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Record W2070748958 · doi:10.1063/1.1635997

Coupled core–shell model of magnetoimpedance in wires

2004· article· en· W2070748958 on OpenAlexaff
L. G. C. Melo, David Ménard, P. Ciureanu, A. Yelon, R. W. Cochrane

Bibliographic record

VenueJournal of Applied Physics · 2004
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMagnetizationCondensed matter physicsShell (structure)Giant magnetoimpedanceMagnetic fieldElectrical impedancePhysicsAmplitudeCoupling (piping)Core (optical fiber)MagnetostaticsMaxwell's equationsField (mathematics)Materials scienceClassical mechanicsMagnetoresistanceGiant magnetoresistanceMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Magnetoimpedance (MI) has been studied extensively in soft magnetic wires and plates. Although a general theoretical basis has evolved, several details remain poorly understood. In particular, the amplitude of the effect in the low field region has proven impossible to fit within current models which assume a uniform static magnetization within the material. In this article, we present magnetization and MI data on CoFeSiBNb melt-extracted wires and conclude that the behavior of these materials can be analyzed on the basis of a core–shell magnetic structure. This approach introduces a nonuniform magnetization into the MI theory in such wires. We calculate the static magnetic configuration in the presence of an exchange coupling between the two regions and use it to solve for the dynamical magnetization of the outer shell using the Landau–Lifshitz and Maxwell equations to obtain the impedance as a function of the applied field and frequency. The agreement for the MI between theory and experiment is greatly improved from that of previous models.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations21
Published2004
Admission routes1
Has abstractyes

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