Computational design of antimicrobial active surfaces via automated Bayesian optimization

TL;DR

This paper presents an automated framework using Bayesian optimization and individual-based modeling to design nano-surfaces with specific topographies for effective biofilm removal under various mechanical stimuli.

Contribution

It introduces a novel automated design process for biofilm-resistant surfaces using Bayesian optimization, tailored to different mechanical removal methods.

Findings

Densely distributed short pillars prevent biofilm formation.

Sparse tall pillars are optimal under fluid shear.

Thick trapezoidal cones are effective with vibrations.

Abstract

Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nano-surfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated ideal nano-surfaces for biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.