The adsorption of single polyelectrolyte molecules in shear flow is studied using Brownian dynamics simulations with hydrodynamic interaction (HI). Simulations are performed with bead-rod and bead-spring chains, and electrostatic interactions are incorporated through a screened Coulombic potential with excluded volume accounted for by the repulsive part of a Lennard-Jones potential. A correction to the Rotne-Prager-Yamakawa tensor is derived that accounts for the presence of a planar wall. The simulations show that migration away from an uncharged wall, which is due to bead-wall HI, is enhanced by increases in the strength of flow and intrachain electrostatic repulsion, consistent with kinetic theory predictions. When the wall and polyelectrolyte are oppositely charged, chain behavior depends on the strength of electrostatic screening. For strong screening, chains get depleted from a region close to the wall and the thickness of this depletion layer scales as N13 Wi23 at high Wi, where N is the chain length and Wi is the Weissenberg number. At intermediate screening, bead-wall electrostatic attraction competes with bead-wall HI, and it is found that there is a critical Weissenberg number for desorption which scales as N-12 κ-3 (lB ∫q∫) 32, where κ is the inverse screening length, lB is the Bjerrum length, is the surface charge density, and q is the bead charge. When the screening is weak, adsorbed chains are observed to align in the vorticity direction at low shear rates due to the effects of repulsive intramolecular interactions. At higher shear rates, the chains align in the flow direction. The simulation method and results of this work are expected to be useful for a number of applications in biophysics and materials science in which polyelectrolyte adsorption plays a key role.
Bibliographical noteFunding Information:
We thank Mr. Hongbo Ma and Professor Michael D. Graham of the University of Wisconsin-Madison for sharing their code for BD simulations of bead-spring chains that incorporates bead-wall HI. We are also grateful for resources from the University of Minnesota Supercomputing Institute. This material is based on work supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911-NF-04-1-0265. Our work was also supported in part by the Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory (ARL) under Cooperative Agreement No. DAAD19-01-2-0014. The content does not necessarily reflect the position or policy of the government, and no official endorsement should be inferred.