Inter-Nanoparticle Repulsion Effect on Self-Organization in Polymer Nanocomposites: A Langevin Dynamics Simulation Study

arXiv:2611.17254

Paawan Chandrakanth, Nikos Ch. Karayiannis, Subhalaxmi Das, Supriya Roy

Demixing through liquid-liquid phase separation is relevant to nanoparticle (NP) aggregation in polymer nanocomposites (PNCs), biomolecular compartmentalization, and liquids embedded in elastic networks. Mixing-demixing phase behavior depends on factors, such as particle size distribution, system dimen-sionality, intermolecular interactions, and relative concentrations. Here, using Langevin Dynamics simu-lations, we systematically investigate the effect of inter-nanoparticle repulsion on the self-assembly of PNCs under extreme confinement. A nanoparticle assembly, in the form of a compact droplet, is immersed in a polymer matrix of semiflexible, rod-like chains. By varying the nanoparticle-nanoparticle repulsion strength, while fixing all other interactions, we identify four distinct regimes with respect to the self-assembled NP morphologies: droplet, slab, crystalline droplet, and dispersed. Cluster-based analysis reveals a progressive loss of nanoparticle droplet integrity, leading to fragmentation within the dispersed regime. Local order of the nanoparticle assembly and global order of polymer chains are gauged through refined descriptors, quantifying the degrees of crystallinity and of nematic order, respectively. Interest-ingly, the slab regime exhibits negligible nanoparticle crystallization yet demonstrates the strongest polymer nematic ordering. In contrast, the crystalline droplet displays extensive triangular close-packed crystallites accompanied by a significant reduction in polymer nematic order, owing to its rhomboid shape. In the dispersed regime, nanoparticle fragmentation facilitates efficient mixing and local smectic-like ordering of the polymer chains. These findings demonstrate that fine-tuning inter-NP repulsions can control self-organization and the resulting morphologies, providing insights into PNCs with tailored functionality.