Rapid Inline Derivatization of Primary and Secondary Amine Containing Drugs by Capillary Electrophoresis with Laser-Induced Fluorescence
Bibliographic record
Abstract
Despite the ongoing “war on drugs” the seizure rates for phenethylamines and their analogues have been steadily increasing over the years. The illicit manufacture of these compounds has become big business all over the world making it all the more attractive to the inexperienced “cook”. However, as a result, the samples produced are more susceptible to contamination with reactionary byproducts and leftover reagents. These impurities are useful in the analysis of seized drugs as their identities can help to determine the synthetic pathway used to make these drugs and thus, the provenance of these analytes. In the present work two fluorescent dyes, 4-fluoro-7-nitrobenzofurazan and 5-(4,6-dichlorotriazinyl)aminofluorescein, were used to label several phenethylamine analogues for electrophoretic separation with laser-induced fluorescence detection. The large scale to which law enforcement is encountering these compounds has the potential to create a tremendous backlog. In order to combat this, a rapid, sensitive method capable of full automation is required. Through the utilization of the inline derivatization method developed whereby analytes are labeled within the capillary efficiently in a minimum span of time, this can be achieved. The derivatization and separation parameters were optimized on the basis of a variety of experimentally determined factors in order to give highly resolved peaks in the fluorescence spectrum with limits of detection in the low µg/mL range.
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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 | 0.000 | 0.000 |
| 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".