The magnetic interfacial properties of an exchange biased nanocrystalline Ni <sub>80</sub> Fe <sub>20</sub> / <i>α</i> -Fe <sub>2</sub> O <sub>3</sub> bilayer studied by polarized neutron reflectometry and Monte Carlo simulation
Bibliographic record
Abstract
Abstract The strength of exchange bias can be influenced by interface roughness and antiferromagnetic morphology. Here, we studied the interface profile of an exchange biased, nanocrystalline Ni 80 Fe 20 / α -Fe 2 O 3 bilayer. Magnetometry determined the bilayer’s exchange bias is observed below a blocking temperature of 75 K. Polarized neutron reflectometry measurements revealed the Ni 80 Fe 20 layer was fully saturated to yield a net-moment of 0.95 μ B /atom, while the majority of the Fe 2 O 3 layer exhibited zero net-magnetization with the exception of the interfacial region with an uncompensated moment between 0.5 and 1.0 μ B /Fe 2 O 3. Monte Carlo simulations of a ferromagnetic/antiferromagnetic bilayer incorporating a granular antiferromagnet indicate that an extrinsic uncompensated moment of ∼1.0 μ B /Fe 2 O 3 can arise from grain boundary disorder. The size of the modeled moment is equivalent to the experimental value, and comparable with previous calculations. Furthermore, unlike intrinsic uncompensated spins, it is found that the disorder-induced moment in the granular antiferromagnet is not destroyed by interface roughness.
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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) | 0.001 | 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".