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Record W2004180644 · doi:10.3390/met2010022

Nanocrystalline Metal Hydrides Obtained by Severe Plastic Deformations

2012· article· en· W2004180644 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.

Bibliographic record

VenueMetals · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNorges Forskningsråd
KeywordsMaterials scienceNanocrystalline materialHydrogen storageHydrogenSevere plastic deformationAlloyBall millMetallurgyMetalSorptionMagnesiumMagnesium alloyComposite materialNanotechnologyChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

It has recently been shown that Severe Plastic Deformation (SPD) techniques could be used to obtain nanostructured metal hydrides with enhanced hydrogen sorption properties. In this paper we review the different SPD techniques used on metal hydrides and present some specific cases of the effect of cold rolling on the hydrogen storage properties and crystal structure of various types of metal hydrides such as magnesium-based alloys and body centered cubic (BCC) alloys. Results show that generally cold rolling is as effective as ball milling to enhance hydrogen sorption kinetics. However, for some alloys such as TiV0.9Mn1.1 alloy ball milling and cold rolling have detrimental effect on hydrogen capacity. The exact mechanism responsible for the change in hydrogenation properties may not be the same for ball milling and cold rolling. Nevertheless, particle size reduction and texture seems to play a leading role in the hydrogen sorption enhancement of cold rolled metal hydrides.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.108
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.014
GPT teacher head0.239
Teacher spread0.224 · 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