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Record W2807787879 · doi:10.1063/1.5024004

Magnetic domain structure in nanocrystalline nickel electrodeposits

2018· article· en· W2807787879 on OpenAlexaff
G. Avramovic-Cingara, J. Zweck, Jason D. Giallonardo, G. Palumbo, U. Erb

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsRedlen Technologies (Canada)Integran (Canada)University of Toronto
Fundersnot available
KeywordsNanocrystalline materialMaterials scienceCondensed matter physicsMagnetic domainDomain wall (magnetism)Magnetic hysteresisGrain sizeMagnetizationMagnetic anisotropyAmorphous solidNickelPermalloySingle domainHysteresisMagnetic fieldCrystallographyNanotechnologyComposite materialMetallurgyPhysicsChemistry

Abstract

fetched live from OpenAlex

The correlation between the crystal/defect and the magnetic domain structure of nanocrystalline (nc) bulk nickel produced by electrodeposition was investigated. By means of conventional and high resolution transmission electron microscopy, an average grain size of 23 nm was determined; nano-grains surrounded by low angle and high angle boundaries and the presence of nanotwins and stacking faults were observed. The nc nickel exhibited soft magnetic properties. Lorentz TEM (LTEM) in the Fresnel mode revealed magnetic domains of various sizes in the micrometer range extending over many grains, with a few random pinning sites, exhibiting a magnetic ripple structure and vortices. The LTEM was used to investigate the motion of domain walls driven by an external in situ magnetic field and to determine the domain wall width. Domain wall movement was observed at very small magnetic fields along the hysteresis loop. The correlation of the grain size and magnetic properties shows good agreement with the Herzer random anisotropy model for nanocrystalline materials, although the nc nickel studied here has no traces of an amorphous phase.

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 score0.249
Threshold uncertainty score0.513

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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