Preconcentration and determination of ranitidine hydrochloride in real samples by using modified magnetic iron oxide nanoparticles
Bibliographic record
Abstract
This method shows a novel, fast, and simple magnetic solid-phase extraction (SPE) and spectrophotometric procedure for preconcentration and determination of ranitidine hydrochloride in human plasma and aquatic samples by using Fe3O4 nanoparticles (NPs) modified by sodium dodecyl sulfate (SDS) as an extractor. The unique properties of Fe3O4 NPs including high surface area and strong magnetism were utilized effectively in the magnetic SPE process. The determination method is based on the SDS-coated Fe3O4 NPs with extracted ranitidine-HCl, which was subsequently monitored spectrophotometrically at λmax = 320 nm. Effects of different parameters influencing the extraction efficiency of ranitidine-HCl including the pH value, amount of SDS, and Fe3O4 NPs, extraction time, desorption solvent, desorption time, and sample volume were optimized. Under optimized conditions, the method was successfully applied to the extraction of ranitidine-HCl from human plasma and aquatic samples. The extraction recovery in human plasma and different matrixes of waters were investigated and values of 89.0%–103.4% were obtained. The calibration graph for the determination of ranitidine-HCl was linear in the range of 0.025–1.50 μg mL−1 with R2 = 0.9946. The limit of detection of the proposed method was 7.5 × 10−3 μg mL−1. The repeatability and reproducibility (relative standard deviation) of the mentioned method were 0.83% and 1.22%, respectively. The experimental results showed that the proposed method was feasible for the analysis of ranitidine-HCl in environmental and 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 | 0.001 | 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".