Abstract
Abstract: We present results from explicit-solvent coarse-grained molecular dynamics (MD) simulations of fully charged, salt-free, and unentangled polyelectrolytes in semidilute solutions. The inclusion of a polar solvent in the model allows for a more physical representation of these solutions at concentrations, where the assumptions of a continuum dielectric medium and screened hydrodynamics break down. The collective dynamic structure factor of polyelectrolytes, S(q, t), showed that at q> q∗ , where q∗= 2 π/ ξ is the polyelectrolyte peak in the structure factor S(q) and ξ is the correlation length, the relaxation time obtained from fits to stretched exponential was τKWW∼ q- 3 , which describes unscreened Zimm-like dynamics. This is in contrast to implicit-solvent simulations using a Langevin thermostat where τKWW∼ q- 2 . At q< q∗ , a crossover region was observed that eventually transitions to another inflection point τKWW∼ q- 2 at length scales larger than ξ for both implicit- and explicit-solvent simulations. The simulation results were also compared to scaling predictions for correlation length, ξ∼cp-1/2 , specific viscosity, ηsp∼cp1/2 , and diffusion coefficient, D∼cp0 , where cp is the polyelectrolyte concentration. The scaling prediction for ξ holds; however, deviations from the predictions for ηsp and D were observed for systems at higher cp , which are in qualitative agreements with recent experimental results. This study highlights the importance of explicit-solvent effects in molecular dynamics simulations, particularly in semidilute solutions, for a better understanding of polyelectrolyte solution behavior. Graphic abstract: [Figure not available: see fulltext.].
Original language | English |
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Article number | 92 |
Journal | European Physical Journal E |
Volume | 46 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2023 |
Funding
This work was performed at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility operated at Oak Ridge National Laboratory. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. YW is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Early Career Research Program Award KC0402010, under Contract DE-AC05-00OR22725. JC acknowledges Bard and ChatGPT, large language models (LLMs) from Google and OpenAI, respectively, for their assistance in proofreading the manuscript, checking grammar, spelling, and punctuation, and providing suggestions for alternative phrasing to enhance clarity and flow. Both models did not contribute to the research content. The authors are grateful to Dr. Ali H. Slim, Prof. Amanda Marciel, Prof. Jacinta Conrad and Prof. Andrey Dobrynin for stimulating discussions. This work was performed at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility operated at Oak Ridge National Laboratory. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. YW is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Early Career Research Program Award KC0402010, under Contract DE-AC05-00OR22725. JC acknowledges Bard and ChatGPT, large language models (LLMs) from Google and OpenAI, respectively, for their assistance in proofreading the manuscript, checking grammar, spelling, and punctuation, and providing suggestions for alternative phrasing to enhance clarity and flow. Both models did not contribute to the research content. The authors are grateful to Dr. Ali H. Slim, Prof. Amanda Marciel, Prof. Jacinta Conrad and Prof. Andrey Dobrynin for stimulating discussions.
Funders | Funder number |
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Center for Nanophase Materials Sciences | |
U.S. Department of Energy | DE-AC05-00OR22725 |
Office of Science | |
Basic Energy Sciences | KC0402010 |
Oak Ridge National Laboratory | |
OpenAI |