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Record W2058897941 · doi:10.1063/1.4890580

Microscopic model for exchange bias from grain-boundary disorder in a ferromagnet/antiferromagnet thin film with a nanocrystalline microstructure

2014· article· en· W2058897941 on OpenAlexaff
David Cortie, A. G. Biternas, R.W. Chantrell, Xiaolin Wang, F. Klose

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCondensed matter physicsGrain boundaryFerromagnetismAntiferromagnetismMaterials scienceNanocrystalline materialExchange biasMicrostructureMicromagneticsMagnetic anisotropyMagnetizationPhysicsNanotechnologyMagnetic fieldMetallurgy

Abstract

fetched live from OpenAlex

Monte Carlo spin simulations were coupled to a Voronoi microstructure-generator to predict the magnitude and behavior of exchange bias in a ferromagnet/antiferromagnet (AF) thin film bilayer with a nanocrystalline microstructure. Our model accounts for the effects of irregular grain-shapes, finite-sized particles, and the possible presence of local random-fields originating from the antiferromagnet's grain-boundary regions. As the grain-boundary represents a crystal-structure distortion, we model the local effect on the exchange constants in the Gaussian approximation which can cause regions resembling a spin glass confined to an unusual 2D topology. Although an ensemble of completely disconnected AF grains isolated by non-magnetic barriers provides a small exchange bias, the introduction of a spin-glass network at the boundaries causes a four-fold enhancement in the magnitude of the loop-shift. This implies the importance of local grain-boundary behavior in defect-engineered antiferromagnets.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.204
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.

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

Citations8
Published2014
Admission routes1
Has abstractyes

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