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Record W3165555416 · doi:10.1126/science.abg6582

CO <sub>2</sub> electrolysis to multicarbon products in strong acid

2021· article· en· W3165555416 on OpenAlexafffund
Jianan Erick Huang, Fengwang Li, Adnan Ozden, Armin Sedighian Rasouli, F. Pelayo Garcı́a de Arquer, Shijie Liu, Shuzhen Zhang, Mingchuan Luo, Xue Wang, Yanwei Lum, Yi Xu, Koen Bertens, Rui Kai Miao, Cao‐Thang Dinh, David Sinton, Edward H. Sargent

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersAustralian Research CouncilArgonne National LaboratoryNatural Sciences and Engineering Research Council of CanadaOffice of ScienceOntario Research Foundation
KeywordsElectrolysisChemistryEnvironmental scienceElectrodeElectrolyte

Abstract

fetched live from OpenAlex

Potassium helps CO 2 compete in acid Electrochemical reduction of carbon dioxide (CO 2 ) is a promising means of converting this greenhouse gas into valuable fuels and chemicals. However, two competing reactions restrict the efficiency of this process. In base, much of the CO 2 is trapped as carbonate before reduction; in acid, protons outpace CO 2 at catching electrons from the cathode. Huang et al. report that a high dose of potassium ions can help to solve the latter problem. By concentrating potassium ions at the electrode, high selectivity toward CO 2 reduction at high current in acid is possible, which the authors attribute to electrostatic stabilization of the desired adsorbates. Science , abg6582, this issue p. 1074

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 score0.004
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations1,274
Published2021
Admission routes2
Has abstractyes

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