Back

Hydrogel-enhanced bioelectrochemical nitrate reduction for ammonium recovery from dilute nitrate via Shewanella oneidensis MR-1

Oshiki, M.; Choi, Y.; Shinto, R.; Okabe, S.

2026-07-31 microbiology
10.64898/2026.07.31.742018 bioRxiv
Show abstract

Bioelectrochemical reduction of dilute nitrate (NO-; sub-mM to low-mM range) to ammonium (NH) offers a promising route toward circular nitrogen management from contaminated groundwater and environmental waters. However, on-site application of bioelectrochemical systems remains challenging due to low reduction rates and poor electron transfer efficiency of naturally formed biofilm electrodes. Here, we constructed a hydrogel biocathode by applying a carbon black/riboflavin/sodium alginate/cellulose hydrogel incorporating Shewanella oneidensis MR-1 cells to a graphite felt electrode via brush coating. The hydrogel electrode achieved NH production rates of 0.16-0.19 mol m-3 h-{superscript 1} without NO2- accumulation, and these rates were maintained without significant performance loss across three consecutive cycles with medium exchange over 1.5 days of total operation. The hydrogel electrode increased the current density by more than 5-fold compared with a conventional S. oneidensis biofilm electrode, indicating enhanced electron transfer efficiency per unit biomass, which directly contributed to the high NH production rates. The electricity consumption for NH production of 1.68-2.39 x 10{superscript 2} kJ g-N-{superscript 1} was substantially lower than that of metal catalyst systems at comparable NO- concentrations (typically, >104 kJ g-N-{superscript 1}). These findings demonstrate that the hydrogel electrode design represents an energy-efficient, and readily fabricated platform for bioelectrochemical NH production from dilute NO-.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.