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Record W4378469979 · doi:10.1016/j.joule.2023.05.003

CO2 electroreduction to multicarbon products from carbonate capture liquid

2023· article· en· W4378469979 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJoule · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersOntario Research FoundationNatural Sciences and Engineering Research Council of CanadaNorthwestern University
KeywordsCarbonateElectrolyteHydroxideElectrochemistryThermal decompositionChemical engineeringPotassium hydroxideMaterials scienceChemistryWaste managementElectrodeInorganic chemistryEngineeringMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Alkali hydroxide systems capture CO 2 as carbonate; however, generating a pure CO 2 stream requires significant energy input, typically from thermal cycling to 900°C. What is more, the subsequent valorization of gas-phase CO 2 into products presents additional energy requirements and system complexities, including managing the formation of (bi)carbonate in an electrolyte and separating unreacted CO 2 downstream. Here, we report the direct electrochemical conversion of CO 2 , captured in the form of carbonate, into multicarbon (C 2+ ) products. Using an interposer and a Cu/CoPc-CNTs electrocatalyst, we achieve 47% C 2+ Faradaic efficiency at 300 mA cm −2 and a full cell voltage of 4.1 V. We report 56 wt % of C 2 H 4 and no detectable C 1 gas in the product gas stream: CO, CH 4 , and CO 2 combined total below 0.9 wt % (0.1 vol %). This approach obviates the need for energy to regenerate lost CO 2 , an issue seen in prior CO 2 -to-C 2+ reports.

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 score0.287
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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