Morphological transformations in polymer brushes in binary mixtures: DPD study

Jianli Cheng, Aleksey Vishnyakov, Alexander V. Neimark

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Morphological transformations in polymer brushes in a binary mixture of good and bad solvents are studied using dissipative particle dynamics simulations drawing on a characteristic example of polyisoprene natural rubber in an acetone-benzene mixture. A coarse-grained DPD model of this system is built based on the experimental data in the literature. We focus on the transformation of dense, collapsed brush in bad solvent (acetone) to expanded brush solvated in good solvent (benzene) as the concentration of benzene increases. Compared to a sharp globule-to-coil transition observed in individual tethered chains, the collapsed-to-expanded transformation in brushes is found to be gradual without a prominent transition point. The transformation becomes more leveled as the brush density increases. At low densities, the collapsed brush is highly inhomogeneous and patterned into bunches composed of neighboring chains due to favorable polymer-polymer interaction. At high densities, the brush is expanded even in bad solvent due to steric restrictions. In addition, we considered a model system similar to the PINR-acetone-benzene system, but with the interactions between the solvent components worsened to the limit of miscibility. Enhanced contrast between good and bad solvents facilitates absorption of the good solvent by the brush, shifting the collapsed-to-expanded transformation to lower concentrations of good solvent. This effect is especially pronounced for higher brush densities.

Original languageEnglish
Pages (from-to)12932-12940
Number of pages9
JournalLangmuir
Volume30
Issue number43
DOIs
Publication statusPublished - 4 Nov 2014
Externally publishedYes

Fingerprint

Dive into the research topics of 'Morphological transformations in polymer brushes in binary mixtures: DPD study'. Together they form a unique fingerprint.

Cite this