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Hydrogen Activation Behavior of Commercial Magnesium Processed by Different Severe Plastic Deformation Routes

2010· article· en· W1996023966 on OpenAlexaff
Daniel Rodrigo Leiva, Jacques Huot, Tomaz Toshimi Ishikawa, Claudemiro Bolfarini, Cláudio Shyinti Kiminami, Alberto Moreira Jorge, Walter José Botta Filho

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMaterials scienceSevere plastic deformationHydrogen storageMagnesiumHydrogenKineticsForgingMetallurgySorptionTorsion (gastropod)Magnesium alloyComposite materialMicrostructureAlloy

Abstract

fetched live from OpenAlex

Severe plastic deformation (SPD) techniques are being considered as low cost processing routes for Mg alloys, aiming hydrogen storage applications. The main objective is to develop air-resistant materials, with lower specific surface area in comparison with ball-milled powders, but with still attractive H-sorption kinetics associated to the microstructural refinement. In this study, the effects of different SPD processing routes (high-pressure torsion, extensive cold rolling and cold forging) in the hydrogen activation behavior of Mg was evaluated. The results show that both microstructural and textural aspects should be controlled during SPD processing to obtain Mg alloys with good H-sorption properties and enhanced activation kinetics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.236
Teacher spread0.228 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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