An efficient biosorption‐based dispersive liquid‐liquid microextraction with extractant removal by magnetic nanoparticles for quantification of bisphenol A in water samples by gas chromatography‐mass spectrometry detection
Bibliographic record
Abstract
Abstract In this work, a simple, fast, sensitive, and environmentally friendly method was developed for preconcentration and quantitative measurement of bisphenol A in water samples using gas chromatography with mass spectrometry. The preconcentration approach, namely biosorption‐based dispersive liquid‐liquid microextraction with extractant removal by magnetic nanoparticles was performed based on the formation of microdroplet of rhamnolipid biosurfactant throughout the aqueous samples, which accelerates the mass transfer process between the extraction solvent and sample solution. The process is then followed by the application of magnetic nanoparticles for easy retrieval of the analyte‐containing extraction solvent. Several important variables were optimized comprehensively including type of disperser solvent and desorption solvent, rhamnolipid concentration, volume of disperser solvent, amount of magnetic nanoparticles, extraction time, desorption time, ionic strength, and sample pH. Under the optimized microextraction and gas chromatography with mass spectrometry conditions, the method demonstrated good linearity over the range of 0.5–500 µg/L with a coefficient of determination of R2 = 0.9904, low limit of detection (0.15 µg/L) and limit of quantification (0.50 µg/L) of bisphenol A, good analyte recoveries (84–120%) and acceptable relative standard deviation (1.8–14.9%, n = 6). The proposed method was successfully applied to three environmental water samples, and bisphenol A was detected in all 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".