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A dynamic simulation of colloidal adsorption has been developed to probe the effects of colloidal interactions on the kinetics and extent of adsorption. The simulation accounts for diffusion by Brownian dynamics to a homogeneous planar adsorption surface from a region of constant chemical potential. A grand canonical Monte Carlo routine is used periodically to re-equilibrate this region. Particle motion in the plane of the surface is subject to either unrestricted diffusion or zero diffusion. Deryaguin-Landau-Verwey-Overbeek pair potentials are used to characterize both particle–particle and particle–surface interactions. The pair potential parameters were chosen to mimic (separately) polystyrene latex microspheres and small globular proteins, two classes of charged colloidal particles for which experimental adsorption data exist. The simulation qualitatively captures the variation in adsorptive capacity with ionic strength distinct to each system: fractional coverage increases for polystyrene latex adsorption but decreases for protein adsorption with increasing salt concentration. In the former, strong lateral repulsion between adsorbed particles appears to govern the extent of adsorption, whereas in the latter, the extent of adsorption is more strongly affected by the screening of the weak attraction between the particle and the surface. Excellent quantitative predictions for polystyrene latex adsorption with and without surface diffusion are obtained without adjustable parameters.

A new method is proposed for calculating adsorption isotherms for small, globular proteins in aqueous solution, based on colloidal descriptions of protein−protein and protein−surface interaction energies. The influence of the structure of the adsorbed protein layer on the energetics is obtained through Brownian dynamics simulations, and an algebraic expression has been developed to correlate the simulation calculations with the relevant colloidal parameters of the adsorption system. The resulting analytic isotherm equation can be used either as a predictive tool or as a means to correlate adsorption data. The qualitative influence of experimental variables such as solution pH, ionic strength, and protein size on the predicted adsorption of proteins is explored.

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