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Record W4392168938 · doi:10.1038/s41467-024-45538-y

In situ copper faceting enables efficient CO2/CO electrolysis

2024· article· en· W4392168938 on OpenAlexafffund
Kaili Yao, Jun Li, Adnan Ozden, Haibin Wang, Ning Sun, Pengyu Liu, Wen Zhong, Wei Zhou, Jieshu Zhou, Xi Wang, Hanqi Liu, Yongchang Liu, Songhua Chen, Yongfeng Hu, Ziyun Wang, David Sinton, Hongyan Liang

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaMarsden FundOntario Research FoundationNational Natural Science Foundation of ChinaCanada Research ChairsRoyal Society Te ApārangiShanghai Jiao Tong UniversityPaul Scherrer Institut
KeywordsFaraday efficiencyCopperFacetingElectrochemistryCatalysisElectrolysisCarbon fibersMaterials scienceElectrocatalystAdsorptionChemical engineeringHydroxideInorganic chemistryChemistryElectrodeCrystallographyPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract The copper (Cu)-catalyzed electrochemical CO 2 reduction provides a route for the synthesis of multicarbon (C 2+ ) products. However, the thermodynamically favorable Cu surface (i.e. Cu(111)) energetically favors single-carbon production, leading to low energy efficiency and low production rates for C 2+ products. Here we introduce in situ copper faceting from electrochemical reduction to enable preferential exposure of Cu(100) facets. During the precatalyst evolution, a phosphate ligand slows the reduction of Cu and assists the generation and co-adsorption of CO and hydroxide ions, steering the surface reconstruction to Cu (100). The resulting Cu catalyst enables current densities of > 500 mA cm −2 and Faradaic efficiencies of >83% towards C 2+ products from both CO 2 reduction and CO reduction. When run at 500 mA cm −2 for 150 hours, the catalyst maintains a 37% full-cell energy efficiency and a 95% single-pass carbon efficiency throughout.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.450

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 science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.307
Teacher spread0.295 · 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 designNot applicable
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

Citations111
Published2024
Admission routes2
Has abstractyes

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