Issue 2, 2025

Dissipative particle dynamics simulations on the self-assembly of rod-coil asymmetric diblock molecular brushes bearing responsive side chains

Abstract

The self-assembly behaviors of rod-coil asymmetric diblock molecular brushes (ADMBs) bearing responsive side chains in a selective solvent are investigated via dissipative particle dynamics simulations. By systematically varying the polymerization degree, copolymer concentration, and side chain length, several morphological phase diagrams were constructed. ADMB assemblies exhibited a rich variety of morphologies, including cylindrical micelles, spherical micelles, nanowires, polyhedral micelles, ellipsoid micelles, and large compound micelles. The structures of the representative nanowires were analyzed in detail. A kinetics study revealed that the one-dimensional growth of nanowires follows the step-growth polymerization mechanism. Besides, by calculating the local order parameter of the rigid chains, we found that increasing the lengths of A and C side chains can promote the ordered arrangement of the rigid chains. Moreover, the rod-to-coil conformation transitions were simulated to explore the stimuli-responsive behaviors of ADMBs with responsive rigid side chains. The simulation results indicated that the volume of the assemblies expanded without the support of the rigid chains. The present work not only provides a comprehensive understanding of the self-assembly behaviors of ADMBs but also provides meaningful theoretical support for the development of novel molecular brush materials.

Graphical abstract: Dissipative particle dynamics simulations on the self-assembly of rod-coil asymmetric diblock molecular brushes bearing responsive side chains

Supplementary files

Article information

Article type
Paper
Submitted
21 Oct 2024
Accepted
29 Nov 2024
First published
03 Dec 2024

Soft Matter, 2025,21, 255-261

Dissipative particle dynamics simulations on the self-assembly of rod-coil asymmetric diblock molecular brushes bearing responsive side chains

H. Zhu, W. Feng, Y. Wang, Z. Li, B. Xu and S. Lin, Soft Matter, 2025, 21, 255 DOI: 10.1039/D4SM01232A

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