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Record W2979947094 · doi:10.1021/acscatal.9b02443

Electrolyte Driven Highly Selective CO<sub>2</sub> Electroreduction at Low Overpotentials

2019· article· en· W2979947094 on OpenAlexafffund
Tengfei Li, Chao Yang, Jing‐Li Luo, Gengfeng Zheng

Bibliographic record

VenueACS Catalysis · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Alberta
FundersChina Scholarship CouncilScience and Technology Commission of Shanghai MunicipalityMinistry of Science and Technology of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsTafel equationCatalysisElectrolyteInorganic chemistryElectrochemistryChemistryRedoxAqueous solutionElectrocatalystChemical engineeringMaterials scienceElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO2 reduction reaction (CO2RR) is a promising technology to use renewable electricity to convert CO2 into value-added carbon-based products. The low-cost, active, selective, and stable catalysts will play a key role in achieving industrialized CO2RR. The electrolyte assists a catalyst in achieving all its latent capability. Here the concentration effect of KHCO3 in CO2RR was systematically investigated on the low-cost core–shell structured Cu2O@SnOx nanoparticle-derived hybrid catalyst. An HCO3–-involved proton-coupled electron transfer was confirmed as the rate-determining step for CO2RR on the hybrid catalyst in aqueous KHCO3 solution based on the analysis of the reaction order and Tafel slope. The nearly 100% selectivity for CO was achieved in a highly concentrated KHCO3 solution accompanied by a high cathodic energetic efficiency of 71.8%. It was attributed to the combined concentration effect of KHCO3 with the related pH effect.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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.

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

Citations63
Published2019
Admission routes2
Has abstractyes

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