MétaCan
Menu
Back to cohort
Record W2991284572 · doi:10.1021/acsanm.9b01691

Synergy between Fe and Promoter Ions Supported on Nanoceria Influences NO<sub><i>x</i></sub> Reduction Catalysis

2019· article· en· W2991284572 on OpenAlexafffund
Vinod K. Paidi, Kimber L. Stamm Masias, Michael Shepit, R. D. Desautels, Charles A. Roberts, J. van Lierop

Bibliographic record

VenueACS Applied Nano Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsCatalysisIonAtomic orbitalChemistryNOxInorganic chemistryMetalMetal ions in aqueous solutionSelective catalytic reductionRedoxReducing agentMaterials scienceCrystallographyPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Fe and Fe–O dispersed on nanoscale CeO supports are considered promising catalysts for oxidation and reduction catalysis and regarded as one of the better alternatives to precious metal catalysts for NO reduction. To understand the role of Fe ions, promoters, and factors that control the NOx reduction, we probed the local environments of Fe, Ce, and O using a range of spectroscopies. The order of Na promotion (sequential vs simultaneous) resulted in a significant difference in the catalytic activity by changing the local electronic structure around the Fe ions in reaction the conditions. The Ce M4,5- and O K-edge X-ray spectroscopy results suggest stabilization of the 4f, eg orbitals, where Fe and promotor ions are affecting the t2g orbitals. Optimizing this bonding environment around the Fe active species with a promoter ion enables tuning of the NO reduction 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.229
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueACS Applied Nano MaterialsSame topicCatalytic Processes in Materials ScienceFrench-language works237,207