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Synthesis of Nanocrystalline CaNi<sub>5</sub>-Based Alloys and Use for Metal Hydride Electrode

2001· article· en· W1987387232 on OpenAlexaff
Guo Xian Liang, Stéphane Ruggeri, C. Lenain, Houshang Alamdari, Jacques Huot, Lionel Roué, Robert Schulz

Bibliographic record

VenueJournal of Metastable and Nanocrystalline Materials · 2001
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité LavalUniversité du Québec à Trois-RivièresInstitut National de la Recherche ScientifiqueHydro-Québec
Fundersnot available
KeywordsNanocrystalline materialMaterials scienceAnnealing (glass)HydrideHydrogen storageElectrochemistryMetallurgyElectrodeHydrogenChemical engineeringMetalNanotechnologyPhysical chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The CaNi 5 -based alloys were synthesized by mechanical alloying followed by a low temperature annealing. Study shows that both substitution and annealing treatment affect the hydrogen storage capacity. Mm substitution for Ca results in an increased plateau pressure. The Zn and Al substitution for Ni leads to a bigger unit cell volume, a lower plateau pressure and a reduced capacity. Annealing treatment of the mechanically alloyed CaNi 5 -based alloys is a necessary process for obtaining good hydrogen storage capacity. The synthesized Ca-Mm-Ni-Zn-Al multicomponent nanocrystalline alloys exhibit fast kinetics on gas phase and electrochemical reaction and significantly improved cycle stability on electrochemical reaction.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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)0.0010.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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