Cylindrical brushes with ionized side chains: Scaling theory revisited

Literature Information

Publication Date 2023-10-26
DOI 10.1039/D3SM00727H
Impact Factor 3.679
Authors

Ekaterina B. Zhulina



Abstract

We revisit the classic scaling model of a cylindrical polyelectrolyte (PE) brush focusing on molecular brushes with stiff backbones and dispersions of polymer-decorated nanorods. Based on the blob representation we demonstrate that similarly to the case of planar PE brushes, separation of intra- and intermolecular repulsions between charges leads to novel scaling regimes for cylindrical PE brushes in salt-added solution and a sharper decrease in its thickness versus salt concentration dependence. These theoretical predictions may inspire further comprehensive experimental research and computer simulations of synthetic and biopolyelectrolyte cylindrical brushes.

Source Journal

Soft Matter

Soft Matter
CiteScore: 6
Self-citation Rate: 10.3%
Articles per Year: 856

Soft Matter provides a unique forum for the communication of significant advances in interdisciplinary soft matter research. There is a particular focus on the interface between chemistry, physics, materials science, biology and chemical engineering. Research may report new soft materials or phenomena, encompass their design, synthesis, and use in new applications; or provide fundamental insight and observations on their behaviour. Experimental, theoretical and computational soft matter approaches are encouraged. The scope of Soft Matter covers the following. Soft matter assemblies, including colloids, granular matter, liquid crystals, gels & networks, polymers, hybrid materials, active matter and further examples Soft nanotechnology, soft robotics and devices Synthesis, self-assembly and directed assembly Biological aspects of soft matter including proteins, biopolymers, cells and tissues Surfaces, interfaces and interactions Phase behaviour, coacervation and rheological behaviour Sustainable soft materials including recycling, circular economy and end of life Mechanistic insights and modelling

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