Implicit-Solvent Coarse-Grained Simulations of Linear-Dendritic Block Copolymer Micelles

The design of nanoassemblies can be conveniently achieved by tuning the strength of the hydrophobic interactions of block copolymers in selective solvents. These block copolymer micelles form supramolecular aggregates, which have attracted great attention in the area of drug delivery and imaging in biomedicine due to their easy-to-tune properties and straightforward large-scale production. In the present work, we have investigated the micellization process of linear-dendritic block copolymers in order to elucidate the effect of branching on the micellar properties. We focus on block copolymers formed by linear hydrophobic blocks attached to either dendritic neutral or charged hydrophilic blocks. We have implemented a simple protocol for determining the equilibrium micellar size, which permits the study of linear-dendritic block copolymers in a wide range of block morphologies in an efficient and parallelizable manner. We have explored the impact of different topological and charge properties of the hydrophilic blocks on the equilibrium micellar properties and compared them to predictions from self-consistent field theory and scaling theory. We have found that, at higher degrees of branching in the corona and for short polymer chains, excluded volume interactions strongly influence the micellar aggregation as well as their effective charge.

Automated summary

The design of nanoassemblies can be achieved by adjusting the strength of hydrophobic interactions in block copolymers. These supramolecular aggregates have attracted attention in drug delivery and imaging due to their tunable properties and scalable production. In this study, we investigated the micellization process of linear-dendritic block copolymers to understand the effect of branching on micellar properties. We found that excluded volume interactions strongly influence the aggregation and charge of micelles at higher degrees of branching and for shorter polymer chains.