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Record W2035567452 · doi:10.1063/1.3679467

Low core loss of non-Si quaternary Fe83.3B8P8Cu0.7 nanocrystalline alloy with high <i>Bs</i> of 1.7 T

2012· article· en· W2035567452 on OpenAlexaff
Akiri Urata, Makoto Yamaki, Masahiko Takahashi, Koichi Okamoto, Hiroyuki Matsumoto, Shigeyoshi Yoshida, Akihiro Makino

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsNanocrystalline materialMaterials scienceAlloyAmorphous solidAmorphous metalMagnetic alloyMetallurgyMagnetic shape-memory alloySaturation (graph theory)CrystallographyMagnetizationMagnetic anisotropyNanotechnologyMagnetic fieldChemistry

Abstract

fetched live from OpenAlex

The effect of replacement Si by P on the soft magnetic and structural properties of nanocrystalline Fe-Si-B-P-Cu alloys has been investigated. The nanocrystalline Fe83.3SiXB8P8−XCu0.7 (X = 0, 2, 4, 6) alloy ribbons consist of precipitated α-Fe phase and residual amorphous phase, and initial permeability of these alloy ribbons are enhanced with decreasing Si content. In particular, the nanocrystalline Fe83.3B8P8Cu0.7 (X = 0) alloy has both low core loss of 1.4 W/kg at 1.0 T – 50 Hz and high saturation magnetic flux density of 1.70 T. In addition, this alloy exhibits the most favorable nanocrystalline structure containing the homogeneously precipitated α-Fe grains with 14 nm in mean diameter. Therefore, it can be concluded that the soft magnetic properties and nanostructure of Fe-Si-B-P-Cu alloys are strongly affected by Si and P content. The Fe83.3B8P8Cu0.7 alloy with low core loss and high saturation magnetic flux density compared with a Fe amorphous alloy is suitable for a magnetic core material in electronic devices such as transformers, inductors and motors.

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 score0.165
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.200
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

Citations35
Published2012
Admission routes1
Has abstractyes

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