An innovative and cost-effective method for hydrogen production from wastewater using a membraneless bioelectrolysis system
Bibliographic record
Abstract
This study presents the development of a novel membraneless microbial electrolysis cell system for biohydrogen production using domestic anaerobic wastewater and sludge as the substrate and inoculum. The experimental design analyzed the effects of sludge/wastewater ratios ranging from 0.2 to 1.0, applied voltages between 1.0 V and 2.0 V, temperatures from 20°C to 60°C, and pH levels of 4, 7, and 10. Optimal conditions for biohydrogen production were identified as a sludge to wastewater ratio of 1.0, a voltage of 2.0 V, a temperature of 40°C, and a neutral pH, resulting in a peak biohydrogen yield of 862.792 mg/L. At the lowest sludge/wastewater ratio of 0.2, hydrogen production remained below 200 mg/L. Production increased to the range of 500 to 600 mg/L at a ratio of 0.6 and reached 800 mg/L at a ratio of 1.0. The impact of temperature was evident, with biohydrogen output rising from 300 mg/L at 20°C to 600 mg/L at 40°C, then stabilizing at higher temperatures. The applied voltage substantially affected hydrogen production, with yields below 400 mg/L at 1.0 V, increasing to 600 mg/L at 1.6 V, and reaching a maximum at 2.0 V. The highest biohydrogen production was achieved at neutral pH, while acidic and alkaline conditions reduced yields to below 200 mg/L. These findings underscore the importance of optimizing operational parameters to maximize biohydrogen production while effectively treating wastewater. The study also highlights the potential of integrating sustainable waste to energy technologies in wastewater management, advancing renewable energy production through process optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".