Separation and determination of chiral composition in penicillamine tablets by capillary electrophoresis in a broad p<scp>H</scp> range
Bibliographic record
Abstract
A chiral capillary electrophoretic method with nearly full pH window was explored for the separation and determination of dl-penicillamine. A facile one-pot labeling technique was coupled in the method for introduction of chromophore and charge groups onto the analytes to facilitate the electromigration and sensitive detection. By using simply a cost-effective neutral β-cyclodextrin as chiral selector, baseline separation of the dl-penicillamine was achieved from pH 2.0 to over pH 10. Quantification of standard d- and l-penicillamines was demonstrated by taking pH 4.5, 7.4, and 9.7 as the representatives of acidic, neutral, and basic conditions. The working curves were constructed between peak area and concentration, having linear ranges of 8.56-8.56 × 10(2) μg/mL for pH 4.5 and 8.56-1.71 × 10(3) μg/mL for pH 7.4 and 9.7, with correlation coefficients all better than 0.999. The limit of detection (S/N = 3) was 2.58 μg/mL in acidic and neutral conditions or 1.41 μg/mL in basic condition. The method was further validated by assaying the commercial penicillamine tablets, applicable to quantification of the effective enantiomer and the trace impurity of l-penicillamine at a content of down to 0.2, 0.6, and 2.0% for pH 9.7, 4.5, and 7.4, respectively. The recovery determined by spiking technique was in a range from 93.1 to 105 %. The method is easily extendable to the analysis of other chiral amines or amino acids.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".