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Record W4323812719 · doi:10.1002/smll.202207374

Over 2 A cm<sup>−2</sup> CO<sub>2</sub>‐to‐Ethanol Conversion by Alkali‐Metal Cation Induced Copper With Dominant (200) Facets

2023· article· en· W4323812719 on OpenAlexaff
Peng Chen, Songtao Yang, Gan Luo, Shuai Yan, Mohsen Shakouri, Junbo Zhang, Yangshen Chen, Zhiqiang Wang, Wei Wei, Tsun‐Kong Sham, Gengfeng Zheng

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsWestern UniversityCanadian Light Source (Canada)University of Saskatchewan
FundersNational Key Research and Development Program of ChinaScience and Technology Commission of Shanghai MunicipalityShanghai Municipal Education CommissionNational Natural Science Foundation of China
KeywordsElectrosynthesisElectrochemistryCatalysisAlkali metalEthanolCopperChemistryInorganic chemistryMetalSelectivityDensity functional theoryOxygenatePhysical chemistryOrganic chemistryElectrodeComputational chemistry

Abstract

fetched live from OpenAlex

Abstract The high‐rate ethanol electrosynthesis from CO 2 is challenging due to the low selectivity and poor activity, which requires the competition with other reduction products and H 2 . Here, the electrochemical reconstruction of Cs 3 Cu 2 Cl 5 perovskite to form surface Cl‐bonded, low‐coordinated Cs modified Cu(200) nanocubes (CuClCs), is demonstrated. Density functional theory calculations reveal that the CuClCs structure possesses low Bader charges and a large coordination capacity; and thus, can promote the CO 2 ‐to‐ethanol pathway via stabilizing C−O bond in oxygenate intermediates. The CuClCs catalyst exhibits outstanding partial current densities for producing ethanol (up to 2124 ± 54 mA cm −2 ) as one of the highest reported values in the electrochemical CO 2 or CO reduction. This work suggests an attractive strategy with surface alkali‐metal cations for ampere‐level CO 2 ‐to‐ethanol electrosynthesis.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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

Citations31
Published2023
Admission routes1
Has abstractyes

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