Materials by Design for Stiff and Tough Nanoparticle Assemblies with Polymeric Hairs

In this work, a computational methodology for predicting the mechanical behavior of assembled hairy nanoparticle systems (aHNPs) using coarse-grained molecular dynamics simulations coupled with Gaussian process metamodeling will be presented (Hansoge et al. ACS NANO,2018). The coarse-graining approach for the polymeric hairs involves systematic parameter development based on the energy renormalization approach, which allows us to describe the behavior of the glassy nanocomposites with greater accuracy at larger length-scales. Simulations reveal that for cellulose nanocrystal-PMMA aHNPs, the Pareto frontier, which marks the optimal trade-offs between modulus and toughness within the design parameter space, can be reached when the weight percentage of nanoparticles is around 60% and the grafted chains exceed the critical length scale governing transition into the semidilute brush regime. A theoretical model with computationally determined parameters based on the Daoud−Cotton model adequately explains the dependence of the critical length scale on graft density and nanoparticle size. The computational results agree well with recent experiments and suggest that high stiffness and high toughness could be achieved by carefully tuning the molecular design parameters, most notably by keeping a relatively low grafting density while having high graft length and nanoparticle volume fraction.