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Record W3045566820 · doi:10.1021/acsenergylett.0c01291

Conversion of Bicarbonate to Formate in an Electrochemical Flow Reactor

2020· article· en· W3045566820 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

VenueACS Energy Letters · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersNatural Resources CanadaCanadian Institute for Advanced Research
KeywordsFormateElectrolysisElectrochemistryCathodeBicarbonateRaw materialInorganic chemistryChemistryAqueous solutionSolubilityCarbon dioxideCarbon fibersChemical engineeringCatalysisMaterials scienceElectrodeElectrolyteOrganic chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO2 reduction studies typically supply CO2 to the cathode as a gas or dissolved in aqueous media. Both of these feedstocks present challenges when scaling a CO2 electrolyzer: gaseous CO2 feedstocks require significant energy to pressurize CO2, while the low solubility of CO2 in water precludes high current densities. Using a liquid bicarbonate feedstock bypasses the need for a gaseous CO2 feedstock while delivering higher concentrations of CO2 to the cathode than currently possible with CO2 dissolved in water. We show here that an electrochemical flow cell can be designed such that protons convert bicarbonate into CO2 (at the catalyst interface), which is then reduced to generate formate. Electrolysis of 3.0 M KHCO3(aq) solutions yield formate at partial current densities > 100 mA cm–2, which is nearly commensurate with electrolyzers fed with gaseous CO2. The use of bicarbonate as a feedstock presents an opportunity to efficiently integrate carbon capture with CO2 electrochemistry.

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 score0.035
Threshold uncertainty score0.462

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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