Superparamagnetic Iron Oxide Nanoparticles (SPIONs) as Cores forMolecularly Imprinted Polymers (MIP) in Trace Analysis
Bibliographic record
Abstract
The following article presents an effective method of core-shell systems preparation, utilizing superparamagnetic iron oxide nanoparticles (SPIONs) as the core.In this research, various molecularly imprinted polymers (MIP) were used as the shell.Obtained system combines magnetic properties of the iron oxide nanoparticles and selective analytical properties of the polymeric coating.Resulting magnetic molecularly imprinted polymers (mag-MIP) were used for initial concentration and trace analysis of organic compounds in environmental samples.SPIONs modified with TEOS (tetraethoxysilane) and MPS (3-(trimethoxysilyl) propyl methacrylate) were used as a magnetic core.EGDMA (ethylene glycol dimethacrylate) and AIBN (2,2'-azobisisobutyronitrile) were used as a crosslinking agent in thermal polymerization.Different classes of compounds were used as polymer matrices: flavonoids, herbicides, pesticides, hormones, for which the appropriate monomers were selected.Mag-MIP was successfully used to determine all tested chemicals in environmental samples.Trace amounts of analytes were adsorbed from their solutions onto the surface of functionalised SPIONs.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.Mag-MIP combined with FAPA-MS is a novel analytical method suitable for trace detection from highly heterogeneous solutions.The combination of an analyte pre-concentration with mag-MIP followed by FAPA-MS analysis significantly reduced limit of detection (LOD) for all trace analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".