Investigating Multi-Component Polymer Grafted Nanoparticles with Bidisperse Polymer Grafts using Molecular Modelling

Single-component polymer grafted nanoparticles (PGNs) are known to possess superior mechanical, thermal, and optical properties compared to polymer melt due to the structural ordering from stiff nanoparticles and interpenetration between grafted chains. However, the behavior of multi-component PGNs is relatively unexplored. To address this knowledge gap, we investigate multi-component PGNs with bi-disperse polymer grafts. A short and long graft chain is chosen to create efficient packing where smaller PGN will stay in the interstitial region of the larger one. A coarse-grained (CG-MD) model for PMMA is used for computational speed-up and the stoichiometric ratio of these two PGNs is systematically varied. Mechanical properties are investigated from uniaxial tensile simulation using atomistic modeling. Ashby plots are generated to identify the optimal ratio for higher strength and toughness. This study provides insights into the properties of the multi-component PGNs which are difficult to obtain solely from experiments. Furthermore, the crucial data from the work is used to create a machine learning-based Gaussian Process model to swiftly explore the vast design space and optimize every design parameter.