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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 CO 2 reduction reaction (CO 2 RR) is a promising technology to use renewable electricity to convert CO 2 into value-added carbon-based products. The low-cost, active, selective, and stable catalysts will play a key role in achieving industrialized CO 2 RR. The electrolyte assists a catalyst in achieving all its latent capability. Here the concentration effect of KHCO 3 in CO 2 RR was systematically investigated on the low-cost core–shell structured Cu 2 O@SnO x nanoparticle-derived hybrid catalyst. An HCO 3 – -involved proton-coupled electron transfer was confirmed as the rate-determining step for CO 2 RR on the hybrid catalyst in aqueous KHCO 3 solution based on the analysis of the reaction order and Tafel slope. The nearly 100% selectivity for CO was achieved in a highly concentrated KHCO 3 solution accompanied by a high cathodic energetic efficiency of 71.8%. It was attributed to the combined concentration effect of KHCO 3 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 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 score0.002
Threshold uncertainty score0.005

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)0.0020.001

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 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

Citations63
Published2019
Admission routes2
Has abstractyes

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