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Record W3106830610 · doi:10.1038/s41467-020-20004-7

Promoting CO2 methanation via ligand-stabilized metal oxide clusters as hydrogen-donating motifs

2020· article· en· W3106830610 on OpenAlexafffund
Yuhang Li, Aoni Xu, Yanwei Lum, Xue Wang, Sung‐Fu Hung, Bin Chen, Ziyun Wang, Yi Xu, Fengwang Li, Jehad Abed, Jianan Erick Huang, Armin Sedighian Rasouli, Joshua Wicks, Laxmi Kishore Sagar, Tao Peng, Alexander H. Ip, David Sinton, Hao Jiang, Chunzhong Li, Edward H. Sargent

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaGovernment of OntarioOffice of ScienceNatural Resources CanadaCanadian Light SourceUniversity of TorontoU.S. Department of Energy
KeywordsMethaneMethanationOxideFaraday efficiencyCatalysisInorganic chemistryHydrogenMaterials scienceMetalCarbon fibersChemical engineeringChemistryElectrochemistryOrganic chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Electroreduction uses renewable energy to upgrade carbon dioxide to value-added chemicals and fuels. Renewable methane synthesized using such a route stands to be readily deployed using existing infrastructure for the distribution and utilization of natural gas. Here we design a suite of ligand-stabilized metal oxide clusters and find that these modulate carbon dioxide reduction pathways on a copper catalyst, enabling thereby a record activity for methane electroproduction. Density functional theory calculations show adsorbed hydrogen donation from clusters to copper active sites for the *CO hydrogenation pathway towards *CHO. We promote this effect via control over cluster size and composition and demonstrate the effect on metal oxides including cobalt(II), molybdenum(VI), tungsten(VI), nickel(II) and palladium(II) oxides. We report a carbon dioxide-to-methane faradaic efficiency of 60% at a partial current density to methane of 135 milliampere per square centimetre. We showcase operation over 18 h that retains a faradaic efficiency exceeding 55%.

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)0.001
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 score0.226
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
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 head0.024
GPT teacher head0.298
Teacher spread0.274 · 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

Citations147
Published2020
Admission routes2
Has abstractyes

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