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• W4307358429 abstract "The performance of any bio-electrochemical system is dependent on the efficiency of electrode-microbial interactions. Surface properties play a focal role in bacterial attachment and biofilm formation on the electrodes. In addition to electrode surface properties, selective bacterial adhesion onto the electrode surface is mandatory to mitigate energy loss due to undesired bacterial interactions on the electrode surface. In the present study, microbial-patterned graphite scaffolds are developed for selective bacterial-electrode interactions. A power density as high as 1105 mW/m2 is achieved with mG-E (a graphite electrode patterned with Escherichia coli), which is about 3 times higher than that of the pristine graphite electrode (370 mW/m2). Initial mechanical pre-treatment of the graphite electrode, followed by bacterial patterning, results in the formation of a unique cobblestone topography with a tuned surface area of 127.12 m2/g. This provides suitable morphology with enhanced active sites for selective bacterial intercalation in graphite layers. This cannot be otherwise achieved by any mechanical or other means. A unique methodology of symbolic regression is adopted to validate a genetic algorithm suitable for predicting a perfect correlation between surface characteristics and electrochemical characteristics with a minimum root-mean-square error of 0.08. The bacterial intercalation onto the graphite electrode causes protuberance of the graphite layers that reduces the surface potential and resistance, leading to high electron transfer. The study presents a unique bacterial-inspired surface patterning on the anode, which is critical for the performance of a microbial fuel cell." @default.
• W4307358429 created "2022-10-31" @default.
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• W4307358429 date "2022-10-27" @default.
• W4307358429 modified "2023-10-01" @default.
• W4307358429 title "Microbial-Inspired Surface Patterning for Selective Bacterial Actions for Enhanced Performance in Microbial Fuel Cells" @default.
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• W4307358429 doi "https://doi.org/10.1021/acsabm.2c00760" @default.
• W4307358429 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36300364" @default.
• W4307358429 hasPublicationYear "2022" @default.
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