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Record W2057848613 · doi:10.1063/1.1850389

Temporary and permanent enhancement of the magnetoimpedance response of amorphous wires

2005· article· en· W2057848613 on OpenAlexafffund
G. Rudkowska, Liviu Clime, P. Ciureanu, A. Yelon

Bibliographic record

VenueJournal of Applied Physics · 2005
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAnisotropyFigure of meritAmorphous solidComposite materialUltimate tensile strengthAnnealing (glass)AmplitudeGiant magnetoimpedanceResidual stressMagnetic fieldTransverse planeMagnetic anisotropyNuclear magnetic resonanceCondensed matter physicsMagnetoresistanceOptoelectronicsMagnetizationStructural engineeringOpticsGiant magnetoresistance

Abstract

fetched live from OpenAlex

Melt extracted amorphous wires have a predominantly helical anisotropy due to residual tensile and torsional stresses quenched in the wire during casting. Consequently, their magnetoimpedance (MI) response is inadequate for linear detection of magnetic field. Several methods are used to accomplish this, both temporary (dc current biasing of the wire, tensile stress applied to the wire, or both) and permanent (current annealing of the wire, with or without applying a tensile stress). Their purpose is to optimize the MI response of these wires. The transverse (circumferential) anisotropy of the wire reaches an optimum in magnitude and orientation when a temporary method is used. This optimum can be quenched in the material using a permanent method. The MI response is characterized with the help of three criteria: sensitivity, figure of merit and peak amplitude per wire unit length. Magnetic field sensors with sensitivities as high as 75V∕(Tmm), figures of merit of about 0.5ΩT∕m, and peak amplitudes of 5Ω∕mm were obtained as a result of the enhancement of the MI response.

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 score0.039
Threshold uncertainty score0.280

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations0
Published2005
Admission routes2
Has abstractyes

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