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Record W1983207796 · doi:10.1021/la061149c

Hydrogen Adsorption Measurements and Modeling on Metal-Organic Frameworks and Single-Walled Carbon Nanotubes

2006· article· en· W1983207796 on OpenAlexafffund
É. Poirier, Richard Chahine, Pierre Bénard, Lyubov Lafi, G. Dorval-Douville, Patrick‐Adam Chandonia

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeAdsorptionMetal-organic frameworkMaterials scienceCarbon fibersMetalChemical engineeringNanotechnologyHydrogenChemistryOrganic chemistryComposite materialComposite numberMetallurgy

Abstract

fetched live from OpenAlex

Hydrogen adsorption measurements on Al-, Cr-, and Zn-based metal-organic frameworks (MOFs) and single-walled carbon nanotubes (SWNTs) are presented. The measurements were performed at temperatures ranging from 77 to 300 K and pressures up to 50 atm using a volumetric approach. The maximum excess adsorption at 77 K ranges from 2.3 to 3.9 wt % for the MOFs and from 1.5 to 2.5 wt % for the SWNTs. These values are reached at pressures below 40 atm. At room temperature and 40 atm, modest amounts of hydrogen are adsorbed (<0.4 wt %). A Dubinin-Astakhov (DA) approach is used to investigate the measured adsorption isotherms and to retrieve energetic and structural parameters. The adsorption enthalpy averaged over filling is about 2.9 kJ/mol for the MOF-5 and about 3.6-4.2 kJ/mol for SWNTs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

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 science0.0010.000
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 head0.030
GPT teacher head0.231
Teacher spread0.201 · 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 designSimulation or modeling
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

Citations64
Published2006
Admission routes2
Has abstractyes

Explore more

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