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Updated: Sep 3, 2026

Synthesis of Monodisperse Cylindrical Nanoparticles via Crystallization-driven Self-assembly of Biodegradable Block Copolymers
Published on: June 20, 2019
Molecular insights into conformational and structural properties of diblock copolymer nanoparticles: a
Krisana Monklang1, Visit Vao-Soongnern2
1Laboratory of Computational and Applied Polymer Science (LCAPS), School of Chemistry, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
Context:
Monte Carlo (MC) simulations reveal that diblock copolymer nanoparticle morphology is dictated by the interplay between intermolecular attractions and soft confinement. Blocks with stronger cohesive interactions undergo local densification in the core, while blocks with weaker interactions localize at the surface. A key stabilizing mechanism is the expulsion of high-mobility end-monomers toward the interface to maximize cohesive energy in the bulk. This process is accompanied by a backbone gauche-to-trans conformational transition that enables polymer chains to flatten tangentially and adapt to nanoparticle curvature. By quantifying radial density profiles, structural anisotropy, and energetic oscillations, the methodology distinguishes morphologies ranging from surface-energy-driven mixing to frustrated core-shell segregation, providing molecular-level insight for the rational design of functional polymeric nanostructures.
Methods:
MC simulation of the coarse-grained (CG) models based on "polyethylene-like" chains mapped onto the second nearest neighbor diamond (2nnd) lattice was employed to investigate conformational and structural properties of diblock copolymer nanoparticles. The model integrates a revised Rotational Isomeric State (RIS) model to define backbone bond conformations and discretized Lennard-Jones (LJ) potential energy settings to systematically vary intermolecular interaction parameters. Free-standing nanoparticles were equilibrated using single bead moves and the Metropolis criterion over 40 million Monte Carlo steps, and data analysis was based on snapshots collected at intervals of 10,000 MCS. All simulations and data analysis were performed using in-house FORTRAN codes with the gfortran compiler.

