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Record W4312207601 · doi:10.1021/acsnano.2c08453

Strain in Copper/Ceria Heterostructure Promotes Electrosynthesis of Multicarbon Products

2022· article· en· W4312207601 on OpenAlexaff
Haibin Wang, Hao Zhang, Yan Huang, Haiyu Wang, Adnan Ozden, Kaili Yao, Huamin Li, Qianying Guo, Yongchang Liu, Alberto Vomiero, Yuhang Wang, Zhao Qian, Jun Li, Ziyun Wang, Xuhui Sun, Hongyan Liang

Bibliographic record

VenueACS Nano · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersHigher Education Discipline Innovation ProjectCollaborative Innovation Center of Suzhou Nano Science and TechnologyNational Natural Science Foundation of ChinaSalt Science Research FoundationSuzhou Key Laboratory of Functional Nano and Soft Materials
KeywordsCopperMaterials scienceHeterojunctionFaraday efficiencyCatalysisSelectivityStrain (injury)MetalOxideElectrosynthesisChemical engineeringCopper oxideAdsorptionInorganic chemistryChemistryMetallurgyElectrochemistryOptoelectronicsElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Elastic strains in metallic catalysts induce enhanced selectivity for carbon dioxide reduction (CO2R) toward valuable multicarbon (C2+) products. However, under working conditions, the structure of catalysts inevitably undergoes reconstruction, hardly retaining the initial strain. Herein, we present a metal/metal oxide synthetic strategy to introduce and maintain the tensile strain in a copper/ceria heterostructure, enabled by the presence of a thin interface layer of Cu2O/CeO2. The tensile strain in the copper domain and deficient electron environment around interfacial Cu sites resulted in strengthened adsorption of carbonaceous intermediates and promoted *CO dimerization. The strain effect in the copper/ceria heterostructure leads to an improved C2+ selectivity with a maximum Faradaic efficiency of 76.4% and a half-cell power conversion efficiency of 49.1%. The fundamental insights gained from this system can facilitate the rational design of heterostructure catalysts for CO2R.

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)0.000
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 score0.015
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations67
Published2022
Admission routes1
Has abstractyes

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