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Record W3041581558 · doi:10.1039/d0nr03028g

Ce–Mn coordination polymer derived hierarchical/porous structured CeO<sub>2</sub>–MnO<sub>x</sub> for enhanced catalytic properties

2020· article· en· W3041581558 on OpenAlexaff
Junwei Feng, Yong Wang, Daowei Gao, Baotao Kang, Shun Li, Chunsheng Li, Guozhu Chen

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsInstitut National de la Recherche ScientifiqueCollège Lionel Groulx
FundersNatural Science Foundation of Shandong ProvinceScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of China
KeywordsCatalysisMaterials sciencePorosityCoordination polymerPolymerChemical engineeringCrystallographyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Catalytic performance is largely dependent on how the structures/compositions of materials are designed. Herein, CeO2-MnOx binary oxide catalysts with a hierarchical/porous structure are prepared by a facile and efficient method, which involves the preparation of the hierarchical Ce-Mn coordination polymer (CPs) precursor, followed by a thermal treatment step. The obtained CeO2-MnOx catalysts not only well inherit the hierarchical structure of Ce-Mn CPs, but also possess porous and hollow features due to the removal of organic ligands and heterogeneous contraction during the calcination process. In addition, the effect of the Mn/Ce ratio is also studied to optimize catalytic performance. Specifically, the as-prepared CeO2-MnOx (5 : 5) catalyst exhibits excellent catalytic performance toward CO oxidation and selective catalytic reduction (SCR) of NO with NH3 at low temperatures. Based on the characterization results, we propose that the special hierarchical structure, high surface area, strong synergistic interaction between CeO2 and MnOx, and high content of active Ce3+, Mn4+ and Osurf are collectively responsible for its remarkable catalytic performance.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations36
Published2020
Admission routes1
Has abstractyes

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