Enrichment of a High-Current Density Denitrifying Microbial Biocathode
Source abstract
Abstract
The pairing of a nitrate reducing microbial biocathode with an organic matter oxidizing microbial bioanode represents a potential high value wastewater treatment methodology. While such bioanodes are relatively optimized, such biocathodes suffer from relatively low current densities and low operating potentials. Here we present enrichment and characterization of a denitrifying microbial biocathode that generates more than 10-fold greater current density per unit geometric surface area and operates at nearly 0.2 V higher than those previously reported. A mixture of aquatic sediments and denitrifying biomass was first enriched for microbes that reduce nitrate with electrons supplied by oxidation of Fe(II), then enriched for microbes that reduce nitrate with electrons supplied by a graphite electrode. The resulting biocathode exhibited a Nernstian current-potential dependency (onset of current at = −0.125 V vs. Ag/AgCl, limiting current density = −3.2 A/m2). Non-turnover voltammetry exhibited current peaks that scale with the square root of scan rate, consistent with diffusive electron hopping to microbes acting as nitrate reduction catalysts from the electrode surface via endogenous redox cofactors. In non-optimized electrochemical reactors, the biocathode removed 14–40% of influent NO3− without significant production of ammonia-nitrogen (NH3-N), suggesting that reduction of nitrate to nitric or nitrous oxide gas is occurring and that reduction of NO3− to NH3 is not a metabolic pathway. Results of 16S rDNA sequencing revealed a predominance of Betaproteobacteria, including Rhodocyclales and Burkholderiales, known environmental nitrogen cyclers, as the potential microbial cathode catalysts.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microbial Fuel Cells and Bioremediation · Wastewater Treatment and Nitrogen Removal · Electrochemical sensors and biosensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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