Superparamagnetic Fe<i><sub>x</sub></i>O<i><sub>y</sub></i>@SiO<sub>2</sub> Core−Shell Nanostructures: Controlled Synthesis and Magnetic Characterization
Bibliographic record
Abstract
We present the results of controlled synthesis of core−shell nanostructures and the influence of silica coating on the magnetic properties of formed hybrid nanoparticles. The core−shell nanoarchitectures composed of magnetic iron oxide cores and amorphous silica shells have been synthesized through a sol−gel approach and characterized by transmission electron microscopy, energy dispersive X-ray analysis, and magnetometry. It is found that many of the hybrid nanoparticles contain a single core. A good control of the silica shell thickness (10−100 nm) has been achieved by adjusting the silane concentration. From temperature-dependent zero-field-cooled (ZFC) and field-cooled (FC) magnetization measurements and ZFC model study, it is found that the mean deblocking temperature and effective anisotropy constant remain similar after the iron oxide nanoparticles are coated with about 12 nm thick silica shells. However, the ZFC peak temperature and the ZFC/FC branching point decrease significantly by over 100 K. To understand these interesting phenomena, a specific sample whose surface is modified with a small amount of tetraethoxysilane was prepared. By studying the ZFC/FC and AC behavior of these three kinds of samples, relative contribution from surface anisotropy and magnetic interparticle interactions to the blocking behavior was evaluated. The magnetic core size effect is studied also by comparing two core−shell samples with slightly different core sizes.
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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".