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Record W4387579904 · doi:10.1002/cctc.202301024

PdCu Alloy Catalyst for Inhibition‐free, Low‐temperature CO Oxidation

2023· article· en· W4387579904 on OpenAlexaff
Yuying Song, Scott Svadlenak, Tanmayi Bathena, Melanie J. Hazlett, William S. Epling, Konstantinos A. Goulas, Lars C. Grabow

Bibliographic record

VenueChemCatChem · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsConcordia University
FundersBasic Energy SciencesU.S. Department of EnergyNational Science Foundation
KeywordsCatalysisAdsorptionAlloyChemistryPlatinumHydrocarbonChemical engineeringInorganic chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Designing robust catalysts for low‐temperature oxidation is pertinent to the development of advanced combustion engines to meet increasingly stringent emissions limitations. Oxidation of CO, hydrocarbon, and NO pollutants over platinum‐group catalysts suffer from strong inhibition due to their competitive adsorption, while coinage metals are generally slow at activating O 2 . Through computational screening, we discovered a PdCu alloy catalyst that completely oxidizes CO below 150 °C without inhibition by NO, propylene or water. This is attributed primarily to geometric effects and the presence of CO bound to Pd sites within the Cu‐rich surface of the PdCu alloy. We demonstrate that the novel PdCu catalyst can be used in tandem with a PtPd catalyst to achieve sequential, inhibition‐free, complete oxidation of CO in a two‐bed system, while also achieving 50 % NO conversion below 120 °C. Moreover, neither water nor propylene adversely affect the low temperature CO oxidation activity.

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 categoriesInsufficient payload (model declined to judge)
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 score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.016
GPT teacher head0.272
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 teacher head, not a consensus.

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

Citations9
Published2023
Admission routes1
Has abstractyes

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