Tailoring Static and Dynamic Properties in Natural Rubber Nanocomposites via Bidisperse Filler Architecture

ABSTRACT The mechanical properties of polymer nanocomposites are governed by filler network architecture, yet the specific influence of nanoparticle (NP) size distribution remains incompletely quantified. Using coarse‐grained molecular dynamics simulations, this work investigates structure–property relationships in natural rubber composites reinforced with bidisperse carbon black (CB) NPs. By systematically varying both the mixing ratio ( α , defined as the volume fraction of large NPs) and total filler content, we disentangle their competing effects on static and dynamic behavior. Our results demonstrate that while smaller NPs facilitate network formation at lower volume fractions due to higher number density, systems with more large NPs develop a superior storage modulus and enhanced network stability under dynamic shear, revealing a fundamental shift in reinforcement mechanisms that govern elastic and viscoelastic responses across loading conditions. Although the Payne effect intensity depends mainly on total filler content, hybrid systems display exceptional reinforcement efficiency in terms of modulus enhancement beyond percolation. This study establishes that optimizing CB size distribution to construct balanced network architectures provides a more effective strategy for tailoring stiffness and viscoelasticity than the unilateral pursuit of high‐specific surface‐area nanomaterials.