MétaCan
Menu
Back to cohort
Record W3045566820 · doi:10.1021/acsenergylett.0c01291

Conversion of Bicarbonate to Formate in an Electrochemical Flow Reactor

2020· article· en· W3045566820 on OpenAlexafffund
Tengfei Li, Eric W. Lees, Zishuai Zhang, Curtis P. Berlinguette

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 CO 2 reduction studies typically supply CO 2 to the cathode as a gas or dissolved in aqueous media. Both of these feedstocks present challenges when scaling a CO 2 electrolyzer: gaseous CO 2 feedstocks require significant energy to pressurize CO 2, while the low solubility of CO 2 in water precludes high current densities. Using a liquid bicarbonate feedstock bypasses the need for a gaseous CO 2 feedstock while delivering higher concentrations of CO 2 to the cathode than currently possible with CO 2 dissolved in water. We show here that an electrochemical flow cell can be designed such that protons convert bicarbonate into CO 2 (at the catalyst interface), which is then reduced to generate formate. Electrolysis of 3.0 M KHCO 3(aq) solutions yield formate at partial current densities > 100 mA cm –2, which is nearly commensurate with electrolyzers fed with gaseous CO 2 . The use of bicarbonate as a feedstock presents an opportunity to efficiently integrate carbon capture with CO 2 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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.009
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

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

Citations166
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueACS Energy LettersSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207