MétaCan
Menu
Back to cohort
Record W2162486077 · doi:10.1002/cjce.22021

Detailed kinetic modelling of automotive exhaust NO<sub><i>x</i></sub> reduction over rhodium catalyst

2014· article· en· W2162486077 on OpenAlexvenueno aff
Qingyun Su, Liang Xie, Yanbin Li, Xinqi Qiao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsRhodiumCatalysisChemistryReaction mechanismKinetic energyElementary reactionDecompositionKineticsAtmospheric temperature rangeAdsorptionReaction ratePhysical chemistryThermodynamicsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The reduction of NO with CO and H 2 as the reductants was investigated on the rhodium‐based catalyst over a temperature range of 150–400 °C. Micro‐kinetic modelling method was utilized to compare the activities of CO and H 2 . Quasi‐elementary mechanisms of NO‐CO and NO‐H 2 consisting of 11 and 25 steps, respectively, were proposed. The reaction steps and kinetic parameters were derived from literature and some parameters were optimized to fit the experimental data. Furthermore, to verify the validity range of the developed mechanism, the reaction intermediate N 2 O reduction by CO and by H 2 over Rh were investigated. Over a wide range of reaction conditions, the detailed mechanism predicted experimental results quite well for both NO and N 2 O conversions with CO and/or H 2 as reductant. The phenomenon that NO‐CO and NO‐H 2 reactions commenced at similar temperature can be interpreted by the hindered H 2 dissociative adsorption steps by extensive NO adsorption. Reaction profiles such as fractional coverages, reaction pathway and sensitivity analysis provided valuable insight into the reaction systems. N 2 O plays a crucial role in N 2 formation for both NO‐CO and NO‐H 2 reaction systems. The routes involving N 2 O formation and decomposition were identified as the dominant pathway in N 2 formation at low temperatures.

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 score0.106
Threshold uncertainty score0.525

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 science0.0010.000
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 head0.009
GPT teacher head0.187
Teacher spread0.177 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicCatalytic Processes in Materials ScienceFrench-language works237,207