Synthesis, transfer, and characterization of core-shell gold-coated magnetic nanoparticles
Bibliographic record
Abstract
Magnetic separation has gained new popularity as a versatile partitioning method with the recent growth in nanotechnology and related biotechnology applications. In this study, iron oxide magnetic nanoparticles were synthesized via solvothermal methods and directly coated with gold to form core-shell gold-coated magnetic nanoparticles (Fe 3 O 4 -AuNPs). High-resolution transmission electron microscopy with Energy dispersive X-ray spectroscopy results suggests that temperature and reaction time play an important role in the formation of small, monodisperse Fe 3 O 4 -AuNPs. We also demonstrate that increased 4- dimethyl(amino)pyridine (DMAP) concentrations and vigorous stirring were required to successfully transfer Fe 3 O 4 -AuNPs into aqueous solution. The structure and morphology of the synthesized and transferred Fe 3 O 4 -AuNPs was further confirmed by UV–vis absorption spectroscopy and solubility experiments. •Direct coating of Fe 3 O 4 with Au: Slowly heating by (10 °C/ min) until 180–190 °C without exceeding this reaction temperature and increasing the reaction time to 3 h from 1.5 h •High yield transfer of Fe 3 O 4 -AuNPs was achieved using 4- dimethyl(amino)pyridine (DMAP) as phase transfer catalyst
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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".