Optimizing Graphene Dispersion via Polymer Grafting

The dispersion of graphene sheets within a polymer matrix is critical for the performance of graphene-reinforced polymer nanocomposites, particularly in achieving optimal percolation and electrical conductivity. However, accurately characterizing and controlling the dispersion of 2D graphene in polymer melts remains a significant challenge due to the complex and varied configurations that graphene sheets can adopt. Herein, we employ coarse-grained molecular dynamics simulations to investigate how the grafting density (g) and grafted chain length (n) of poly(methyl methacrylate), p(MMA), affect graphene dispersion, where graphene is classified into three distinct morphologies, i.e., “aggregation”, “intercalated”, and “unbound”. We find that increasing gand nenhances graphene dispersion, evidenced by a higher dispersity parameter (fd), stronger interfacial interactions, greater Gaussian surface area of graphene clusters, and lower aggregation energy (EAggregation). Our results also indicate that a higher fdis linked to a higher Young’s modulus in the nanocomposite, reaching a maximum of 4.18 GPa. However, the electrical conductivity of nanocomposites initially rises with increasing gand nbut declines beyond g> 5% and n> 10 due to reduced conductive pathways caused by graphene overdispersion, as revealed by the conductive edge analysis. Additionally, the free polymer fraction and chain length significantly influence toughness, and grafting p(MMA) chains on graphene slows down the dynamics of the surrounding polymer due to the intrinsic stiffness of graphene, an effect more pronounced at higher fd(well dispersed). These findings present an effective approach for tuning and precisely characterizing graphene dispersity, clarifying its influence on material properties and forming the interfacial design of advanced nanocomposites reinforced with functional two-dimensional nanofillers.