Speaker
Description
Sulfate-reducing bacteria (SRB) thrive in many natural environments, deep environments, and processing facilities in industrial settings and form biofilms. SRB biofilms alter the physiochemical properties of metals, inducing fouling and later biocorrosion, which cost USD 1.1Trillion to global GDP every year. To combat biocorrosion, it is essential to understand the interaction at the bacteria (biofilm)-metal surface interface. Biofilms grow on the likeliness (metal types, surface characteristics, atomic orientation, lattice, grain boundaries, grain energies) of the bacteria, a critical factor that could affect SRB biofilms, altering the biofilm-metal interface interactions.
Identification of the biofilm-metal interface interaction schemes will lead us to design a next-generation metal that will not allow biofilm formation and ultimately save resources. We hypothesize that variation in atomic lattice orientation and physical grains and grain boundaries corresponds to different surface energies, and which may affect (attract or repel) bacterial attachment for biofilm formation. To understand the interaction between the metal surface and bacteria during initial attachment and biofilm formation. We used three different types of surfaces of Copper (Cu) viz, Bare-Cu, Annealed-Cu, and CzCu, to grow biofilm of a SRB strain, Oleidesulfovibrio alaskensis G20. An anaerobic bioreactor (CDC-Bioreactor) was used to grow the biofilm for 7 days at 30°C. Biofilm was harvested and analyzed using Scanning Electron Microscopy and Confocal Laser Scanning Microscopy. It is observed that the Bare-Cu surface has the highest biofilm, followed by AnCu and CzCu. It aligned with our hypothesis that surface characters affect bacterial interactions, as BaCu has the highest number of grains, followed by AnCu and CzCu, which corresponds to their surface energies. The CzCu has the lowest number (35) of grains. High-quality, biofilm images will be used to understand the biofilm-metal interface interactions, and to develop a Unet AI model. The AI model will integrate the metal characteristics data (grains, grain boundaries, and grain energies) and surface data (before and after the biofilm growth) to predict and design a surface with desired modifications to avoid SRB biofilms.
