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Record W2895353622 · doi:10.1063/1.5050935

Measurements of interlayer exchange coupling of Pt in Py|Pt|Py system

2018· article· en· W2895353622 on OpenAlexafffund
Pavlo Omelchenko, B. Heinrich, Erol Girt

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsSimon Fraser University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsFerromagnetismCoupling (piping)Condensed matter physicsMaterials scienceMagnetizationCoupling strengthResonance (particle physics)Ferromagnetic resonanceNuclear magnetic resonanceAnalytical Chemistry (journal)ChemistryAtomic physicsPhysicsMagnetic fieldComposite material

Abstract

fetched live from OpenAlex

Ferromagnetic coupling strength through Pt is experimentally determined using ferromagnetic resonance studies of Py|Pt(dPt)|[Py|Fe] for Pt thicknesses, dPt, between 0.5 and 2.2 nm. The coupling strength decreases exponentially with the Pt thickness from 4.5 mJ/m2 for dPt = 0.5 nm and reduced to less than 0.02 mJ/m2 for dPt = 2.2 nm. The mechanism mediating exchange coupling is assumed to originate from the induced magnetization of Pt due to its proximity to ferromagnetic Py. The fitting thickness dependence of coupling with this model yields a characteristic coupling length scale of ξ = 0.31 ± 0.01 nm. Additionally, the molar susceptibility of proximity induced Pt is found to be 1.4 × 10−7 ± 0.2 × 10−7 m3/mol, an enhancement of ∼100 times as compared to bulk Pt. Ruderman-Kittel-Kasuya-Yosida type oscillations with a period of ∼0.8 nm are also observed as a small contribution of the total coupling.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.710

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)0.0000.000

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 head0.026
GPT teacher head0.226
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations20
Published2018
Admission routes2
Has abstractyes

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