Magnetic mixed hemimicelle solid-phase extraction based on mixed hemi-/ad-micelle SDS-coated magnetic nanoparticles Fe<sub>2-</sub><sub><i>x</i></sub>Al<sub><i>x</i></sub>O<sub>3</sub> (<i>x</i> = 0.4) for the fluorimetric determination of carvedilol in biological samples
Bibliographic record
Abstract
Mixed hemi-/ad-micelle SDS-coated magnetic nanoparticles (Fe2-xAlxO3 (x = 0.4)) were used as an efficient adsorbent for the extraction and preconcentration of carvedilol (CVD) based on magnetic mixed hemimicelle solid-phase extraction. The Fe2-xAlxO3 magnetic nanoparticles not only have better stability and resistance to acidity, as well as alkalinity, but also are easy to prepare, inexpensive, and environmentally friendly. Several parameters that affected the extraction efficiency were investigated, including the type and volume of desorption solvent, extraction and desorption times, pH of the solution, zeta potential, and amounts of adsorbent and surfactant. Under the optimized extraction conditions, the developed method showed good linearity (R2 = 0.9998) within the range of 0.02–2.7 ng mL−1, and the limit of detection was 0.009 ng mL−1. The spiked recoveries of CVD in urine and plasma samples ranged from 101.50% to 111.00%. To the best of our knowledge, this is the first time that a mixed hemi-/ad-micelle solid-phase extraction method based on magnetic separation and nanoparticles has been used as a simple and sensitive method for the monitoring of CVD in biological samples.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".