Phase behavior of polymer-nanorod composites: A comparative study using PRISM theory and molecular dynamics simulations

Dispersion and orientation patterns in nanorod-infused polymer melts

Introducing nanorods into a polymeric matrix can enhance the physical and mechanical properties of the resulting material. In this paper, we focus on understanding the dispersion and orientation patterns of nanorods in an unentangled polymer melt, particularly as a function of nanorod concentration, using molecular dynamics simulations. The system is comprised of flexible polymer chains and multi-thread nanorods that are equilibrated in the NPT ensemble. All interactions are purely repulsive except for those between polymers and rods. Results with attractive vs repulsive polymer-rod interactions are compared and contrasted. The concentration of rods has a direct impact on the phase behavior of the system. At lower concentrations, rods phase separate into nematic clusters, whereas at higher concentrations more isotropic and less structured rod configurations are observed. A detailed examination of the conformation of the polymer chains near the rod surface shows extension of the chains along the director of the rods (especially within clusters). The dispersion and orientation of the nanorods are a result of the competition between depletion entropic forces responsible for the formation of rod clusters, the enthalpic effects that improve mixing of rods and polymer, and entropic losses of polymers interpenetrating rod clusters.

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. We expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

Polymer-mediated self-assembly, dispersion, and phase separation of Janus nanorods

The challenge of stabilizing polymer nanocomposites lies in the fact that nanoparticles tend to phase separate from the polymer melt due to an entropic 'depletion attraction' between nanoparticles. Additionally, composites of polymer and nanorods show a decrease in miscibility with increasing nanorod aspect ratio [U. K. Sankar and M. Tripathy, Macromolecules, 2015, 48, 432-442; U. Erigi, U. Dhumal and M. Tripathy, J. Chem. Phys., 2021, 154, 124903]. In this work, we have studied the structure and phase behaviour of polymer-Janus nanorod mixtures using Polymer Reference Interaction Site Model (PRISM) theory and molecular dynamics simulations. The composite system of polymer and Janus nanorods of two different thicknesses, at various Janus nanorod densities, and for different interaction strengths between polymer and attractive sites of Janus nanorods (ε_pa), is investigated for their miscibility and self-assembly. At low Janus nanorod density, PRISM theory predicts transitions from the entropic depletion-driven contact aggregation of Janus nanorods to a well-dispersed phase to the bridging-driven phase separation of Janus nanorods, with increasing ε_pa. This behaviour is similar to earlier predictions for homogeneous nanorods. However, molecular dynamics simulations do not confirm the bridging-driven phase separation at high ε_pa predicted by PRISM theory. We find that both PRISM theory and molecular dynamics simulations are in agreement in the intermediate and high Janus nanorod density regimes. PRISM theory predicts, and simulations confirm, that at high Janus nanorod densities, the system undergoes a transition from depletion-driven macrophase separation to dispersion to chemical anisotropy-driven self-assembly with increasing ε_pa. The self-assembly at high ε_pa is mediated by the polymer. At intermediate Janus nanorod densities, the usual transition from an entropic depletion-driven macrophase separation to dispersion is predicted at low ε_pa. At high ε_pa, both PRISM theory and molecular dynamics simulations show transition to a state that is simultaneously macrophase separated and microphase separated (self-assembled).

Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

Using molecular dynamics simulations, we elucidate the effect of nanorod roughness on nanorod aggregation, dispersion, and percolation in polymer nanocomposites (PNCs). By choosing coarse-grained models that enable systematic variation of the nanorod roughness and by selecting purely repulsive pairwise interactions for nanorods and polymer chains, we show how nanorod roughness affects the entropic driving forces for various PNC morphologies. At this entropically driven limit, we find that increasing nanorod roughness hinders nanorod aggregation and promotes nanorod percolation in the polymer melt. As nanorod roughness increases, the nanorod volume fraction needed to induce nanorod aggregation also increases. Increasing nanorod roughness increases the configurational entropy of the polymer chains and lowers the entropically induced depletion attraction between nanorods.