Acetone–Butanol–Ethanol Production from Eastern Canadian Yellow Birch and Screening of Isopropanol–Butanol–Ethanol-Producing Strains
Bibliographic record
Abstract
Yellow birch barks is one of the abundant species in Quebec with harvest surplus in several regions. Biofuels or biochemicals such as biobutanol can be produced using the surplus feedstock, however challenges such as the cost of pretreatment, production of unwanted by-products in the fermentation process, and the efficient recovery of solvents must be addressed to make it feasible. The objectives of this study are to establish the optimal conditions to produce biobutanol from Eastern Canadian yellow birch; to identify natural/local Clostridium sp. strains that are capable of producing Isopropanol-Butanol-Ethanol (IBE) from synthetic sugar mixtures, as candidates for metabolic engineering and to benchmark solvent producing ability with commercially available strains; and to elucidate the challenges of paradigm shift to IBE production. Alkali pretreatment of the biomass using chemical that are present in the Kraft process were performed, followed by enzymatic hydrolysis to obtain fermentable sugars and subsequent fermentation with Clostridium acetobutylicum DSM 792. The results showed that the produced Acetone-Butanol-Ethanol (ABE) solvent concentration were 6.6–8.2 g/L of acetone; 11.2–13.1 g/L of butanol; and 2.5–2.7 g/L of ethanol. The organic acids concentration was acetic acid, 1.1–1.8 g/L, and butyric acid, 0.1–0.2 g/L. Further fermentation experiments to benchmark IBE were performed using both Clostridium beijerinckii DSM 6423 and wild isolated strains, which revealed the gaps in terms of yields and the need to optimize the fermentation paradigm. Moreover, alternative process sequences for product recovery were identified, and the impact of prior liquid-liquid extraction elucidated.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".