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Record W2074214766 · doi:10.1063/1.3670509

Synthesis, microstructure and magnetic properties of low Nd content Fe90Nd5B3.5M1.5 (M = Hf, Ti and Ta) alloys

2012· article· en· W2074214766 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 · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties of Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMaterials scienceMicrostructureRemanenceGrain sizeAnnealing (glass)AlloyCrystallizationTetragonal crystal systemNeodymium magnetRibbonMagnetMetallurgyCoercivityMelt spinningGrain growthAnalytical Chemistry (journal)Composite materialMagnetizationCrystallographyChemical engineeringCondensed matter physicsCrystal structureMagnetic fieldChemistry

Abstract

fetched live from OpenAlex

We have investigated the effects of Hf, Ti and Ta addition on crystallization behavior, microstructure and magnetic properties of melt-spun Fe90Nd5B3.5M1.5 (M = Hf, Ti and Ta) alloy ribbons. Optimization of processing conditions lead to the development of nano-composite magnets, made from high Bs soft magnetic bcc α-Fe (grain size ∼20 nm) and tetragonal Nd2Fe14B (grain size ∼18 nm) hard magnetic phases. Hf is most effective in restraining the growth of α-Fe and Nd2Fe14B phases during annealing. The Fe90Nd5B3.5Hf1.5 ribbon annealed at 1003 K for 600 s exhibits very high maximum energy product (BH)max ∼127 kJ/m3 and reduced remanence ∼1.34 T. Strong exchange coupling among the nano-sized soft and hard magnetic phases is shown to be responsible for the excellent magnetic properties. Among low Nd-containing NdFeB nano-composite magnets, the present alloy seems to have highest (BH)max.

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)0.000
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 score0.004
Threshold uncertainty score0.627

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 studies0.0000.000
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 head0.020
GPT teacher head0.196
Teacher spread0.176 · 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