A molecularly imprinted polymer coated-nanocomposite of magnetic nanoparticles for organic compounds recognition
Bibliographic record
Abstract
Following article presents an efficient method for the core-shell systems preparation, composed of magnetic nanoparticles modified with molecularly imprinted polymers (mag-MIP). Obtained mag-MIP were utilized for pre-concentration and trace analysis of organic compounds in real samples. Superparamagnetic iron oxide nanoparticles (SPION) modified with TEOS (tetraethoxysilane) and MPS propyl methacrylate) were used as the magnetic core, EGDMA (ethylene glycol dimethacrylate) was used as a cross-linking agent and AIBN (2,2-azobisisobutyronitrile) as a thermal polymerisation initiator. Quercetin, estrone and -estradiol were used as templates for which the appropriate monomers were chosen. Mag-MIP were successfully applied for the determination of chemical compounds in environmental samples. Quercetin, estrone and -estradiol were adsorbed from their solutions onto the surface of functionalised SPION. Subsequently, mag-MIP were attracted by magnets immersed in the solutions and analysed via electrospray ionization mass spectrometry (ESI-MS) and flowing atmospheric pressure afterglow mass spectrometry (FAPA-MS) combined with thermally initiated desorption. The desorption occurs concomitantly with the plasma stream ioniaztion of the molecules, which are then transported to the analyser. FAPA-MS combined with mag-MIP is a novel analytical method suitable for trace detection from highly heterogeneous solutions. The combination of analyte pre-concentration with mag-MIP followed by FAPA-MS analysis significantly reduced limit of detection (LOD) for several trace analyses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".