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Record W1972277398 · doi:10.1179/cmq.2009.48.1.11

Nanomaterials for Hydrogen Storage Produced by Ball Milling

2009· article· en· W1972277398 on OpenAlexaff
R.A. Varin, Tomasz Czujko, Zbigniew S. Wronski, Z. Zarański

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2009
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHydrideHydrogen storageBall millHydrogenDesorptionMaterials scienceChemical engineeringDecompositionNanomaterialsMagnesium hydrideParticle sizeEnergetic materialInorganic chemistryChemistryMetallurgyNanotechnologyPhysical chemistryOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

AbstractAbstractThree methods of hydrogen desorption temperature reduction and desorption kinetics improvement of nanostructured hydrides processed by mechanical (ball) milling are discussed. The first method is based on a simultaneous particle size refinement of MgH2 hydride and the formation of an unstable γ-MgH2 phase. The second method utilizes catalytic effects of nanometric Ni (n-Ni) additives. The third method is based on the compositing of nanohydride mixtures such as NaBH4+MgH2 and MgH2+LiAlH4 where the first hydride in a pair has higher decomposition temperature than the second one. The low decomposition temperature hydride results in the destabilization of the high temperature constituent hydride.Three methods of hydrogen desorption temperature reduction and desorption kinetics improvement of nanostructured hydrides processed by mechanical (ball) milling are discussed. The first method is based on a simultaneous particle size refinement of MgH2 hydride and the formation of an unstable γ-MgH2 phase. The second method utilizes catalytic effects of nanometric Ni (n-Ni) additives. The third method is based on the compositing of nanohydride mixtures such as NaBH4+MgH2 and MgH2+LiAlH4 where the first hydride in a pair has higher decomposition temperature than the second one. The low decomposition temperature hydride results in the destabilization of the high temperature constituent hydride.On discute de trois méthodes de réduction de la température de désorption de l'hydrogène et d'amélioration de la cinétique de désorption d'hydrures nanostructurés traités par broyage à boulets. La première méthode est basée sur un raffinement simultané de la taille de particule de l'hydrure MgH2 et la formation d'une phase instable de γ-MgH2. La seconde méthode utilise les effets catalytiques d'additifs de Ni nanométrique (n-Ni). La troisième méthode est basée sur la composite de mélanges de nanohydrures tels que NaBH4+MgH2 et MgH2+LiAlH4 où le premier hydrure de la paire a une plus haute température de décomposition que le second. L'hydrure à basse température de décomposition résulte en la déstabilisation de l'hydrure constituant de haute température.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.227
Teacher spread0.217 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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