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Record W2997319198 · doi:10.1063/1.5129804

Effect of ingot cooling rate on Cu distribution and magnetic properties of Sm(CobalFe0.28Cu0.07Zr0.03)7.6 magnets

2019· article· en· W2997319198 on OpenAlex
Zhifeng Shang, Dongtao Zhang, Z. Altounian, Zhihong Xie, Pengbiao Qiao, Weiqiang Liu, Yunqiao Wang, Ming Yue

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

VenueAIP Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties of Alloys
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsIngotMagnetMaterials scienceAlloyMicrostructureCoercivityMetallurgyPhase (matter)CastingCondensed matter physicsChemistryMechanical engineering

Abstract

fetched live from OpenAlex

The Sm(CobalFe0.28Cu0.07Zr0.03)7.6 magnets were made by a casting process with two different cooling rates. X-ray diffraction analysis shows that the main phase of the two as-cast alloys consist of 1:5, 1:7, and 2:17 (hexagonal) phase. While a few of rhombohedral 2:17 phases appear in the ingots with the lower cooling rate. The electron probe micro-analysis and corresponding wavelength dispersive x-ray results indicate that a higher cooling rate of the as-cast alloy is helpful to the uniformity of Cu element distribution in the ingots and magnets, especially for suppressing the formation of 2:17 R phase in ingots. The coercivity and squareness of magnet prepared from ingot with higher cooling rate increase by 72 and 48 percentage, respectively. The microstructure observation shows that some cell boundaries are destroyed in the magnet made by lower cooling rate, while the cell boundaries are well developed in the magnet made by higher cooling rate.

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 score0.012
Threshold uncertainty score0.668

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 science0.0000.000
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 head0.006
GPT teacher head0.219
Teacher spread0.212 · 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