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Record W2314505058 · doi:10.1021/jp5126975

<i>In Situ</i> Characterization of Highly Dispersed, Ceria-Supported Fe Sites for NO Reduction by CO

2015· article· en· W2314505058 on OpenAlexafffund
Charles A. Roberts, Darío Prieto‐Centurión, Yasutaka Nagai, Yusaku Nishimura, R. D. Desautels, J. van Lierop, Paul T. Fanson, Justin M. Notestein

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsCatalysisRedoxX-ray absorption spectroscopyIn situAbsorption (acoustics)Characterization (materials science)Inorganic chemistryOxidation statePhase (matter)Materials scienceChemistrySelective catalytic reductionAbsorption spectroscopyChemical engineeringNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Highly dispersed FeOx was impregnated onto CeO 2 using the unique precursor Na/Fe-ethylenediaminetetraacetate (NaFeEDTA) at varying Fe surface density (0–1.5 Fe/nm 2 ). These catalysts were used for NO reduction by CO and were compared to a more traditional Fe(NO 3 ) 3 precursor impregnated on Na-promoted CeO 2 . Extensive characterization and spectroscopic measurements showed that NaFeEDTA produced a narrower distribution of smaller, noncrystalline, surface FeOx species with excellent redox cyclability (Fe 3+ ↔ Fe 2+ ). The NaFeEDTA catalysts exhibited corresponding higher steady-state activity for NO reduction by CO. In situ X-ray absorption spectroscopy with simultaneous gas-phase monitoring indicated that NO conversion began concurrent with reduction of Fe and Ce, suggesting that NO reduction occurred at a reduced Fe–O–Ce interface site. These results illustrate a new synthesis–structure–activity relationship for NO reduction by CO over redox-cycling FeOx sites that may support future rational design of emission control catalysts without Pt-group metals or zeolites.

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)0.000
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 score0.001
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.014
GPT teacher head0.269
Teacher spread0.255 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

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