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Record W3184130465 · doi:10.1103/physrevb.104.024422

Spacer layer thickness and temperature dependence of interlayer exchange coupling in Co/Ru/Co trilayer structures

2021· article· en· W3184130465 on OpenAlexaff
Tommy McKinnon, B. Heinrich, Erol Girt

Bibliographic record

VenuePhysical review. B./Physical review. B · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCoupling (piping)Materials scienceCondensed matter physicsOscillation (cell signaling)AntiferromagnetismLayer (electronics)PerpendicularAtmospheric temperature rangeReflection (computer programming)PhysicsComposite materialChemistryThermodynamics

Abstract

fetched live from OpenAlex

In this paper, we measure the bilinear interlayer magnetic coupling ${J}_{1}$ between two Co layers coupled across a Ru spacer layer over a wide range of spacer layer thicknesses from 0.4 to 3.4 nm and temperatures from 5 to 300 K. These measurements are fit using the interface-reflection interlayer magnetic coupling model in order to determine coupling strengths and electron Fermi velocities within the spacer layer in the direction perpendicular to the film interface for each of the critical spanning vectors. We find that there is a significant contribution to ${J}_{1}$ from several different critical spanning vectors, all with different periods of oscillation with respect to the spacer layer thickness. The results indicate that there is likely no exponential superexchangelike contribution to coupling in our samples. The nonoscillatory antiferromagnetic coupling bias of ${J}_{1}$ seen in thinner Ru spacer layers can be explained solely by a linear combination of oscillatory Ruderman-Kittel-Kasuya-Yosida-like coupling from several different critical spanning vectors, all with different periods of oscillation. The experimentally determined electron Fermi velocities are found to be within the range expected from theoretical calculations. The results also indicate that the interface-reflection model is capable of describing the bilinear interlayer exchange coupling in our samples over the entire range of spacer layer thicknesses and temperatures measured in this paper.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.366
Teacher spread0.341 · 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 designTheoretical or conceptual
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

Citations21
Published2021
Admission routes1
Has abstractyes

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