Micellar Electrokinetic Chromatography with Laser-Induced Fluorescence Detection for Separation of Red and Yellow Historical Dyes
Bibliographic record
Abstract
In this study, the separation parameters in micellar electrokinetic chromatography (MEKC)-laser-induced fluorescence (LIF) were optimized for the separation of red and yellow historical dyes in 20 mM borate buffer with 20 mM sodium dodecyl sulfate (SDS). Separation conditions were optimized by changing pH, organic modifier (methanol and acetonitrile) concentrations and applied voltage. The mixtures of dyes used in this study included four anthraquinone dyes (alizarin, purpurin, emodin and carmine) and six flavonoid dyes (luteolin, apigenin, kaempferol, quercetin, morin and myricetin). For this work, dyes were introduced electro-kinetically (10 kV for 5 s) into a 50 cm capillary (10 µm id) and separated using a running potential of 18, 20, 22 and 25 kV. Absolute limits of detection for most of these dyes was less than 1 pg. For dyes such as alizarin, improved detection limits were achieved at pH = 9.24; however dyes such as purpurin had significantly improved detection limits at pH = 8.0. The successful extraction and identification of a number of dyes in plants and textiles samples is also described.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".