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Record W2804235153 · doi:10.1126/science.aas9100

CO <sub>2</sub> electroreduction to ethylene via hydroxide-mediated copper catalysis at an abrupt interface

2018· article· en· W2804235153 on OpenAlexaff
Cao‐Thang Dinh, Thomas Burdyny, Md Golam Kibria, Ali Seifitokaldani, Christine M. Gabardo, F. Pelayo Garcı́a de Arquer, Amirreza Kiani, Jonathan P. Edwards, Phil De Luna, Oleksandr S. Bushuyev, Chengqin Zou, Rafael Quintero‐Bermudez, Yuanjie Pang, David Sinton, Edward H. Sargent

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrocatalystElectrolyteCopperPotassium hydroxideCatalysisInorganic chemistryHydroxideFaraday efficiencyEthyleneChemistryCarbon monoxideChemical engineeringElectrodeMaterials scienceElectrochemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A very basic pathway from CO 2 to ethylene Ethylene is an important commodity chemical for plastics. It is considered a tractable target for synthesizing renewably from carbon dioxide (CO 2 ). The challenge is that the performance of the copper electrocatalysts used for this conversion under the required basic reaction conditions suffers from the competing reaction of CO 2 with the base to form bicarbonate. Dinh et al. designed an electrode that tolerates the base by optimizing CO 2 diffusion to the catalytic sites (see the Perspective by Ager and Lapkin). This catalyst design delivers 70% efficiency for 150 hours. Science , this issue p. 783 ; see also p. 707

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.279
Teacher spread0.270 · 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

Citations2,439
Published2018
Admission routes1
Has abstractyes

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