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Record W4214726573 · doi:10.1002/aenm.202200195

Highly‐Exposed Single‐Interlayered Cu Edges Enable High‐Rate CO<sub>2</sub>‐to‐CH<sub>4</sub> Electrosynthesis

2022· article· en· W4214726573 on OpenAlexaff
Peng Chen, Zikai Xu, Gan Luo, Shuai Yan, Junbo Zhang, Si Li, Yangsheng Chen, Lo Yueh Chang, Zhiqiang Wang, Tsun‐Kong Sham, Gengfeng Zheng

Bibliographic record

VenueAdvanced Energy Materials · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsWestern University
FundersNational Key Research and Development Program of ChinaScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsElectrosynthesisMaterials scienceFaraday efficiencyElectrolysisCurrent densityDensity functional theoryElectrochemistryCatalysisSingle crystalNanosheetChemical engineeringInorganic chemistryNanotechnologyPhysical chemistryElectrodeCrystallographyElectrolyteChemistryComputational chemistry

Abstract

fetched live from OpenAlex

Abstract The electrochemical CO 2 reduction to CH 4 is a promising approach for producing highly specific combustion fuel but has relatively poor selectivity and activity at high‐current‐density electrolysis. In this work, ultrathin CuGaO 2 nanosheets with highly exposed single‐interlayered Cu edges are synthesized via an induced anisotropic growth strategy. Density functional theory calculations indicate that the exposed single‐interlayered Cu(I) edges on the (001) surface of CuGaO 2 present a high‐density of single‐atomic Cu sites, which feature excellent CO 2 electroreduction catalytic activity toward CH 4 . The CuGaO 2 nanosheet catalysts exhibit efficient and stable CO 2 ‐to‐CH 4 electroreduction with Faradaic efficiency (FE CH4 ) of 71.7% at a high current density of –1 A cm −2 , corresponding to a superior CH 4 partial current density of 717 ± 33 mA cm −2 . This work suggests an attractive design strategy for tuning both the crystal facets and Cu–Cu distance to promote the CH 4 electrosynthesis at high‐current‐density CO 2 reduction.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.218
Teacher spread0.207 · 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

Citations58
Published2022
Admission routes1
Has abstractyes

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