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Record W1981433091 · doi:10.1021/ie0496333

Coke Formation over a Nickel Catalyst under Methane Dry Reforming Conditions:  Thermodynamic and Kinetic Models

2005· article· en· W1981433091 on OpenAlexaff
Jason M. Ginsburg, Juliana Piña, Tarek El Solh, Hugo de Lasa

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsImperial Oil (Canada)Western University
Fundersnot available
KeywordsDisproportionationCokeCatalysisChemistryCarbon dioxide reformingMethaneThermodynamicsCarbon monoxideThermodynamic equilibriumDecompositionNickelChemical engineeringSyngasOrganic chemistry

Abstract

fetched live from OpenAlex

The CO 2 reforming of methane is studied over a 20 wt % Ni/USY-zeolite, and more specifically, a thermodynamic analysis of the formation of coke is used as a basis for the kinetic modeling of coke phenomena that exist under dry reforming conditions. Two thermodynamic parameters, α and β, are compared to the equilibrium constants for the CH 4 decomposition and the CO disproportionation reactions and defined to determine whether coke formation is favored. This thermodynamic analysis elucidates the significance of the CO disproportionation reaction on the amount of coke deposited over the catalyst under consideration. A kinetic model with negative overall order of one, with respect to the partial pressure of carbon monoxide, is found as the most accurate prediction of the rate of coke formation. This type of kinetics strongly suggests the requirement of three adjacent free catalyst sites for the coking reaction to proceed under allowable thermodynamic conditions.

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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.325
Teacher spread0.257 · 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

Citations313
Published2005
Admission routes1
Has abstractyes

Explore more

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