MétaCan
Menu
Back to cohort
Record W2018869674 · doi:10.1149/1.1928114

Synergetic Effect between Ti and Al on the Cycling Stability of MgNi-Based Metal Hydride Electrodes

2005· article· en· W2018869674 on OpenAlexafffund
Carine Rongeat, Lionel Roué

Bibliographic record

VenueJournal of The Electrochemical Society · 2005
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHydrideElectrodeAlloyAmorphous solidX-ray photoelectron spectroscopyHydrogen storageDiffusionCyclic voltammetryMetalHydrogenChemical engineeringParticle (ecology)MetallurgyComposite materialElectrochemistryChemistryCrystallographyPhysical chemistryThermodynamics

Abstract

fetched live from OpenAlex

Amorphous MgNi, , and compounds have been prepared by mechanical alloying and evaluated as metal hydride electrodes. The electrode retains 67% of its initial discharge capacity after , compared to 54% for and 34% for MgNi. This confirms the synergetic effect between Ti and Al that improves the cycling stability of the MgNi-based metal hydride electrode. X-ray photoelectron spectroscopy shows the presence of and onto the particles. These two oxides appear to be very efficient at preventing the accumulation of onto the particles upon cycling as suggested by X-ray diffraction analyses and cyclic voltammetry experiments. Moreover, on the basis of the evolution with cycling of the ratio of the hydrogen diffusion coefficient to the particle radius , material appears less sensitive to pulverization. This is in accordance with an increase of the maximal amount of hydrogen absorbed into before a significant decay in capacity occurs. Finally, we have fabricated a electrode with large particles (diameter ) having, under controlled charging conditions, a capacity decay rate as low as that observed for a commercial -type alloy (i.e., ).

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)0.003
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 score0.002
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.245
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicHydrogen Storage and MaterialsFrench-language works237,207