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Record W3016019453 · doi:10.1021/acsenergylett.0c00536

Molecular Catalysts Boost the Rate of Electrolytic CO<sub>2</sub> Reduction

2020· article· en· W3016019453 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Energy Letters · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersInstitut Universitaire de FranceChina Scholarship CouncilAir Liquide
KeywordsElectrolysisElectrochemical reduction of carbon dioxideCatalysisRedoxElectrochemistryMaterials scienceNanotechnologyElectrolyteCopperChemistryInorganic chemistryElectrodeCarbon monoxideMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Electrolysis is a potentially useful approach for converting carbon dioxide into chemicals and fuels. The most active electrocatalysts for efficiently mediating the carbon dioxide reduction reaction (CO2RR) have long been assumed to be solid silver, gold, and copper. However, there is an emerging body of data showing that molecular catalysts can operate at levels of performance commensurate with solid-state catalysts. These recent advances in deploying molecular catalysts present entirely new opportunities for understanding CO2RR in electrochemical reactors and tailoring active sites for the selective formation of CO2RR products. This Perspective highlights the recent advances and the opportunities for the implementation of molecular electrocatalysts into CO2RR electrolyzers.

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.

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 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 score0.018
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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