Using magnetic core‐shell nanoparticles coated with an ionic liquid dispersion assisted by effervescence powder for the micro‐solid‐phase extraction of four beta blockers from human plasma by ultra high performance liquid chromatography with mass spectrometry detection
Bibliographic record
Abstract
has been created and functionalized with 1-butyl-3-methylimidazolium hexafluorophosphate as an ionic liquid. This sorbent was applied for microextraction of four beta blockers, propranolol, metoprolol, atenolol, and alprenolol with bupivacaine as internal standard from human plasma followed by liquid chromatography with mass spectrometric detection. A mixture of sodium bicarbonate and sodium dihydrogen phosphate was used as an extractant dispersive agent (effervescent power) to enhance the interaction between the magnetic sorbent and analytes. Main affecting parameters on microextraction and elution were optimized. Figures of merit for dispersive solid phase extraction with ionic liquid coated magnetic nanoparticles assisted by effervescent powder were calculated under the optimized conditions. The detection limits for propranolol, metoprolol, atenolol, and alprenolol were found at 0.33, 0.62, 0.03, and 0.44 ng/mL, respectively. For all analytes, good linearity was obtained. Intra- (n = 5) and interday (n = 10) precision were both under 6.3% while the preconcentration factors were obtained in the range between 15-18. The extraction efficiencies for each analyte ranged from 75 to 91%. The method was successfully applied for determination of trace amounts of the beta blockers in human plasma 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) | 0.000 | 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 | 0.000 |
| 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".