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Record W2779210243 · doi:10.1021/acscentsci.7b00442

Theoretical Investigations of the Electrochemical Reduction of CO on Single Metal Atoms Embedded in Graphene

2017· article· en· W2779210243 on OpenAlexfundno aff
Charlotte Kirk, Leanne D. Chen, Samira Siahrostami, Mohammadreza Karamad, Michal Bajdich, Johannes Voss, Jens K. Nørskov, Karen Chan

Bibliographic record

VenueACS Central Science · 2017
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersBasic Energy SciencesDivision of Graduate EducationNatural Sciences and Engineering Research Council of CanadaStanford University
KeywordsGrapheneElectrochemistryReduction (mathematics)Materials scienceMetalNanotechnologyChemistryElectrodeMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

product formation. On these single atom sites, the corresponding barrier scales more favorably with the CO binding energy than for 211 and 111 transition metal surfaces, in the direction of improved activity. Intermediates and transition states for the hydrogen evolution reaction were found to be less stable than those on transition metals, suggesting a higher selectivity for CO reduction. We present a rate volcano for the production of methane from CO. We identify promising candidates with high activity, stability, and selectivity for the reduction of CO. This work highlights the potential of these systems as improved electrocatalysts over pure transition metals for CO 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.269
Teacher spread0.255 · 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 designSimulation or modeling
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

Citations66
Published2017
Admission routes1
Has abstractyes

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