MétaCan
Menu
Back to cohort
Record W2024287470 · doi:10.2495/mc130131

Physisorption of molecular hydrogen in curved carbon nanomaterials: a computational study

2013· article· en· W2024287470 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWIT transactions on engineering sciences · 2013
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPhysisorptionNanomaterialsMaterials scienceHydrogenCarbon fibersHydrogen moleculeNanotechnologyChemistryPhysical chemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

Hydrogen physisorption on carbon nanomaterials is a promising method of hydrogen storage because carbon materials are cheap, abundant and light weight. However, storage is difficult because dispersion forces between C and H are weak. Curved carbon substrates are more promising than planar systems because the increased level of sp 3 -hybridization enhances H 2 physisorption. The present study uses density functional theory to model large fullerenes, single-walled carbon nanotubes and graphene to investigate the interaction with H 2 ; decorating platinum is also considered. We conclude that H 2 can be stored in fullerenes without an energy input if the H 2 molecules are more than 3 from the carbon surface and more than 2 from each other. In addition, confinement effects are observed when hydrogen is stored in fullerenes rather than nanotubes -storage in nanotubes is more favourable for systems with small diameters.

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.

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)0.000
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 score0.361
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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