Improved Preparation and Use of Room-Temperature Ionic Liquids in Lipase-Catalyzed Enantio- and Regioselective Acylations
Bibliographic record
Abstract
Polar organic solvents such as methanol or N-methylformamide inactivate lipases. Although ionic liquids such as 3-alkyl-1-methylimidazolium tetrafluoroborates have polarities similar to these polar organic solvents, they do not inactivate lipases. To get reliable lipase-catalyzed reactions in ionic liquids, we modified their preparation by adding a wash with aqueous sodium carbonate. Lipase-catalyzed reactions that previously did not occur in untreated ionic liquids now occur at rates comparable to those in nonpolar organic solvents such as toluene. Acetylation of 1-phenylethanol catalyzed by lipase from Pseudomonas cepacia (PCL) was as fast and as enantioselective in ionic liquids as in toluene. Ionic liquids permit reactions in a more polar solvent than previously possible. Acetylation of glucose catalyzed by lipase B from Candida antarctica (CAL-B) was more regioselective in ionic liquids because glucose is up to one hundred times more soluble in ionic liquids. Acetylation of insoluble glucose in organic solvents yielded the more soluble 6-O-acetyl glucose, which underwent further acetylation to give 3,6-O-diacetyl glucose (2-3:1 mixture). However, acetylation of glucose in ionic liquids yielded only 6-O-acetyl glucose (>13:1 and up to >50:1).
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".