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Mechanosynthesis of Nanocrystalline MgB<sub>2</sub> Ceramic Powders in Hydrogen Alloying Mills via Amorphous Hydride Intermediate

2006· article· en· W2036801604 on OpenAlexaff
Zbigniew S. Wronski, R.A. Varin, Ch. Chiu, Tomasz Czujko

Bibliographic record

VenueAdvances in science and technology · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconductivity in MgB2 and Alloys
Canadian institutionsHydrogenics (Canada)University of WaterlooNatural Resources Canada
Fundersnot available
KeywordsMaterials scienceNanocrystalline materialMechanosynthesisHydrideAmorphous solidBall millHydrogenChemical engineeringBorideImpurityMagnesium hydrideMetallurgyAnnealing (glass)NanotechnologyCrystallographyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

In the present work we report on the synthesis of nanocrystalline MgB2 by mechanochemical reaction (mechanosynthesis) conducted in a high-energy mechanical alloying mill filled with hydrogen. The solid-state reaction of mechanochemical alloying between Mg and B with H (hydrogen alloying) leads to formation of an intermediate amorphous (Mg,B)Hx hydride. This amorphous intermediate is subsequently annealed (devitrified) to nucleate and grow nanocrystalline boride. The first stage of synthesis was carried out at room temperature from elemental Mg and B powders in a high-energy ball mill under sequential supply of hydrogen. The subsequent annealing of the amorphous product led to nearly single-phase MgB2, with only small fraction of MgO impurity. The easy room-temperature synthesis renders the method promising for production of MgB2, which recently gained attention as a new 39K ceramic superconductor. The amorphous intermediate itself can be studied further for its capacity to store ca. 2 wt% H in a metastable hydride phase. The effort was undertaken to predict formation of amorphous hydride phase through analysis of atomic volume mismatch between atoms of Mg, B, and H.

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)0.000
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 score0.079
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.004
GPT teacher head0.220
Teacher spread0.216 · 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.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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