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Record W1886871970 · doi:10.1002/cjce.22229

Methane oxidation over Pt, Pt:Pd, and Pd based catalysts: Effects of pre‐treatment

2015· article· en· W1886871970 on OpenAlexafffundvenue
Reza Abbasi, Guangyu Huang, Georgeta Istratescu, Long Wu, Robert E. Hayes

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsPalladiumCatalysisPlatinumMethaneAnaerobic oxidation of methaneOxidizing agentInorganic chemistryChemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents a comparison of the activity for methane oxidation of selected commercial platinum, palladium, and platinum‐palladium supported catalysts. Precious metal loadings are typical of those found in the catalytic converters for lean‐burn natural gas engines. Experiments are presented for de‐greened as well as hydrothermally aged catalysts, both in the presence and the absence of water. For the platinum catalyst, fraction conversion of methane is shown to be independent of methane concentration, while for the palladium‐containing catalysts a dependence of conversion on methane partial pressure is observed. For catalysts containing palladium, reduction in hydrogen gives an increase in methane conversion activity, although the increase is subsequently lost in the oxidizing atmosphere. Hydrothermal aging of the platinum catalyst causes a relatively large and permanent loss in methane oxidation activity, while the palladium‐based catalysts showed more resistance to deactivation. Adding a small amount of palladium to the platinum catalyst provides an overall improvement in performance.

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)0.001
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 score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations37
Published2015
Admission routes3
Has abstractyes

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