Saffron sporopollenin modified with graphene oxide for efficient extraction of acetaminophen from serum using deep eutectic solvent prior to HPLC
Bibliographic record
Abstract
A facile and environmentally benign or “green” analytical method was developed for the extraction of acetaminophen (APAP) from human blood serum. Fe 3 O 4 -saffron stamen sporopollenin/graphene oxide (Fe 3 O 4 -SSSP/GO) and deep eutectic solvent (DES) were employed as natural adsorbent and desorption components, respectively. The structure of magnetic nanoparticles and DES was investigated through Fourier-transformed infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). The main factors influencing the analytical response were optimized by central composite design (CCD). Under the optimum conditions, the linearity of this method ranged from 15.6 to 1500 µg L − 1 and 3500–50,000 µg L − 1 , with coefficient of determinations (R 2 ) of 0.991 and 0.993, respectively. The relative standard deviations (RSDs) were less than 6.3%. The limits of detection (LOD) and the limit of quantification (LOQ) were 5.1 to 15.6 µg L − 1 . The developed method has been successfully applied to the quantification of APAP in spiked samples of blood serum, with recoveries between 81% and 98.2%. The developed method shows significant potential for the bioanalysis of APAP in medical laboratories, given its green characteristics and satisfactory performance metrics.
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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) | 0.000 | 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".