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Record W2118407395 · doi:10.1109/tmag.2002.801906

Magnetic relaxations in metallic multilayers

2002· article· en· W2118407395 on OpenAlexaff
B. Heinrich, R. Urban, Georg Woltersdorf

Bibliographic record

VenueIEEE Transactions on Magnetics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCondensed matter physicsFerromagnetic resonanceMagnonMagnetic dampingMaterials scienceFerromagnetismRelaxation (psychology)MagnetizationPhononSpin (aerodynamics)PhysicsMagnetic fieldVibration

Abstract

fetched live from OpenAlex

The intrinsic damping mechanism in metals caused by incoherent scattering of itinerant electron-hole pair excitations by phonons and magnons will be reviewed. The unique features of magnetic relaxations in multilayers were studied by ferromagnetic resonance (FMR) using magnetic single, Au–Fe–GaAs(001), and double layer Au–Fe–Au–Fe–GaAs(001) structures prepared by molecular beam epitaxy. The magnetic relaxation in single-layer films is described by the Gilbert damping with no extrinsic contributions to the FMR linewidth. These films provided an excellent opportunity to investigate nonlocal damping. The main result of these studies is that ultrathin Fe films in magnetic double layers acquire an additional interface Gilbert damping. This is in agreement with recent predictions of nonlocal interface damping which is based on the transport of spin angular momentum between the ferromagnetic layers. Measurements of the nonlocal Gilbert damping offer a possibility to carry out quantitative studies of the relaxation torques caused by nonlocal spin momentum transfer. Numerical simulations of magnetization reversal and stationary precession for an applied perpendicular current in Au–Fe–Au–Fe–GaAs(001) multilayers will be shown.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.937
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0210.001

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 head0.020
GPT teacher head0.216
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations26
Published2002
Admission routes1
Has abstractyes

Explore more

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