MétaCan
Menu
Back to cohort
Record W1966753191 · doi:10.1002/cssc.201402958

Metal–Inorganic–Organic Matrices as Efficient Sorbents for Hydrogen Storage

2015· article· en· W1966753191 on OpenAlexafffund
Abdelkrim Azzouz, Saadia Nousir, Nabil Bouazizi, René Roy

Bibliographic record

VenueChemSusChem · 2015
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsHydrogen storageHydrogenChemistryMetalChemical engineeringAdsorptionInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Stabilization of metal nanoparticles (MNPs) without re-aggregation is a major challenge. An unprecedented strategy is developed for achieving high dispersion of copper(0) or palladium(0) on montmorillonite-supported diethanolamine or thioglycerol. This results in novel metal-inorganic-organic matrices (MIOM) that readily capture hydrogen at ambient conditions, with easy release under air stream. Hydrogen retention appears to involve mainly physical interactions, slightly stronger on thioglycerol-based MIOM (S-MIOM). Thermal enhancement of desorption suggests also a contribution of chemical interactions. The increase of hydrogen uptake with prolonged contact times arises from diffusion hindrance, which appears to be beneficial by favoring hydrogen entrapment. Even with compact structures, MIOMs act as efficient sorbents with much higher efficiency factor (1.14-1.17 mmol H 2 m(-2)) than many other sophisticated adsorbents reported in the literature. This opens new prospects for hydrogen storage and potential applications in microfluidic hydrogenation reactions.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.079
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.259
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; both teacher heads agree on what is shown here.

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

Citations36
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueChemSusChemSame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207