Investigating bacterial behavior through finite element analysis on nanostructured surfaces

Abstract

Bioinspired nanostructured surfaces on wings of cicada and dragon fly can kill bacteria upon come in to their contact. A major challenge in developing the effective bactericidal surfaces stems from our limited understanding of interaction mechanism between bacteria and nanostructured surfaces leading to mechanical damage of bacteria. In this work, we study interaction of bacteria with multiple pillars, typically encountered on a textured surface. Towards this, atomic force microscopy (AFM) nanoindentation experiments on bacterial cell has been conducted to analyze and extract their mechanical behavior by a single-tip indenter. A viscoelastic and plastic finite element model was used to verify the experimental data. The study was expanded further to analyze the rupture behavior of bacteria on multipillar nanostructured surfaces having varying geometry of pillars. The experimental results show that the stress vs strain behavior of bacteria is similar to a polymeric material with linear trend till yield at 32.3 ± 2.4 kPa, following a plastic deformation till fracture of cell membrane at 37.1 ± 4.4 kPa. A comparative analysis of single- and multiple indentations on bacteria shows that the rupture force of bacteria actually rises as a result of multi-indentation. This outcome suggests a decreased likelihood of rupture owing to the distribution of load across the multiple pillars of a nanostructured surface. Further evaluation of the multipillar effect by changing spacing between the pillars reveals that when the spacing is less than 100 nm, bacterial rupture is encouraged for all pillar tip radii. The result on varying tip radius of the pillar shows that the sharper pillars give non-uniform strain distribution which enhances the chances of rupture in bacterial cell wall. It is envisaged that the insights gained from this work will contribute to the development of effective bactericidal surfaces. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.