Exchange bias in a nanocrystalline hematite/permalloy thin film investigated with polarized neutron reflectometry
Bibliographic record
Abstract
We investigated a hematite $\ensuremath{\alpha}$-Fe${}_{2}$O${}_{3}$/permalloy Ni${}_{80}$Fe${}_{20}$ bilayer film where the antiferromagnetic layer consisted of small hematite grains in the 2 to 16 nm range. A pronounced exchange bias effect occurred below the blocking temperature of 40 K. The magnitude of exchange bias was enhanced relative to reports for identical compounds in large grain, epitaxial films. However, the blocking temperature was dramatically reduced. As the N\'eel temperature of bulk $\ensuremath{\alpha}$-Fe${}_{2}$O${}_{3}$ is known to be very high (860 K), we attribute the low-temperature onset of exchange bias to the well-known finite-size effect which suppresses the Morin transition for nanostructured hematite. Polarized neutron reflectometry was used to place an upper limit on the concentration and length scale of a layer of uncompensated moments at the antiferromagnetic interface. The data were found to be consistent with an induced magnetic region at the antiferromagnetic interface of 0.5--1.0 ${\ensuremath{\mu}}_{B}$ per Fe atom within a depth of 1--2 nm. The field dependence of the neutron spin-flip signal and spin asymmetry was analyzed in the biased state, and the first and second magnetic reversal were found to occur by asymmetric mechanisms. For the fully trained permalloy loop, reversal occurred symmetrically at both coercive fields by an in-plane spin rotation of ferromagnetic domains.
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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".