Analyzing the Effects of Electrode Conductivity and Effective Bacterial Surface Area on Predicted Current Density in Microbial Fuel Cells

Microbial fuel cells (MFCs) have gained attention as a sustainable technology because they can simultaneously generate electricity and treat organic waste through the metabolic activity of electroactive bacteria. This study developed a modified Nernst-Monod computational model to investigate how electrode conductivity and effective bacterial surface area influence the normalized current-density response in Geobacter sulfurreducens MFCs under saturation conditions. Peer-reviewed literature and published experimental studies were used to develop and evaluate the computational framework. The effects of electrode properties on predicted MFC performance were analyzed through heatmaps, three-dimensional response surfaces, sensitivity analyses, Monte Carlo uncertainty analysis, and qualitative comparison with a published experimental dataset. The model predicted that increasing electrode conductivity and effective bacterial surface area increased the normalized current-density response, although the rate of improvement diminished at higher parameter values due to saturation behavior incorporated into the modified computational framework. The Monte Carlo simulations showed that the model produced consistent trends despite random variation of conductivity and surface area, suggesting that the computational framework was relatively stable under simulated uncertainty. A qualitative comparison with a published experimental dataset matched similar trends predicted by the model. These findings demonstrate that extending the Nernst-Monod framework with electrode conductivity and effective bacterial surface area provides a useful computational approach for studying the individual and combined influences on normalized current-density response. This framework can help scientists generate hypotheses and guide future experimental investigations.