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Record W2764310852 · doi:10.1039/c7cp04784c

Cu<sup>0</sup>-Loaded organo-montmorillonite with improved affinity towards hydrogen: an insight into matrix–metal and non-contact hydrogen–metal interactions

2017· article· en· W2764310852 on OpenAlexafffund
Radia Sennour, Tze Chieh Shiao, Vasilica Alisa Aruş, M.N. Tahir, Nabil Bouazizi, René Roy, Abdelkrim Azzouz

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsHydrogenChemistryCopperDesorptionThermal desorptionInorganic chemistryHydrogen storageChemisorptionChemical engineeringAdsorptionPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

nanoparticles (CuNPs) and reduces their mobility. Transmission electron microscopy showed predominant 1.0-1.5 nm CuNPs. Hydrogen capture appears to involve predominantly physical interaction, since differential scanning calorimetry measurements gave low desorption heat and almost complete gas release between 20 °C and 75 °C. Possible hydrogen condensation within the compacted structure should hinder gas diffusion inside CuNPs and prevent chemisorption. These results allow safe hydrogen storage with easy gas release to be envisaged even at room temperature under vacuum. The reversible capture of hydrogen can be even more attractive when using natural inorganic supports and commercial plant-derived dendrimers judiciously functionalized, even at the expense of porosity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2017
Admission routes2
Has abstractyes

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