Strategies for Improving Lignocellulosic Butanol Production and Recovery in ABE Fermentation by Tailoring Clostridia Metabolic Perturbations
Bibliographic record
Abstract
The present study investigates approaches to enhance bio-butanol production using lignocellulosic feedstock via supplements of metabolism perturbation. Traditionally, bio-butanol has been produced through chemical synthesis in a process known as acetone–butanol–ethanol (ABE) fermentation. Today, biochemical techniques involving bacterial strains capable of producing butanol are used with renewable sources of biomass. In this study, a stepwise approach was tailored for metabolic perturbations to maximize butanol production from pure sugar and lignocellulosic feedstock as a reference model fermentation. In preliminary investigations, impacts of CaCO3, furfural and methyl red on cell growth, sugar utilization, acid production and butanol production were evaluated in glucose feedstock and xylose feedstock. Following the preliminary investigation, with supplementation of 4 g/L CaCO3, the concentrations of furan derivatives (75% furfural and 25% HMF) and ZnSO4 were optimized for maximal butanol production from glucose and xylose feedstocks, respectively. A final experiment of butanol production was concluded using lignocellulosic feedstock hydrolysate normally containing 0.5~1.5 g/L furan derivatives under optimized conditions of 2 mg/L ZnSO4 and 4 g/L CaCO3. Under optimized conditions, butanol production exceeded 10 g/L in wheat straw hydrolysate, which was significantly higher than that obtained in the absence of ZnSO4 and CaCO3. As compared to the traditional lignocellulosic feedstock post-treatment method, the metabolic perturbations method shows advantages in terms of productivity and economics.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".