Kanamycin as a New Chiral Selector in Enantiomeric Separation of Hydrochloric Dipivefrin by Capillary Electrophoresis
Bibliographic record
Abstract
Kanamycin(KAN)as a chiral selector for the enantiomeric separation of hydrochloric dipivefrin(HD)by capillary electrophoresis(CE)was reported.The influences of the chiral selector concentration,buffer pH,capillary temperature,and the additives of organic modifier on the enantioseparation were all investigated and the separation conditions were optimized.The optimal separation conditions were as following.The concentration of KAN was 0.6%,the concentration of organic modifier was 5% and a mixture of acetonitrile and methanol(9∶1,V/V)was used as the modifier.Phosphate buffer with pH 2was used.The injection amount of HD was 25psi for 2s.The applied voltage on separation and capillary temperature were + 20kV and 25 ℃,respectively.The detection waveleagth was 262nm.The results show that capillary electrophoresis is a fast and efficient separation method for enantioseparation of chiral drugs and that KAN is a viable chiral selector in CE for the separation of HD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".