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Record W4211027999 · doi:10.1038/s41467-022-28456-9

A metal-supported single-atom catalytic site enables carbon dioxide hydrogenation

2022· article· en· W4211027999 on OpenAlexafffund
Sung‐Fu Hung, Aoni Xu, Xue Wang, Fengwang Li, Shao‐Hui Hsu, Yuhang Li, Joshua Wicks, Eduardo González Cervantes, Armin Sedighian Rasouli, Yuguang Li, Mingchuan Luo, Dae‐Hyun Nam, Ning Wang, Tao Peng, Yu Yan, Geonhui Lee, Edward H. Sargent

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersCanadian Light SourceNatural Sciences and Engineering Research Council of CanadaMinistry of Science and Technology, TaiwanUniversity of TorontoGovernment of CanadaOffice of ScienceIndustrial Technology Research InstituteNatural Resources CanadaMinistry of EducationGovernment of OntarioOntario Research FoundationArgonne National LaboratoryU.S. Department of Energy
KeywordsCatalysisCopperInorganic chemistryGrapheneMethaneMaterials scienceAdsorptionRaman spectroscopyMetalFaraday efficiencyElectrocatalystPhotochemistryChemistryElectrolytePhysical chemistryNanotechnologyElectrochemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nitrogen-doped graphene-supported single atoms convert CO 2 to CO, but fail to provide further hydrogenation to methane – a finding attributable to the weak adsorption of CO intermediates. To regulate the adsorption energy, here we investigate the metal-supported single atoms to enable CO 2 hydrogenation. We find a copper-supported iron-single-atom catalyst producing a high-rate methane. Density functional theory calculations and in-situ Raman spectroscopy show that the iron atoms attract surrounding intermediates and carry out hydrogenation to generate methane. The catalyst is realized by assembling iron phthalocyanine on the copper surface, followed by in-situ formation of single iron atoms during electrocatalysis, identified using operando X-ray absorption spectroscopy. The copper-supported iron-single-atom catalyst exhibits a CO 2 -to-methane Faradaic efficiency of 64% and a partial current density of 128 mA cm −2 , while the nitrogen-doped graphene-supported one produces only CO. The activity is 32 times higher than a pristine copper under the same conditions of electrolyte and bias.

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: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.803

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.266
Teacher spread0.246 · 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

Citations202
Published2022
Admission routes2
Has abstractyes

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