Enhanced Green Production of Biobutanol by Novel Fusants of Two and Three Clostridia
Bibliographic record
Abstract
Protoplast fusion, which is a novel genetic engineering approach, was developed between mesophilic and thermophilic butanol producing bacteria to enhance production of biobutanol as a green energy resource. Three strains of anaerobic gram-positive clostridia were fused through a protoplast fusion technique to produce biobutanol from wheat straw as a feedstock during the process of Simultaneous Saccharification and Fermentation (SSF). These strains have the natural enzymatic ability for biobutanol production, and include Clostridium beijerinckii (ATCC BA101), Clostridium thermocellum, and Thermoanaerobacterium saccharolyticum. The objective of the present study was to increase enzymatic activity during saccharification by raising the temperature of fermentation to increase biobutanol production. Results showed that protoplast fusion of thermophilic and mesophilic clostridia have led to improving thermostability in a fermentation medium at 45°C. This represents the optimum temperature for enzymatic hydrolysis. Results also showed that the fused strain produced essential hydrolysis enzymes, which eliminated the need to add any enzymes during the hydrolysis step. Furthermore, results in the present study demonstrated that the fused culture of bacteria was able to tolerate the elevated concentration of acetone, butanol, and ethanol during production, which resulted in higher biobutanol production of 13.8 g/L. This study included a comparison to the coculture as a benchmark to account for the effects of protoplast fusion.
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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) | 0.000 | 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 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".