A computational model for the effect of surface topography on the antibacterial character of a nanocoating

Abstract Biocidal nanocoatings represent a promising strategy to limit pathogen transmission. Understanding how surface topography influences microbial deposition is, therefore, essential for their rational design. This study established a computational framework in which a pathogen (Staphylococcus aureus) cell is represented by an elastic triangular mesh immersed in a Lattice–Boltzmann fluid and interacting with a nanocoated substrate, also meshed, via a Lennard–Jones potential. Pathogen deposition was simulated across systematically varied surface roughness and dispersion, allowing quantification of both the contact area between cell and substrate and the mechanical load endured by the cell. The results demonstrated a robust inverse relationship between contact area and maximum local stress, revealing how substrate geometry governs the distribution of mechanical loads during deposition. This predictive link provides design-level insight into how nanoscale roughness and dispersion could be used to improve the substrate’s antibacterial potential, establishing a computational pathway for the optimisation of next-generation coatings.