Abstract
We study the structural characteristics of a system of charged nanoparticles in a neutral polymer solution while accounting for the differences in the dielectric constant between the particles, polymer and the solvent. We use a hybrid computational methodology involving a combination of single chain in mean-field simulations and the solution of the Poisson's equation for the electrostatic field. We quantify the resulting particle structural features in terms of radial distribution function among particles as a function of the dielectric contrast, particle charge, particle volume fraction and polymer concentration. In the absence of polymers, charged macroions experience increased repulsion with a lowering of the ratio of particle to solvent dielectric constant. The influence of the dielectric contrast between the particle and the solvent however diminishes with an increase in the particle volume fraction and/or its charge. In the presence of neutral polymers, similar effects manifest, but with the additional physics arising from the fact that the polymer-induced interactions are influenced by the dielectric contrast of the particle and solvent.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3748-3759 |
| Number of pages | 12 |
| Journal | Soft Matter |
| Volume | 14 |
| Issue number | 19 |
| DOIs | |
| State | Published - 2018 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): OSR-2016-CRG5-2993-1
Acknowledgements: We are grateful to Dr Jian Qin, Dr Issei Nakamura and Dr Victor Pryamitsyn for their valuable comments on a preprint of this article. We acknowledge funding in part by grants from the Robert A. Welch Foundation (Grant F1599), the National Science Foundation (DMR-1721512), to King Abdullah University of Science and Technology (OSR-2016-CRG5-2993-1). Acknowledgment is also made to the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research (56715-ND9). We acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for computing resources that contributed to the research results reported within this paper.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.