Strategies for Improving Lignocellulosic Butanol Production and Recovery in ABE Fermentation by Tailoring Clostridia Metabolic Perturbations
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it