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The Effect of Co Addition on Glassy Forming Ability and Soft Magnetic Properties of Fe-Si-B-P Bulk Metallic Glass

2012· article· en· W2078697916 on OpenAlexaff
Xue Li, Yan Zhang, Hidemi Kato, Akihiro Makino, Akihisa Inoue

Bibliographic record

VenueKey engineering materials · 2012
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMaterials scienceAmorphous metalCoercivityAlloySaturation (graph theory)FerromagnetismComposite materialMetallurgyMetalCondensed matter physics

Abstract

fetched live from OpenAlex

We Successfully Prepared the Rod Glassy Samples of (Fe1-xCox)76Si9B10P5(x= 0~0.4) Bulk Metallic Glass (BMG) with the Diameters up to 3.0 mm by Substituting Fe for a Small Amount of Co Element. A Certain Amount of Co Substitution for Fe Contributes to the Increase of the Glass-Forming Ability (GFA) while Maintaining Good Mechanical Properties (the Fracture Strength up to 3700 MPa). This Co-Added Ferromagnetic Bulk Glassy Alloy System Also Exhibits a Higher Saturation Magnetization of 1.49 T and Lower Coercive Force (Hc, 1.2 A/m). The Fe-Based BMGs with Alloying a Small Amount of Co Element Demonstrate Excellent Combination of High GFA, Good Soft-Magnetic Properties as Well as High Strength.

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 score0.002
Threshold uncertainty score0.000

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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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".

Quick stats

Citations10
Published2012
Admission routes1
Has abstractyes

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