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.

细菌附着是一个复杂的过程，受流动条件，施加的应力以及支撑材料和细胞的表面特性和结构的影响。在这项工作中，已经报道了关于牙菌斑的重要早期定居者戈登链球菌（S. gordonii）的细胞初始附着在不锈钢（SS）样品上的实验。推动这项研究的主要目的是确定SS样品的表面粗糙度和拓扑结构对戈登酵母菌细胞初始附着的影响（如果有的话）。选择这种材料和细菌是因为它们与牙科植入物和牙科植入物感染有关。在细菌附着之前，表面会受到界面环境（例如来自口腔的唾液薄膜）的调节。由于这个原因，还研究了细胞与唾液预涂的SS样品的附着情况。通过实现对流扩散反应方程式和表面粗糙度信息的扩展Derjaguin Landau Verwey和Overbeek（XDLVO）理论，开发了一个计算模型，以帮助更好地了解细胞粘附的物理原理。通过使用源自原子力显微镜（AFM）测量的统计参数重建表面形貌，对表面粗糙度进行建模。使用此计算模型，在静态和流动流体环境中，均定量检查了粗糙度和表面图案对细菌附着的影响。结果表明，在静态流体条件下，较粗糙的表面（在亚微米级范围内）通常会增加细菌附着，这在数量上与实验测量结果吻合。在流动条件下，计算流体力学（CFD）模拟预测通道内对流扩散的减少，这将减少细菌的附着。当结合表面粗糙度影响时，该计算模型还预测，这项工作中讨论的表面形貌会导致总体细菌附着率略有下降。这表明表面图案的防止附着效果在对附着力有利的亚微米级表面粗糙度上占主导地位；因此，粘附细胞的净减少。这在质量上与此处报告的实验观察结果相符，并且在测量误差范围内与低流速在数量上相匹配。