Transition from ferromagnetic to noncollinear to paramagnetic state with increasing Ru concentration in FeRu films
Bibliographic record
Abstract
The structural and magnetic properties of sputter-deposited ${\mathrm{Fe}}_{100\ensuremath{-}x}{\mathrm{Ru}}_{x}$ films were studied for $x\ensuremath{\le}50$. The crystal structure of ${\mathrm{Fe}}_{100\ensuremath{-}x}{\mathrm{Ru}}_{x}$ is shown to be predominantly body-centered cubic for $x<13$ and to undergo a gradual transition to hexagonal close-packed in the Ru concentration range $13\ensuremath{\lesssim}x\ensuremath{\lesssim}20$. Magnetic measurements indicate that the addition of Ru to Fe gives rise to a noncollinear magnetic alignment between Fe atoms in the body-centered cubic FeRu alloys, while the hexagonal close-packed FeRu alloys exhibit paramagnetic behavior. A simple atomistic model was used to show that the competition between ferromagnetic coupling of neighboring Fe atoms and antiferromagnetic coupling of Fe atoms across Ru atoms in cubic FeRu structures can induce noncollinear magnetic order. Magnetic multilayer structures used in thin-film magnetic devices make extensive use of both Fe and Ru layers. Our results reveal that the presence of even a small amount of Ru in Fe influences the magnetic order of Fe, which could impact the performance of these devices.
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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".