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Record W2744938530 · doi:10.1109/intmag.2017.8008060

Enhanced coercivity of spark plasma sintered (La,Ce)FeB magnets

2017· article· en· W2744938530 on OpenAlexaff
Qingmei Lu, J. Niu, Weiwei Liu, Ming Yue, Z. Altounian

Bibliographic record

Venue2017 IEEE International Magnetics Conference (INTERMAG) · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties of Alloys
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoercivitySpark plasma sinteringNanocrystalline materialMaterials scienceMagnetRibbonPhase (matter)Analytical Chemistry (journal)CrystallographyCondensed matter physicsNanotechnologyMetallurgyComposite materialPhysicsMicrostructureChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The increasing global demand on Nd-Fe-B makes rare earth (Re) elements including Nd, and Tb facing long-term criticality due to resource limitation and price, it is important to develop new Re-Fe-B magnets with high content of light rare earths (LRe) such as La and Ce which are more abundant and cheaper. However, large amounts exceeds 40 wt.% of La/Ce substitutions for Nd or Pr worse the properties. In addition, the nonmagnetic CeFe2phase, which coexists with the main 2:14:1 phase, lowers the intrinsic magnetic properties of La/Ce-FeB. It is reported that the substitution of La for Ce can inhibit the formation of CeFe2and improve the magnetic properties of CeFeB magnets. In this study, we prepared a series of (La/Ce)FeB ribbons and investigated the effect of La content on the structure and magnetic properties. Based on the optimized (La/Ce)FeB ribbon, spark plasma sintered (SPS) nanocrystalline bulks with high density were fabricated, and (La/Ce)FeB magnets with NdCu additions, to improve the coercivity, were also investigated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.053
GPT teacher head0.301
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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".

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Citations2
Published2017
Admission routes1
Has abstractyes

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Same venue2017 IEEE International Magnetics Conference (INTERMAG)Same topicMagnetic Properties of AlloysFrench-language works237,207