Spontaneously Formed Interfacial Metal Silicates and Their Effect on the Magnetism of Superparamagnetic FeCo/SiO<sub>2</sub>Core/Shell Nanoparticles
Bibliographic record
Abstract
The integration of superparamagnetic core/shell nanoparticles into devices and other nanoscale technological applications requires a detailed understanding of how the intimate contact between core and shell nanophases affects the magnetism. We report how, for single-domain FeCo nanoparticles, an FeCo phase unique to the nanoscale with silica shells of increasing thicknesses spontaneously formed interfacial metal silicates between the core and shell (such as Fe2SiO4 and Co2SiO4) and altered the overall magnetism of the nanomaterial significantly. The influence of this previously overlooked phenomenon on magnetic properties is reported. Evidence of these metal silicate interfacial layers was observed by X-ray absorption spectroscopy (XAS) collected over the L3,2 absorption edges of Fe and X-ray photoelectron spectra (XPS) collected over the 2p transitions of Fe and Co. Through the correlation of magnetometry and XPS data, the evolution of nanoparticle magnetic anisotropy is shown to increase with the metal silicate.
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) | 0.000 | 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) | 0.001 | 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".