Fermentative Butanol Production—Perspectives and Scale-Up Challenges
Bibliographic record
Abstract
Sustainable solutions to the use of petrochemical products have been increasingly sought after in recent years. While alternatives such as biofuels have been extensively explored and commercialized, major challenges remain in using heterogeneous feedstocks and scaling-up processes. Among biofuels, higher alcohols have recently gained renewed interest, especially in the context of upcycling agri-food residues and other industrial organic wastes. One of the higher alcohols produced via fermentation is butanol, which was developed over a century ago. However, the commercial production of butanol is still not widespread, although diverse feedstocks are readily available. Hydrolysis of the feedstocks and scale-up challenges in the fermentation and purification of butanol are recurring bottlenecks. This review addresses the current state of fermentative butanol production and opportunities to address scale-up challenges, including purification. With the significant interest and promise of precision fermentation, this review also addresses some of the recent advances and potential for enhanced fermentative butanol production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".