MétaCan
Menu
Back to cohort
Record W2507871308 · doi:10.1021/acsami.6b07122

Hydrogen Storage Performance in Pd/Graphene Nanocomposites

2016· article· en· W2507871308 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsChina Scholarship CouncilU.S. Department of Energy
KeywordsMaterials scienceGrapheneHydrogen storageNanocompositeHydrogenNanotechnologyChemical engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

We have developed a Pd-graphene nanocomposite for hydrogen storage. The spherically shaped Pd nanoparticles of 5-45 nm in size are homogeneously distributed over the graphene matrix. This new hydrogen storage system has favorable features like desirable hydrogen storage capacity, ambient conditions of hydrogen uptake, and low temperature of hydrogen release. At a hydrogen charging pressure of 50 bar, the material could yield a gravimetric density of 6.7 wt % in the 1% Pd/graphene nanocomposite. As we increased the applied pressure to 60 bar, the hydrogen uptake capacity reached 8.67 wt % in the 1% Pd/graphene nanocomposite and 7.16 wt % in the 5% Pd/graphene nanocomposite. This system allows storage of hydrogen in amounts that exceed the capacity of the gravimetric target announced by the U.S. Department of Energy (DOE).

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)0.001
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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