Random sequential adsorption: An efficient tool for investigating the deposition of macromolecules and colloidal particles

Abstract

Random Sequential Adsorption (RSA) is one of the most efficient theoretical models used to investigate adsorption of macromolecules and particles, with a long-standing tradition in the field of colloid and interface science. In the first part of this paper, we demonstrate how the RSA model can be applied to interpret the experimental data and extract information about the density of the adsorption monolayer, the kinetics of its growth, and microstructural properties such as pair-correlation function and monolayer roughness. We briefly summarized the most important generalizations of the RSA model for monolayers and reviewed its extensions considering, e.g., various particle shapes, the introduction of electrostatic interaction, or adsorption on non-uniform substrates. We thoroughly scrutinized the extended RSA model developed for bilayer and multilayer formation. We collected the mean saturated packing fractions of various two- and three-dimensional objects and provided the most accurate result for two-dimensional disk packing. In the second part of this paper, we summarize various numerical algorithms and techniques that allow one to effectively implement RSA algorithms. We describe efficient methods for detecting intersections of various shapes and techniques enabling generation of strictly saturated RSA packings built of a wide range of different shapes. We hinted at how an inherently sequential RSA scheme can be parallelized. Finally, we critically discuss the limitations of the model and possible directions for future studies.

Keywords: Intersection detection; Kinetics of macromolecule adsorption; Mean packing fraction; Numerical modeling of colloidal particle deposition; Parallelization and optimization of the RSA algorithm; Random sequential adsorption.