Seeking to quantify the ferromagnetic-to-antiferromagnetic interface coupling resulting in exchange bias with various thin-film conformations
Bibliographic record
Abstract
Ni3Fe/(Ni, Fe)O thin films with bilayer and nanocrystallite dispersion morphologies are prepared with a dual ion beam deposition technique permitting precise control of nanocrystallite growth, composition, and admixtures. A bilayer morphology provides a Ni3Fe-to-NiO interface, while the dispersion films have different mixtures of Ni3Fe, NiO, and FeO nanocrystallites. Using detailed analyses of high resolution transmission electron microscopy images with Multislice simulations, the nanocrystallites' structures and phases are determined, and the intermixing between the Ni3Fe, NiO, and FeO interfaces is quantified. From field-cooled hysteresis loops, the exchange bias loop shift from spin interactions at the interfaces are determined. With similar interfacial molar ratios of FM-to-AF, we find the exchange bias field essentially unchanged. However, when the interfacial ratio of FM to AF was FM rich, the exchange bias field increases. Since the FM/AF interface ‘contact’ areas in the nanocrystallite dispersion films are larger than that of the bilayer film, and the nanocrystallite dispersions exhibit larger FM-to-AF interfacial contributions to the magnetism, we attribute the changes in the exchange bias to be from increases in the interfacial segments that suffer defects (such as vacancies and bond distortions), that also affects the coercive fields.
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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".