Biohydrogen production by<i>Clostridium beijerinckii</i>
Bibliographic record
Abstract
Climate change, along with the rapid depletion of petroleum and natural gas reserves, has prompted many to search for renewable and environmentally friendly energy options.Hydrogen has been identified as a possible alternative to fossil fuel energy.Biological hydrogen production from organic substrates can be achieved by a two-stage approach combining the anaerobic and photosynthetic continuous processes in series.As a first step, an investigation of the production of hydrogen from glucose by Clostridium beijerinckii was conducted.A study examining the effect of initial pH (range 5.7 to 6.5) and COD loading (range 1 to 3 g/L) on the specific conversion and specific hydrogen production rate has shown interaction behaviour between the two independent variables.The highest conversion of 10.3 mL H 2 /(g COD/L) was achieved at pH of 6.1 and COD of 3 g/L, whereas the highest production rate of 71 mL H 2 /(h*L) was measured at pH 6.3 and substrate loading of 2.5 g COD/L.In general, there appears to be a strong trend of increasing hydrogen production rate with an increase in both substrate concentration and pH.The current work focuses on the identification of soluble metabolites such as ethanol, acetate and butyrate in order to evaluate the possible shift in metabolism resulting from varying initial conditions and micro-nutrients availability.The results obtained so far, along with some preliminary experiments using industrial wastewater as substrate, indicate the possible use of such waste streams for the production of biohydrogen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".