Electrochemically assisted dark fermentation for enhanced hydrogen and butyric acid production from brewery waste slurry
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it