MétaCan
Menu
Back to cohort
Record W4229331889 · doi:10.3390/catal12050524

Reducibility Studies of Ceria, Ce0.85Zr0.15O2 (CZ) and Au/CZ Catalysts after Alkali Ion Doping: Impact on Activity in Oxidation of NO and CO

2022· article· en· W4229331889 on OpenAlexafffund
Ewa M. Iwanek, Leonarda Francesca Liotta, Shazam Williams, Linjie Hu, Huitian Ju, G. Pantaleo, Zbigniew Kaszkur, Donald W. Kirk, Wojciech Patkowski, Marek Gliński

Bibliographic record

VenueCatalysts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Toronto
FundersPolitechnika WarszawskaUniversity of Toronto
KeywordsAlkali metalCatalysisInorganic chemistryIonPotassiumRedoxMetalMaterials scienceDopingChemistryMetallurgy

Abstract

fetched live from OpenAlex

The aim of these studies was to perform thorough research on the influence of alkali metal ions (Li, Na, K and Cs) on the properties of nanogold catalysts supported on ceria–zirconia. The addition of alkali metal ions onto CeO2 further affected the reducibility, which was not noted for the Zr-doped support (Ce0.85Zr0.15O2). Despite the substantial impact of alkali metal ions on the reducibility of ceria, the activity in CO oxidation did not change much. In contrast, they do not have a large effect on the reducibility of Au/CZ but suppressed the activity of this system in CO oxidation. The results show that for CO oxidation, the negative effect of potassium ions is greater than that of sodium, which corresponds to the shift in the Tmax of the reduction peak towards higher temperatures. The negative effect of Li+ and Cs+ spans 50% CO conversion. The negative effect was visible for CO oxidation in both the model stream and the complex stream, which also contained hydrocarbons and NO. In the case of NO oxidation to NO2, two temperature regimes were observed for Au + 0.3 at% K/CZ, namely in the temperature range below 350 °C; the effect of potassium ions was beneficial for NO oxidation, whereas at higher temperatures, the undoped gold catalyst produced more NO2.

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)0.001
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 score0.074
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.027
GPT teacher head0.336
Teacher spread0.310 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCatalystsSame topicCatalytic Processes in Materials ScienceFrench-language works237,207