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Characterization of Magnetic Properties of Nanocrystalline Ni and Ni-15%Fe Alloy

2012· article· en· W2033378486 on OpenAlexaff
Sahar Arabi, G. Avramovic-Cingara, G. Palumbo, U. Erb, M. Niewczas

Bibliographic record

VenueMaterials science forum · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsNanocrystalline materialCoercivityMaterials scienceRemanenceAlloyMagnetizationGrain sizeSaturation (graph theory)Condensed matter physicsMetallurgyAnisotropyNanotechnologyMagnetic field

Abstract

fetched live from OpenAlex

Magnetic properties of nanocrystalline Ni and Ni-Fe alloys produced by electrodeposition have been studied at 2K and at 298K. Ni and Ni-15%Fe alloy deposits show nano-grain structure with the average grain size of 23 nm and 12 nm, respectively. Both materials exhibit soft magnetic properties. Nanocrystalline Ni at 2K shows saturation magnetization, coercive force, and remanent magnetization of 57 emu/gr, 101 Oe, and 16 emu/gr respectively. Nanocrystalline Ni-15%Fe alloy exhibits superior soft magnetic properties than Ni with corresponding saturation magnetization, coercive force, and remanent magnetization at 2K of 96 emu/gr, 6 Oe, and 4 emu/gr respectively. The magnetic properties and their dependence upon temperature data are interpreted in terms of the Herzer random anisotropy model for nanocrystalline materials.

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 score0.002
Threshold uncertainty score0.908

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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

Citations6
Published2012
Admission routes1
Has abstractyes

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