Electrochemically assisted dark fermentation for enhanced hydrogen and butyric acid production from brewery waste slurry
Bibliographic record
Abstract
Hydrogen production from wastewater treatment with microbial electrolysis has developed rapidly over the last two decades. However, much remains to be explored regarding the combined use of electrochemical techniques and dark fermentation (DF) using brewery effluents with high organic content. This study investigates a sequential DF-microbial electrolysis cells treatment (DF-MEC), and a DF process functioning as an electro-fermentation (DEF), aiming to improve hydrogen production utilizing a substrate with an unprecedent chemical oxygen demand (~60 g L −1 ), based on a brewery waste slurry (BWS). Both anodes and cathodes were polyaniline-modified carbon felt electrodes. The DF-MEC did not show any significant improvement. However, hydrogen was produced 1.6-fold more compared to a process without applied current. The production rose 95 % of the theoretical hydrogen-to-substrate molar yield. The applied voltage (0.4 V) suppressed the activity of methanogens while favouring the growth of hydrogen-producing species, such as Clostridium butyricum , which alone constituted 44.8 % of the microbial population. The electric current induced a shift in the DEF metabolism in the second half of the process, attaining a production of 17.9 g L −1 of butyrate from the conversion of the lactate formed in the initial hours. Hydrogen was generated mostly as a by-product of the butyrate formation. Consequently, the DEF process required only one fifth of the energy input per kg of hydrogen compared to commercial electrolyzers, since hydrogen production was mostly supported by the microbial metabolism. This reaffirms the potential of the innovative electro-fermentation approach as an attractive alternative to produce green hydrogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".