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Record W1982721472 · doi:10.1002/cjce.21954

Preparation and characterization of palladium–ruthenium composite membrane on alumina‐modified PSS substrate

2014· article· en· W1982721472 on OpenAlexafffundvenue
Nong Xu, Sung Su Kim, Anwu Li, John R. Grace, C. Jim Lim, Tony Boyd

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsNORAM (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRutheniumPalladiumPermeationX-ray photoelectron spectroscopyMembraneHydrogenMaterials scienceSubstrate (aquarium)AlloyChemical engineeringComposite numberMetalPlating (geology)Nuclear chemistryChemistryInorganic chemistryCatalysisMetallurgyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A new Ru/Pd/Al 2 O 3 /PSS layered membrane was fabricated by electroless plating of palladium and ruthenium for hydrogen separation. Pd and Ru were successively deposited on a pretreated PSS substrate using palladium and ruthenium plating baths at 333 and 358 K, respectively. EDX, XRD and XPS results confirmed the successful plating of ruthenium. Surface analysis showed that the palladium and ruthenium layers did not alloy significantly at temperatures of 723, 823 and 923 K over 100 h. The hydrogen permeation performance of the Pd–Ru composite membrane followed Sieverts' law, suggesting that diffusion of atomic hydrogen through the dense metal layer was rate‐limiting. The addition of ruthenium had a negligible effect on hydrogen permeability when the Ru weight content was ≤8 wt%. However, when the Ru content was increased to 11 wt%, H 2 permeability decreased by 10 %. The Pd–Ru membrane exhibited good chemical stability and high H 2 /N 2 selectivity in long‐term permeation testing.

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)0.000
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 score0.044
Threshold uncertainty score0.505

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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 teacher head, 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

Citations14
Published2014
Admission routes3
Has abstractyes

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