Increased Selectivity for Butanol in Clostridium Pasteurianum Fermentations via Butyric Acid Addition or Dual Feedstock Strategy
Bibliographic record
Abstract
Volatility of the petroleum market has renewed research into butanol as an alternate fuel. In order to increase the selectivity for butanol during glycerol fermentation with Clostridium pasteurianum, butyric acid can be added to the medium. In this manuscript, different methods of extracellular butyric acid addition are explored, as well as self-generation of butyric acid fermented from sugars in a co-substrate strategy. Molasses was used as an inexpensive sugar substrate, and the optimal molasses to glycerol ratio was found to allow the butyric acid to be taken back up into the cells and increase the productivity of butanol from all carbon sources. When butyric acid is added directly into the media, there was no significant difference between chemically pure butyric acid, or butyric acid rich supernatant from a separate fermentation. When low concentrations of butyric acid (1 or 2 g/L) are added to the initial media, an inhibitory effect is observed, with no influence on the butanol selectivity. However, when added later to the fermentation, over 1 g/L butyric acid is taken into the cells and increased the relative carbon yield from 0.449 to 0.519 mols carbon in product/mols carbon in substrate. An optimized dual substrate fermentation strategy in a pH-controlled reactor resulted in the relative carbon yield rising from 0.439 when grown on solely glycerol, to 0.480 mols C product/mols C substrate with the dual substrate strategy. An additional benefit is the utilization of a novel source of sugars to produce butanol from C. pasteurianum. The addition of butyric acid, regardless of how it is generated, under the proper conditions can allow for increased selectivity for butanol from all substrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".