Abstract:

Bacterial attachment is a complex process affected by flow conditions, imparted stresses, and the surface properties and structure of both the supporting material and the cell. Experiments on the initial attachment of cells of the bacterium Streptococcus gordonii (S. gordonii), an important early coloniser of dental plaque, to samples of stainless steel (SS) have been reported in this work. The primary aim motivating this study was to establish what affect, if any, the surface roughness and topology of samples of SS would have on the initial attachment of cells of the bacterium S. gordonii. This material and bacterium were chosen by virtue of their relevance to dental implants and dental implant infections. Prior to bacterial attachment, surfaces become conditioned by the interfacing environment (salivary pellicle from the oral cavity for instance). For this reason, cell attachment to samples of SS pre-coated with saliva was also studied. By implementing the Extended Derjaguin Landau Verwey and Overbeek (XDLVO) theory coupled with convection-diffusion-reaction equations and the surface roughness information, a computational model was developed to help better understand the physics of cell adhesion. Surface roughness was modelled by reconstructing the surface topography using statistical parameters derived from atomic force microscopy (AFM) measurements. Using this computational model, the effects of roughness and surface patterns on bacterial attachment were examined quantitatively in both static and flowing fluid environments. The results have shown that rougher surfaces (within the sub-microscale) generally increase bacterial attachment in static fluid conditions which quantitatively agrees with experimental measurements. Under flow conditions, computational fluid dynamics (CFD) simulations predicted reduced convection-diffusion inside the channel which would act to decrease bacterial attachment. When combined with surface roughness effects, the computational model also predicted that the surface topographies discussed within this work produced a slight decrease in overall bacterial attachment. This would suggest that the attachment-preventing effects of surface patterns dominate over the adhesion-favourable sub-microscale surface roughness; hence, producing a net reduction in adhered cells. This qualitatively agreed with experimental observations reported here and quantitatively matched experimental observations for low flow rates within measurement error.

Key words: Bacterial attachment, XDLVO theory, Computational modelling, Surface topography, Surface roughness