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Record W2085105911 · doi:10.1063/1.2711714

Influence of quenching rate on the microstructure and magnetic properties of melt-spun L10-FePt∕Fe2B nanocomposite magnets

2007· article· en· W2085105911 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Physics · 2007
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties of Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsNanocompositeMaterials scienceMelt spinningCoercivityRemanenceMicrostructureAmorphous solidQuenching (fluorescence)MagnetChemical engineeringHomogeneousComposite materialCondensed matter physicsMagnetizationCrystallographySpinningMagnetic fieldThermodynamicsChemistryOptics

Abstract

fetched live from OpenAlex

The quenching rate, which is dependent on the surface velocity (Vs) of Cu wheel during melt spinning, has significant influence on the formation of nanocomposite structure in the Fe52Pt32B18 melt-spun ribbons. The L10-FePt∕Fe2B hard magnetic nanocomposite structure was formed at Vs=20–37m∕s, while the soft magnetic fcc-FePt+amorphous phases were formed at Vs=40–50m∕s. The ribbons melt spun at Vs=37m∕s exhibit in-plane coercivity (Hci)=760kA∕m, remanence (Br)=0.71T, and energy product (BH)max=93.4kJ∕m3. The Br=0.74–0.77T, Hci=681–718kA∕m, and (BH)max=101–108kJ∕m3 were obtained for the ribbons melt spun at Vs=50m∕s and annealed at 748–773K for 900s. The improvement in hard magnetic properties is due to the formation of more finer and homogeneous nanocomposite structure, which results in the enhancement in exchange coupling among the nanosized hard L10-FePt and soft Fe2B magnetic phases.

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.

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)0.001
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 score0.005
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.009
GPT teacher head0.199
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