Efficient Low-Cost Procedure for Microextraction of Estrogen from Environmental Water Using Magnetic Ionic Liquids
Bibliographic record
Abstract
In this study, three magnetic ionic liquids (MILs) were investigated for extraction of four estrogens, i.e., estrone (E1), estradiol (E2), estriol (E3), and ethinylestradiol (EE2), from environmental water. The cation trihexyl(tetradecyl)phosphonium ([P66614]+), selected to confer hydrophobicity to the resulting MIL, was combined with tetrachloroferrate(III), ferricyanide, and dysprosium thiocyanate to yield ([P66614][FeCl4]), ([P66614]3[Fe(CN)6]), and ([P66614]5[Dy(SCN)8]), respectively. After evaluation of various strategies to develop a liquid–liquid microextraction technique based on synthesized MILs, we placed the MILs onto a magnetic stir bar and used them as extracting solvents. After extraction, the MIL-enriched phase was dissolved in methanol and injected into an HPLC–UV for qualitative and quantitative analysis. An experimental design was used to simultaneously evaluate the effect of select variables and optimization of extraction conditions to maximize the recovery of the analytes. Under optimum conditions, limits of detection were in the range of 0.2 (for E3 and E2) and 0.5 μg L−1 (for E1), and calibration curves exhibited linearity in the range of 1–1000 μg L−1 with correlation coefficients higher than 0.998. The percent relative standard deviation (RSD) was below 5.0%. Finally, this method was used to determine concentration of estrogens in real lake and sewage water samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".