Chiral Separations Using a Modified Water Stationary Phase in Supercritical Fluid Chromatography
Bibliographic record
Abstract
A novel means of achieving chiral separations in supercritical fluid chromatography (SFC) using a water stationary phase is presented. By adding various chiral selectors to the phase, different chiral analytes can be readily separated using neat CO2 as a mobile phase. For example, by adding β-cyclodextrin, it is found that certain flavanone enantiomers can be separated, while using the antibiotic vancomycin as a selector provides separation of some chiral phenoxypropionic acids. Other additives such as sodium chloride and triethylamine are also explored and found to enhance certain separations when also present in the water phase. While column pressure has a moderate impact on chiral analyte retention and separation in this SFC method, column temperature has a comparatively larger influence. In particular, relatively cooler temperatures below about 5 °C are found to markedly increase resolution and selectivity. For instance, notably large resolution of 4.7 is achieved for a phenoxypropionic acid pair at 0 °C and 150 atm CO2. Since the method does not require modifier to elute such polar species, it is also readily compatible with FID detection and does not generate organic waste. Therefore, results indicate that this approach could be a potentially simple and flexible means of achieving chiral separations in SFC. Graphical abstract
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".