Exchange bias in a columnar nanocrystalline<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="normal">Ni</mml:mi><mml:mn>80</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">Fe</mml:mi><mml:mn>20</mml:mn></mml:msub><mml:mo>∕</mml:mo><mml:mi mathvariant="normal">Co</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow></mml:math>thin film
Bibliographic record
Abstract
The effects of interfacial coupling at the boundary of ferromagnetic and antiferromagnetic components in a nanoscale columnar-structured thin film of ${\mathrm{Ni}}_{80}{\mathrm{Fe}}_{20}∕\mathrm{Co}\mathrm{O}$ have been examined. Field-cooling the film results in very different temperature dependences of the enhanced coercivity and exchange-bias shift of the hysteresis loop. The exchange-bias temperature dependence is well described by thermal fluctuations of the interfacial spins while the coercivity temperature dependence indicates that single-domain-like columns are being coherently rotated by the thermal fluctuations of the interface spins. Furthermore, only a portion of the spins in the antiferromagnetic layer seem to be associated with the spin coupling that results in exchange bias. X-ray magnetic resonant scattering measurements show clearly the presence of canted Co interfacial moments that provide a local field which enables exchange bias at a significantly higher temperature than the onset of an enhanced coercivity.
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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) | 0.002 | 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".