Optimization of Influential Nutrients during Direct Cellulose Fermentation into Hydrogen by Clostridium thermocellum
Bibliographic record
Abstract
Combinatorial effects of influential growth nutrients were investigated in order to enhance hydrogen (H2) production during direct conversion of cellulose by Clostridium thermocellum DSM 1237. A central composite face-centered design and response surface methodology (RSM) were applied to optimize concentrations of cellulose, yeast extract (YE), and magnesium chloride (Mg) in culture. The overall optimum composition generated by the desirability function resulted in 57.28 mmol H2/L-culture with 1.30 mol H2/mol glucose and 7.48 mmol/(g·cell·h) when cultures contained 25 g/L cellulose, 2 g/L YE, and 1.75 g/L Mg. Compared with the unaltered medium, the optimized medium produced approximately 3.2-fold more H2 within the same time-frame with 50% higher specific productivity, which are also better than previously reported values from similar studies. Nutrient composition that diverted carbon and electron flux away from H2 promoting ethanol production was also determined. This study represents the first investigation dealing with multifactor optimization with RSM for H2 production during direct cellulose fermentation.
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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) | 0.001 | 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".