Enhanced Direct Crystallization Resolution of Racemates Mediated in Chiral Ionic Liquid Cosolvent Mixtures: Experimental, Statistical, and QM Simulation Studies
Bibliographic record
Abstract
Four chiral ionic liquids (CILs) of 1-hexyl-3-methylimidazolium- l -2-aminobutyrate ([Hmim][ l -2-AbA]), 1-hexyl-3-methylimidazolium- d -2-aminobutyrate ([Hmim][ d -2-AbA]), di-1-hexyl-3-methylimidazolium- l -malate ([Hmim] 2 [ l -MA]), and di-1-hexyl-3-methylimidazolium- d -malate ([Hmim] 2 [ d -MA]) were first prepared by a neutralization method and adequately characterized. In light of a gravimetric method, the molar fraction solubilities of dl - and d - together with l -threonine (Thr) were then acquired in the binary cosolvent mixtures (CM) of CILs and water, with different mass fractions (ω) of CILs under 101.3 kPa from 283.15 to 333.15 K. In CIL cosolvent mixtures (CCM), a cosolvency phenomenon was observed with the biggest solubilization and discrepant solubilities of Thr enantiomers, with ω of 0.3. Meanwhile, the asymmetry phenomenon was also found from the ternary phase diagram of Thr enantiomers and CCM, in which the enantiomeric excess values at the eutectic point (ee eut ) were sensitive to temperature. Subsequently, the nucleation experiments and appearance probability statistics confirmed that CCM with ω of 0.3 (CCM-0.3) can act as excellent nucleation inhibitors which preferred to suppress the nucleation of enantiomeric crystals with the opposite chirality to them, leading to the preferential nucleation of enantiomeric crystals with the same chirality. Furthermore, by coupling seeds and CCM-0.3, the yield and yield ratio of l -Thr products through a direct antisolvent crystallization resolution were improved greatly compared with that in water, where the largest yield ratio was up to 27.47%. At the same time, isosurface diagrams and binding energy calculations by quantum mechanics (QM) revealed that the higher chiral recognition ability of [Hmim] 2 [ l -MA] than [Hmim][ l -2-AbA] stemmed from the stronger weak interactions. Particularly, the enhanced direct crystallization resolution can be feasibly recycled owing to the stability and recyclability of the CILs.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".