Iron effect on the fermentative metabolism of Clostridium acetobutylicum ATCC 824 using cheese whey as substrate
Bibliographic record
Abstract
Butanol is considered a superior liquid fuel that can replace gasoline in internal combustion engines. It is produced by acetone-butanol-ethanol (ABE) fermentation using various species of solventogenic clostridia. Performance of ABE fermentation process is severely limited mostly by high cost of substrate, substrate inhibition and low solvent tolerance; leading to low product concentrations, low productivity, low yield, and difficulty in controlling culture metabolism. In order to decrease the cost per substrate and exploit a waste generated by dairy industry, this study proposes using cheese whey as substrate for ABE fermentation. It was observed that the addition of an iron source was strictly necessary for the cheese whey to be a viable substrate because this metal is needed to produce ferredoxin, a key protein in the fermentative metabolism of Clostridium acetobutylicum serving as a temporary electron acceptor. Lack of iron in the cheese whey impedes ferredoxin synthesis and therefore, restricts pyruvate-ferredoxin oxidoreductase activity leading to the production of lactic acid instead of acetone, butanol and ethanol. Moreover, the addition of FeSO4 notably improved ABE production performance by increasing butanol content (7.13 ± 1.53 g/L) by 65% compared to that of FeCl3 (4.32 ± 0.94 g/L) under the same fermentation conditions.
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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 | 0.000 | 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".