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Record W1974030541 · doi:10.1002/cjce.5450850308

Bifurcation Analysis on Pt and Ir for the Reduction of NO by CO

2007· article· en· W1974030541 on OpenAlexvenueno aff
Dinesh Mantri, Viral Mehta, Preeti Aghalayam

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisIsothermal processBistabilityReduction (mathematics)Atmospheric pressureBifurcationChemistryThermodynamicsReaction mechanismKinetic energyMaterials scienceMechanicsPhysical chemistryPhysicsNonlinear systemMathematicsClassical mechanicsMeteorologyOptoelectronicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Due to the importance of the NO‐CO reaction in current catalytic converters, reduction of NO by CO on Pt group catalysts is important to study. Various reaction mechanisms have been proposed for the NO‐CO reaction on Pt(100), which shows bifurcations, kinetic oscillations and multiple steady states under ultra high vacuum (UHV) conditions due to complex surface dynamics. Some experiments on supported Pt group catalysts reported in literature show oscillations and bistability under atmospheric conditions as well. Industrially relevant conditions require the modelling and detailed analysis of the system at atmospheric pressure. We have proposed a reaction mechanism for the NO‐CO system on Pt group catalysts and coupled it with an isothermal PSR model to obtain solutions at atmospheric conditions with the continuation software CONTENT 1.5, at different operating conditions. Simulation results suggest that Pt(111) shows bifurcations at certain operating conditions while Ir(111) shows stable solutions at all the operating conditions studied here.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.226
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2007
Admission routes1
Has abstractyes

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