR-SiO2-PIL Elastomers.

Intermolecular interactions between the constituents of a polymer nanocomposite at the polymer-particle interface strongly affect the segmental mobility of polymer chains, correlated with their glass transition behavior, and are responsible for the improved dynamical viscoelastic properties. In this work, we emphasized on the evolution of characteristic interfaces and their dynamics in silica (SiO2 NP)-reinforced, solution-polymerized, styrene butadiene rubber (SSBR) composites, whose relative prevalence varied with the phosphonium ionic liquid (PIL) volume fraction, used as an interfacial modifier. The molecular origins of such interfaces were examined through systematic dielectric spectroscopy, molecular dynamics (MD) simulations, and dynamic-mechanical analyses. The PIL facilitated H-bonding, cation-π, surface-phenyl, and van der Waals interfacial interactions between SSBR and SiO2 NP, thereby regulating the polymer chain dynamics, orientation, and mean-square displacement. Specifically, the mass density profiles from MD simulations revealed the dynamic gradient of polymer chains in the interfacial region as a function of radial distance from the center of mass of the SiO2 NP surface. The results showed a structuring effect to result in well-resolved density peaks at specific radial distances with the tangential orientation of styrene monomers in the vicinity of the SiO2 NP surface. These domino effects highlighted strong interfacial interactions to have an indispensable effect on the viscoelastic performance and thermal motion of SSBR molecular chains, leading to a higher glass transition temperature (T g) by ∼15 K, validating the experimental data. More importantly, our results gave new insights into the fundamental understanding of the fact that the strength of intermolecular interactions induced by PIL at the polymer-particle interface is the key to control the α-relaxation dynamics and T g optimization, desired for specific applications.