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Record W4210321824 · doi:10.1021/acsenergylett.1c02522

Continuum Model to Define the Chemistry and Mass Transfer in a Bicarbonate Electrolyzer

2022· article· en· W4210321824 on OpenAlexafffund
Eric W. Lees, Justin C. Bui, Datong Song, Adam Z. Weber, Curtis P. Berlinguette

Bibliographic record

VenueACS Energy Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsNational Research Council CanadaCanadian Institute for Advanced ResearchUniversity of British Columbia
FundersBasic Energy SciencesNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaTotalU.S. Department of EnergyKillam TrustsOffice of ScienceCanada First Research Excellence FundCanada Research ChairsCanadian Institute for Advanced ResearchNational Science Foundation
KeywordsBicarbonateMass transferElectrolysisElectrochemistryChemistryCatalysisCathodeChemical engineeringHydrogen productionMembraneHydrogenInorganic chemistryElectrodeChromatographyOrganic chemistryPhysical chemistryElectrolyte

Abstract

fetched live from OpenAlex

Bicarbonate electrolyzers are devices designed to convert CO 2 captured from point sources or the atmosphere into chemicals and fuels without needing to first isolate pure CO 2 gas. We report here an experimentally validated model that quantifies the reaction chemistry and mass transfer processes within the catalyst layer and cation exchange membrane layer of a bicarbonate electrolyzer. Our results demonstrate that two distinct chemical microenvironments are key to forming CO at high rates: an acidic membrane layer that promotes in situ CO 2 formation and a basic catalyst layer that suppresses the hydrogen evolution reaction. We show that the rate of CO product formation can be increased by modulating the catalyst and membrane layer properties to increase the rate of in situ CO 2 generation and transport to the cathode. These insights serve to inform the design of bicarbonate and BPM-based CO 2 electrolyzers while demonstrating the value of modeling for resolving rate-determining processes in electrochemical systems.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations96
Published2022
Admission routes2
Has abstractyes

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