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Record W2952750469 · doi:10.1038/s41467-019-10464-x

Nickel@Siloxene catalytic nanosheets for high-performance CO2 methanation

2019· article· en· W2952750469 on OpenAlexafffund
Xiaoliang Yan, Wei Sun, Liming Fan, Paul N. Duchesne, Wu Wang, Christian Kübel, Di Wang, Sai Govind Hari Kumar, Young Feng Li, Alexandra Tavasoli, Thomas E. Wood, Darius L. H. Hung, Lili Wan, Lu Wang, Rui Song, Jiuli Guo, Ilya Gourevich, Feysal M. Ali, Jingjun Lu, Ruifeng Li, Benjamin D. Hatton, Geoffrey A. Ozin

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversity of Toronto
FundersArgonne National LaboratoryOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaOffice of ScienceNational Natural Science Foundation of ChinaNatural Science Foundation of Shanxi ProvinceLions Clubs International FoundationUniversity of TorontoMinistero dello Sviluppo EconomicoU.S. Department of Energy
KeywordsMethanationCatalysisNickelSelectivityMaterials scienceNucleationChemical engineeringNanocompositeNanotechnologyChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Two-dimensional (2D) materials are of considerable interest for catalyzing the heterogeneous conversion of CO 2 to synthetic fuels. In this regard, 2D siloxene nanosheets, have escaped thorough exploration, despite being composed of earth-abundant elements. Herein we demonstrate the remarkable catalytic activity, selectivity, and stability of a nickel@siloxene nanocomposite; it is found that this promising catalytic performance is highly sensitive to the location of the nickel component, being on either the interior or the exterior of adjacent siloxene nanosheets. Control over the location of nickel is achieved by employing the terminal groups of siloxene and varying the solvent used during its nucleation and growth, which ultimately determines the distinct reaction intermediates and pathways for the catalytic CO 2 methanation. Significantly, a CO 2 methanation rate of 100 mmol g Ni −1 h −1 is achieved with over 90% selectivity when nickel resides specifically between the sheets of siloxene.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations188
Published2019
Admission routes2
Has abstractyes

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Same venueNature CommunicationsSame topicCarbon dioxide utilization in catalysisFrench-language works237,207