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Fe-Metalloid Metallic Glasses with High Magnetic Flux Density and High Glass-Forming Ability

2007· article· en· W1965592753 on OpenAlexaff
Akihiro Makino, Takeshi Kubota, Masahiro Makabe, Chun Chang, Akihisa Inoue

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMaterials scienceAmorphous metalCoercivityAlloyAmorphous solidMetallurgyComposite materialEngineering physicsCondensed matter physicsCrystallography

Abstract

fetched live from OpenAlex

Fe-based bulk metallic glasses with good soft magnetic softness, high strength and relatively low material cost should have greatest potential for wide variety of applications among many kinds of bulk metallic glasses (BMGs). However, the glass-forming metal elements such as Al, Ga, Nb, Mo and so forth in the Fe-based BMGs significantly decrease saturation magnetization (Js) which is a essential property as soft magnetic materials. Since the coexistence of high Js and high glass-forming ability (GFA) has been earnestly desired from academia to industry, however, has been left unrealized over many years. Here, we present a Fe76Si9B10P5 bulk glassy alloy exhibiting with unusual combination of high Js of 1.51 T comparable to the Fe-Si-B amorphous alloy ribbons with thickness of about 25 μm in now practical use, because of not-containing the glass-forming metal elements, and high GFA leading to a rod with a diameter of 2.5 mm. This alloy composed of familiar and low-priced elements also has extremely low coercivity which should enable ultra-high efficient transformers, therefore, has a great advantage for engineering and industry, and thus significantly improves energy saving, conservation of earth resources and environment.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.203
Teacher spread0.196 · 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 teacher head, not a consensus.

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

Citations15
Published2007
Admission routes1
Has abstractyes

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