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Record W2367722029

The Influence of Magnesium Content on the Hydrogen Absorption Properties of MmNi_(5-x) (CoAlMn)_x/Mg Nanocrystalline Composite

2000· article· en· W2367722029 on OpenAlexaff
Wen Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsMagnesiumComposite numberMaterials scienceHydrogenAbsorption (acoustics)Nanocrystalline materialNuclear chemistryInorganic chemistryChemistryMetallurgyComposite materialNanotechnologyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

WT5BZ]In this work,the SEM, X ray and hydrogen absorption measurement have been used to study the influence of magnesium on the hydrogen absorption properties of MmNi 5 x (CoAlMn) x/Mg composite The results of experiment indicate that the activation property of the composite shows a bad good bad trent with the increase of magnesium content When the magnesium reachs 50wt%, the composite can not absorb hydrogen at all Magnesium also has an influence on the hydrogen absorbed content The absorbed hydrogen content is increased following with the increasing of magnesium content.

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 categoriesInsufficient 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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.031
GPT teacher head0.215
Teacher spread0.184 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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