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Record W4211098009 · doi:10.1021/acsami.1c24321

Activating Surface Lattice Oxygen of a Cu/Zn<sub>1<i>–x</i></sub>Cu<sub><i>x</i></sub>O Catalyst through Interface Interactions for CO Oxidation

2022· article· en· W4211098009 on OpenAlexafffund
Minli Zeng, Xiyang Wang, Qilei Yang, Xuefeng Chu, Zuolong Chen, Zhen Li, Carl Redshaw, Chao Wang, Yue Peng, Nannan Wang, Yanqiu Zhu, Yimin A. Wu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Waterloo
FundersHefei Science Center, Chinese Academy of SciencesEngineering and Physical Sciences Research CouncilNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsCatalysisX-ray photoelectron spectroscopyOxygenMaterials scienceOxideMetalRedoxPhysical chemistryPhotochemistryChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Surface lattice oxygen in metal oxides is a common participant in many chemical reactions. Given this, the structural design of catalysts to activate lattice oxygen and moreover investigations into the effect of lattice oxygen on reaction pathways are hot topics. With this in mind, herein we prepare CuO–Zn1–xCuxO (ZCO) nanofibers akin to the Trojan horse legend and via an in situ reduction obtain activated Cu/Zn1–xCuxO (Cu/ZCO) nanofibers. X-ray absorption spectroscopy and X-ray photoelectron spectroscopy reveal that surface lattice oxygen of Cu/ZCO is effectively activated from inert O2– to reactive O2–x. This activation stems from the enhanced covalence of metal–oxygen bonds and the electron transfer between Cu and the support. Online mass spectrometry reveals that Cu/ZCO with activated lattice oxygen exhibits a higher Mars–van Krevelen reaction efficiency during the CO oxidation process. This study offers a new avenue to engineer interface interactions, given, as highlighted here, the importance of surface lattice oxygen in oxide supports during the catalytic process.

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)0.003
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 score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.288
Teacher spread0.265 · 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

Citations29
Published2022
Admission routes2
Has abstractyes

Explore more

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