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Record W2910859364 · doi:10.3390/catal9010048

Electrochemical Promotion of Nanostructured Palladium Catalyst for Complete Methane Oxidation

2019· article· en· W2910859364 on OpenAlexafffund
Yasmine M. Hajar, Balaji Venkatesh, Elena A. Baranova

Bibliographic record

VenueCatalysts · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisPolarization (electrochemistry)ElectrochemistryMaterials scienceAnodeMethanePalladiumElectrolyteYttria-stabilized zirconiaInorganic chemistryOxideFaraday efficiencyAnaerobic oxidation of methaneCubic zirconiaChemical engineeringElectrodeChemistryMetallurgyPhysical chemistryOrganic chemistryCeramic

Abstract

fetched live from OpenAlex

Electrochemical promotion of catalysis (EPOC) was investigated for methane complete oxidation over palladium nano-structured catalysts deposited on yttria-stabilized zirconia (YSZ) solid electrolyte. The catalytic rate was evaluated at different temperatures (400, 425 and 450 °C), reactant ratios and polarization values. The electrophobic behavior of the catalyst, i.e., reaction rate increase upon anodic polarization was observed for all temperatures and gas compositions with an apparent Faradaic efficiency as high as 3000 (a current application as low as 1 μA) and maximum rate enhancement ratio up to 2.7. Temperature increase resulted in higher enhancement ratios under closed-circuit conditions. Electrochemical promotion experiments showed persistent behavior, where the catalyst remained in the promoted state upon current or potential interruption for a long period of time. An increase in the polarization time resulted in a longer-lasting persistent promotion (p-EPOC) and required more time for the reaction rate to reach its initial open-circuit value. This was attributed to continuous promotion by the stored oxygen in palladium oxide, which was formed during the anodic polarization in agreement with p-EPOC mechanism reported earlier.

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)0.001
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 score0.002
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.016
GPT teacher head0.259
Teacher spread0.244 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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