Increased Butanol Yields through Cosubstrate Fermentation of Jerusalem Artichoke Tubers and Crude Glycerol by <i>Clostridium pasteurianum</i> DSM 525
Bibliographic record
Abstract
Abstract Clostridium pasteurianum DSM 525 can produce butanol, 1,3-propanediol, and ethanol from glycerol. The product distribution can be tilted toward butanol when adding butyric acid. The strain predominantly produces acetic and butyric acids when grown on saccharides. Hence, butyrate formed from saccharide conversion can be used to stimulate butanol production from glycerol under cosubstrate cultivation. The optimal cosubstrate ratio was determined, and under optimal conditions, a butanol yield and a productivity of 0.27 ± 0.01 gbutanol g–1(glycerol + sugar)–1 and 0.74 ± 0.02 g L–1 h–1 were obtained. On the basis of these results, batch fermentation in a 5 L bioreactor was performed using Jerusalem artichoke hydrolysate (carbohydrate source) and crude glycerol (residue from biodiesel production) at the previously determined optimal condition. A butanol yield and a productivity of 0.28 ± 0.007 gbutanol g(glycerol+sugar)–1 and 0.55 ± 0.008 g L–1 h–1 were achieved after 27 h fermentation, indicating the suitability of those low-cost carbon sources as cosubstrates for butanol production via C. pasteurianum.
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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 | 0.000 |
| 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) | 0.001 | 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".