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Record W2015062670 · doi:10.1088/1757-899x/63/1/012114

Improvement of hydrogen storage properties of magnesium alloys by cold rolling and forging

2014· article· en· W2015062670 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education of Perm Territory
KeywordsMagnesiumMaterials scienceMetallurgyHydrogen storageMagnesium alloyForgingCreepHydrogenAlloyTexture (cosmology)NanostructureGrain boundaryMicrostructureChemistryNanotechnology

Abstract

fetched live from OpenAlex

In this talk we show that cold rolling (CR) could be used to enhance hydrogen sorption properties of magnesium and magnesium alloys. In particular, cold rolling could reduce the first hydrogenation time, the so-called activation. Pure magnesium, commercial AZ91D alloy, and an experimental creep resistant magnesium alloy MRI153 in the as-cast and die-cast states were investigated. We found that both MRI and AZ91 alloys present faster activation kinetic than pure magnesium. This could be explained by the texture, higher number of defects, and nanostructure in CR materials but also precipitates at the grain boundaries. The effect of filing was also investigated.

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)0.002
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 score0.001
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.186
Teacher spread0.177 · 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