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Record W1985835355 · doi:10.1021/ef700398f

Effect of Relative Bed Lengths on a Platinum/Nickel Stratified Dual Bed Catalyst for the Catalytic Partial Oxidation of Methane at Millisecond Contact Times

2007· article· en· W1985835355 on OpenAlexaff
Christa J. Bell, C.A. Leclerc

Bibliographic record

VenueEnergy & Fuels · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMcGill University
Fundersnot available
KeywordsPlatinumCatalysisNickelPartial oxidationChemistryMethaneRhodiumInorganic chemistryCarbon monoxideSpace velocityChemical engineeringSelectivityOrganic chemistry

Abstract

fetched live from OpenAlex

Stratified, dual bed catalysts composed of platinum followed by nickel have shown reactant conversions and product selectivities similar to rhodium, which has shown the highest activity for the millisecond catalytic partial oxidation reaction. The goal of the stratified catalyst is to carry out the catalytic partial oxidation in a two-step process: combustion catalyzed by platinum followed by steam and carbon dioxide reforming catalyzed by nickel. Previous studies have shown that sequential platinum and nickel beds with the same space velocity have high activity and long-term stability. In this work, the relative bed length of the platinum and nickel catalysts is investigated. Since combustion occurs much faster than reforming, the platinum bed can be much shorter than the nickel bed. Experiments show that the relative bed length of platinum can be much smaller than that of nickel in the dual bed catalyst. Reducing the amount of platinum increases the methane conversion and hydrogen selectivity while reducing the carbon monoxide selectivity. Degradation studies under harsh operating conditions show that the catalyst is stable. The number of potential experiments required to optimize this system leads one to believe a simulation-based approach will be more efficient.

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)0.003
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 score0.005
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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