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Record W4402588229 · doi:10.48550/arxiv.2407.17575

Transition from ferromagnetic to noncollinear to paramagnetic state with increasing Ru concentration in FeRu films

2024· preprint· en· W4402588229 on OpenAlexafffund
Juliana Lisik, Manuel Rojas, Spencer Myrtle, D. H. Ryan, René Hübner, Pavlo Omelchenko, Claas Abert, Amil Ducevic, Dieter Suess, Ivan Soldatov, Rudolf Schaefer, Johannes Seyd, M. Albrecht, Erol Girt

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMcGill UniversitySimon Fraser University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesHelmholtz-Zentrum Dresden-RossendorfAustrian Science FundBundesministerium für Bildung und Forschung
KeywordsFerromagnetismParamagnetismCondensed matter physicsState (computer science)Materials scienceChemistryCrystallographyPhysicsComputer science

Abstract

fetched live from OpenAlex

The structural and magnetic properties of sputter-deposited Fe$_{100-x}$Ru$_x$ films were studied for $x \leq 50$. The crystal structure of Fe$_{100-x}$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 concentration range $13 \lesssim x \lesssim 20$. Magnetic measurements indicate that the addition of Ru induces a noncollinear magnetic order in the body-centered cubic FeRu alloys, while the hexagonal close-packed FeRu alloys exhibit paramagnetic behavior. Increasing the Ru concentration in body-centered cubic FeRu alloys decreases the size of magnetic domains, approaching the size of magnetic grains. A simple atomistic model was used to show that antiferromagnetic coupling of Fe atoms across Ru atoms can be responsible for inducing noncollinear order in the FeRu cubic structures. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.177
Teacher spread0.150 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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