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Record W1999168263 · doi:10.1063/1.4890615

Model of the magnetization of nanocrystalline materials at low temperatures

2014· article· en· W1999168263 on OpenAlexaff
Q. Bian, M. Niewczas

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNanocrystalline materialCondensed matter physicsMagnetizationMagnetocrystalline anisotropyMaterials scienceGrain boundaryMagnetic hysteresisMagnetic anisotropyGrain sizeHysteresisPhysicsMagnetic fieldMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

A theoretical model incorporating the material texture has been developed to simulate the magnetic properties of nanocrystalline materials at low temperatures where the effect of thermal energy on magnetization is neglected. The method is based on Landau-Lifshitz-Gilbert (LLG) theory and it describes the magnetization dynamics of individual grains in the effective field. The modified LLG equation incorporates the intrinsic fields from the intragrain magnetocrystalline and grain boundary anisotropies and the interacting fields from intergrain dipolar and exchange couplings between the neighbouring grains. The model is applied to study magnetic properties of textured nanocrystalline Ni samples at 2K and is capable to reproduce closely the hysteresis loop behaviour at different orientations of applied magnetic field. Nanocrystalline Ni shows the grain boundary anisotropy constant K1s=−6.0×104J/m3 and the intergrain exchange coupling denoted by the effective exchange constant Ap = 2.16 × 10–11 J/m. Analytical expressions to estimate the intergrain exchange energy density and the effective exchange constant have been formulated.

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 score0.042
Threshold uncertainty score0.187

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 head0.011
GPT teacher head0.202
Teacher spread0.191 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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