Ultrasonication‐assisted synthesis of molecularly imprinted polymer‐encapsulated magnetic nanoparticles for rapid and selective removal of 17β‐estradiol from aqueous environment
Bibliographic record
Abstract
Abstract Molecularly imprinting technique was combined with magnetic nanoparticles to synthesize molecularly imprinted polymer‐encapsulated particles for selective removal and efficient separation of estrogenic compounds from water. The core‐shell‐structured particles were successfully prepared by a novel 2‐h ultrasonication‐assisted synthesis in a mixture of water and organic solvent using dual‐layer surfactant‐modified magnetic particles as core, the most physiologically active estrogenic compound (17β‐estradiol) as template, and widely adapted methacrylic acid as functional monomer. Ultraviolet–visible spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, and magnetic separation were used to characterize the particles. High‐performance liquid chromatography–tandem mass spectrometry was used for quantitative binding performance analysis at low‐nanogram per milliliter levels. The particles exhibited satisfactory recognition of 17β‐estradiol in water. They possessed great potential for rapid, cost‐effective, and efficient separation of estrogenic compounds from aqueous environment with specificity. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers
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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 | 0.000 | 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".