We report the size-controlled self-assembly of polymersomes through the cooperative self-assembly of nanoparticles and amphiphilic polymers. Polymersomes densely packed with magnetic nanoparticles in the polymersome membrane (magneto-polymersome) were fabricated with a series of different sized iron oxide nanoparticles. The distribution of nanoparticles in a polymersome membrane was size-dependent; while small nanoparticles were dispersed in a polymer bilayer, large particles formed well-ordered superstructure at the interface between the inner and outer layer of a bilayer membrane. The yield of magneto-polymersomes increased with increasing the diameter of incorporated nanoparticles. Moreover, the size of polymersomes was effectively controlled by varying the size of incorporated nanoparticles. This size-dependent self-assembly was attributed to the polymer chain entropy effect and the size-dependent localization of nanoparticles in polymersome bilayers. The transverse relaxation rates (r2) of magneto-polymersomes increased with increasing the nanoparticle diameter and decreasing the size of polymersomes, reaching 555 ± 24 s−1mM−1 for 241 ± 16 nm polymersomes, which is the highest value reported to date for superparamagnetic iron oxide nanoparticles.

Polymer field theory has emerged as a powerful tool for describing the equilibrium phase behavior of complex polymer formulations, particularly when one is interested in the thermodynamics of dense polymer melts and solutions where the polymer chains can be accurately described using Gaussian models. However, there are many systems of interest where polymer field theory cannot be applied in such a straightforward manner, such as polymer nanocomposites. Current approaches for incorporating nanoparticles have been restricted to the mean-field level and often require approximations where it is unclear how to improve their accuracy. In this paper, we present a unified framework that enables the description of polymer nanocomposites using a field theoretic approach. This method enables straightforward simulations of the fully fluctuating field theory for polymer formulations containing spherical or anisotropic nanoparticles. We demonstrate our approach captures the correlations between particle positions, present results for spherical and cylindrical nanoparticles, and we explore the effect of the numerical parameters on the performance of our approach.

We analyze the dynamics from microsecond-long, atomistic molecular dynamics (MD) simulations of a series of precise poly­(ethylene-co-acrylic acid) ionomers neutralized with lithium, with three different spacer lengths between acid groups on the ionomers and at two temperatures. At short times, the intermediate structure factor calculated from the MD simulations is in reasonable agreement with quasi-elastic neutron scattering data for partially neutralized ionomers. For ionomers that are 100% neutralized with lithium, the simulations reveal three dynamic processes in the chain dynamics. The fast process corresponds to hydration librations, the medium-time process corresponds to local conformational motions of the portions of the chains between ionic aggregates, and the long-time process corresponds to relaxation of the ionic aggregates. At 600 K, the dynamics are sufficiently fast to observe the early stages of lithium-ion motion and ionic aggregate rearrangements. In the partially neutralized ionomers with isolated ionic aggregates, the Li-ion-containing aggregates rearrange by a process of merging and breaking up, similar to what has been observed in coarse-grained (CG) simulations. In the 100% neutralized ionomers that contain percolated ionic aggregates, the chains remain pinned by the percolated aggregate at long times, but the lithium ions are able to move along the percolated aggregate. Here, the ion dynamics are also qualitatively similar to those seen in previous CG simulations.

Polymer nanocomposites are an important class of materials due to the nanoparticles' ability to impart functionality not commonly found in a polymer matrix, such as electrical conductivity or tunable optical properties. While the equilibrium properties of polymer nanocomposites can be treated using numerous theoretical and simulation approaches, in experiments the effects of processing and kinetic traps are significant and thus critical for understanding the structure and the functionality of polymer nanocomposites. However, simulation methods that can efficiently predict kinetically trapped and metastable structures of polymer nanocomposites are currently not common. This is particularly important in inhomogeneous polymers such as block copolymers, where techniques such as solvent vapor annealing are commonly employed to improve the long-range order. In this work, we introduce a dynamic mean field theory that is capable of predicting the result of processing the structure of polymer nanocomposites, and we demonstrate that our method accurately predicts the equilibrium properties of a model system more efficiently than a particle-based model. We subsequently use our method to predict the structure of block copolymer thin films with grafted nanoparticles after solvent annealing, where we find that the final distribution of the grafted nanoparticles can be controlled by varying the solvent evaporation rate. The extent to which the solvent evaporation rate can affect the final nanoparticle distribution in the film depends on the grafting density and the length of the grafted chains. Furthermore, the effects of the solvent evaporation rate can be anticipated from the equilibrium nanoparticle distribution in the swollen and dry states.