Monte Carlo simulation of selective adsorption in a binary hard-disk mixture on patterned adhesive surfaces

TL;DR

This study uses grand canonical Monte Carlo simulations to analyze how surface patterning influences selective adsorption of a binary hard-disk mixture, revealing the importance of domain size and arrangement.

Contribution

It demonstrates how surface geometry and domain size affect selectivity, separating affinity effects from particle size and chemical potential influences.

Findings

Surface geometry significantly influences selectivity at various chemical potentials.

Domains comparable to particle size enhance selectivity through large particle-domain overlap.

Smaller domains can increase selectivity near uniform attractive surfaces.

Abstract

Selective adsorption in a two-dimensional model of a binary hard-disk mixture on patterned adhesive surfaces is studied using grand canonical Monte Carlo simulations. The two species have equal diameters and equal bulk chemical potentials, but different attraction strengths to adhesive domains. Thus, affinity-driven selectivity is separated from particle-size asymmetry and unequal chemical potentials. The surface pattern is defined by domain size, domain surface coverage, and ordered or disordered arrangement of circular domains. The results show that selectivity depends strongly on surface geometry, especially at low and intermediate chemical potentials. Domains comparable to the particle size enhance selectivity by forming adsorption regions with large particle-domain overlap, whereas larger domains can provide high selectivity at low chemical potentials. For small domains, further reduction in size can also increase selectivity as the system approaches a uniform attractive surface with corresponding effective affinity parameters of the species.