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Record W2779986708 · doi:10.1021/acsenergylett.7b01017

Electrolysis of Gaseous CO<sub>2</sub> to CO in a Flow Cell with a Bipolar Membrane

2017· article· en· W2779986708 on OpenAlexafffund
Danielle A. Salvatore, David M. Weekes, Jingfu He, Kevan E. Dettelbach, Yuguang Li, Thomas E. Mallouk, Curtis P. Berlinguette

Bibliographic record

VenueACS Energy Letters · 2017
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of British Columbia
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada First Research Excellence FundKillam TrustsCanada Foundation for InnovationCanadian Institute for Advanced Research
KeywordsElectrolysisAnodeCathodeFaraday efficiencyPolymer electrolyte membrane electrolysisElectrolyteChemical engineeringMaterials scienceMembrane electrode assemblyElectrolytic cellMembraneChemistryInorganic chemistryElectrode

Abstract

fetched live from OpenAlex

The conversion of CO 2 to CO is demonstrated in an electrolyzer flow cell containing a bipolar membrane at current densities of 200 mA/cm 2 with a Faradaic efficiency of 50%. Electrolysis was carried out by delivering gaseous CO 2 at the cathode with a silver catalyst integrated in a carbon-based gas diffusion layer. Nonprecious nickel foam in a strongly alkaline electrolyte (1 M NaOH) was used to mediate the anode reaction. While a configuration where the anode and cathode were separated by only a bipolar membrane was found to be unfavorable for robust CO 2 reduction, a modified configuration with a solid-supported aqueous layer inserted between the silver-based catalyst layer and the bipolar membrane enhanced the cathode selectivity for CO 2 reduction to CO. We report higher current densities (200 mA/cm 2 ) than previously reported for gas-phase CO 2 to CO electrolysis and demonstrate the dependence of long-term stability on adequate hydration of the CO 2 inlet stream.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.210
Teacher spread0.204 · 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

Citations348
Published2017
Admission routes2
Has abstractyes

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