Abstract
The current study addresses the critical issue of bacterial adhesion and proliferation on surfaces, particularly in industries such as petroleum, marine, textiles, healthcare, food, and water management, with global biofilm-associated costs exceeding $5 trillion. Here we propose a transdisciplinary approach with computational methods to bring together the strengths, particularly advanced knowledge and cutting edge technologies of life sciences, material science, nanotechnology, engineering, and machine learning, with a goal of addressing vexing challenges facing microbiologically influenced corrosion. The current study focuses on developing biofouling resistant graphene nanocomposites with a thin silver film on nickel substrates against sulfate reducing bacteria (SRB). SEM images of biofilm formation after 30 days of exposure to SRB are analyzed using a pretrained deep learning architecture and otsu thresholding for semi-automated microbial segmentation. A pretrained multi-task deep learning model cellpose and segment anything model are employed to extract bacterial regions from the images, providing precise quantitative analysis of SRB cell surface coverage on graphene nanocomposites. This computational model establishes the interrelation between corrosion findings and the antibiofouling performance of graphene nanocomposites on nickel surfaces.