Surfactant-Modified Dispersive Liquid–Liquid Microextraction for the Determination of Salbutamol or Dapsone via Reciprocal Derivatization
Bibliographic record
Abstract
Salbutamol (SAL) and dapsone (DPSN) derivatization reactions and analysis procedures were established and improved in this study. SAL was used as a derivatization agent for DPSN, and DPSN was a derivatization agent for SAL. The method of derivatization, the diazonium coupling of SAL and DPSN, was optimized. The optimum parameters were 12 mM hydrochloric acid and 2.4 mM sodium nitrite for diazotization and 24 mM potassium hydroxide for azo coupling. Surfactant-modified dispersive liquid‒liquid microextraction (SM-DLLME) was optimized using a new solvent mixture: an aqueous dye derivative sample mixture, 600 µL of butanol as the extracting solvent, 1.05 wt.% Triton X-100 as the dispersive solvent, and 42 mM potassium chloride. Compared to existing methods, the linearity, correlation coefficients, limits of detection, and molar absorptivities were improved when SM-DLLME followed by HPLC-UV was employed. The proposed technique can detect pharmacological, environmental, and medicinal trace concentrations of SAL and DPSN.
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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 | 0.000 | 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".