Aromatics extraction from pyrolytic sugars using ionic liquid to enhance sugar fermentability
Bibliographic record
Abstract
Fermentative bioethanol production from pyrolytic sugars was improved via aromatics removal by liquid-liquid extraction. As solvents, the ionic liquid (IL) trihexyltetradecylphosphonium dicyanamide (P666,14[N(CN)2]) and ethyl acetate (EA) were compared. Two pyrolytic sugar solutions were created from acid-leached and untreated pinewood, with levoglucosan contents (most abundant sugar) of 29.0% and 8.3% (w/w), respectively. In a single stage extraction, 70% of the aromatics were effectively removed by P666,14[N(CN)2] and 50% by EA, while no levoglucosan was extracted. The IL was regenerated by vacuum evaporation (100mbar) at 220°C, followed by extraction of aromatics from fresh pyrolytic sugar solutions. Regenerated IL extracted aromatics with similar extraction efficiency as the fresh IL, and the purified sugar fraction from pretreated pinewood was hydrolyzed to glucose and fermented to ethanol, yielding 0.46g ethanol/(g glucose), close to the theoretical maximum yield.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".