Spin-glass-like freezing of inner and outer surface layers in hollow γ-Fe2O3 nanoparticles
Bibliographic record
Abstract
Disorder among surface spins is a dominant factor in the magnetic response of magnetic nanoparticle systems. In this work, we examine time-dependent magnetization in high-quality, monodisperse hollow maghemite nanoparticles (NPs) with a 14.8 ± 0.5 nm outer diameter and enhanced surface-to-volume ratio. The nanoparticle ensemble exhibits spin-glass-like signatures in dc magnetic aging and memory protocols and ac magnetic susceptibility. The dynamics of the system slow near 50 K, and become frozen on experimental time scales below 20 K. Remanence curves indicate the development of magnetic irreversibility concurrent with the freezing of the spin dynamics. A strong exchange-bias effect and its training behavior point to highly frustrated surface spins that rearrange much more slowly than interior spins. Monte Carlo simulations of a hollow particle corroborate strongly disordered surface layers with complex energy landscapes that underlie both glass-like dynamics and magnetic irreversibility. Calculated hysteresis loops reveal that magnetic behavior is not identical at the inner and outer surfaces, with spins at the outer surface layer of the 15 nm hollow particles exhibiting a higher degree of frustration. Our combined experimental and simulated results shed light on the origin of spin-glass-like phenomena and the important role played by the surface spins in magnetic hollow nanostructures.
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) | 0.000 | 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".