Efficient Simulation of High-Generation, Dendritic Phytoglycogen Nanoparticles using Dynamical Self-Consistent Field Theory

Phytoglycogen (PG) is a glucose-based polymer with a dendritic architecture that is extracted from sweet corn as soft, compact, monodisperse 22 nm radius nanoparticles. Extensive experimental studies have been performed to characterize the morphology and hydration properties of PG; however, little work has been done to develop a realistic, yet efficient, model for PG. We use dynamical self-consistent field theory (dSCFT) to simulate the evolution of a PG nanoparticle in water. To improve the efficiency of the evolution of the 11-generation dendrimer, we exploit the dendritic architecture of PG to decompose the bead-spring dynamics of the entire dendrimer into the independent dynamics of its constituent sub-chains. By tuning the strength of the interaction between dendrimer and solvent beads, we obtain a core-chain morphology, with a size and hydration of the nanoparticle that agree with the values for PG in water measured using small angle neutron scattering (SANS). This work validates the use of dSCFT for polymers with non-linear architectures, which will help to guide new avenues for experimental investigation.