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Meta-analysis of Microbial Fuel Cells Using Waste Substrates

Dowdy, F.Ryan, Kawakita, Ryan, Lange, Matthew, Simmons, ChristopherW.
Applied biochemistry and biotechnology 2018 v.185 no.1 pp. 221-232
chemical oxygen demand, data collection, electric power, meta-analysis, microbial fuel cells, models, regression analysis, wastes
Microbial fuel cell experimentation using waste streams is an increasingly popular field of study. One obstacle to comparing studies has been the lack of consistent conventions for reporting results such that meta-analysis can be used for large groups of experiments. Here, 134 unique microbial fuel cell experiments using waste substrates were compiled for analysis. Findings include that coulombic efficiency correlates positively with volumetric power density (p < 0.001), negatively with working volume (p < 0.05), and positively with percentage removal of chemical oxygen demand (p < 0.005). Power density in mW/m² correlates positively with chemical oxygen demand loading (p < 0.005), and positively with maximum open-circuit voltage (p < 0.05). Finally, single-chamber versus double-chamber reactor configurations differ significantly in maximum open-circuit voltage (p < 0.005). Multiple linear regression to predict either power density or maximum open-circuit voltage produced no significant models due to the amount of multicollinearity between predictor variables. Results indicate that statistically relevant conclusions can be drawn from large microbial fuel cell datasets. Recommendations for future consistency in reporting results following a MIAMFCE convention (Minimum Information About a Microbial Fuel Cell Experiment) are included.