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Record W4387672791 · doi:10.1021/acsenergylett.3c01716

Direct Membrane Deposition for CO<sub>2</sub> Electrolysis

2023· article· en· W4387672791 on OpenAlexafffund
Tartela Alkayyali, Ali Shayesteh Zeraati, Harrison Mar, Fatemeh Arabyarmohammadi, Sepehr Saber, Rui Kai Miao, Colin P. O’Brien, Hanshuo Liu, Zhong Xie, Guangyu Wang, Edward H. Sargent, Nana Zhao, David Sinton

Bibliographic record

VenueACS Energy Letters · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsNational Research Council CanadaUniversity of British ColumbiaUniversity of Toronto
FundersUniversity of TorontoAlbert-Ludwigs-Universität FreiburgNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNational Research Council CanadaBoettcher Foundation
KeywordsElectrolysisFaraday efficiencyMembraneDeposition (geology)ChemistryVoltageIonMaterials scienceChemical engineeringAnalytical Chemistry (journal)ElectrodeElectrochemistryChromatographyElectrical engineeringElectrolyteEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

The use of forward-bias bipolar membranes (f-BPM) in CO 2 electrolyzers offers the advantage of avoiding costly CO 2 reactant loss. However, current f-BPM-based electrolyzers require a high voltage and produce H 2 at the expense of CO 2 reduction products. In this work, we develop a direct membrane deposition (DMD) approach that combines anion and cation exchange membranes (AEM and CEM, respectively) to increase transport and facilitate CO 2 regeneration. The DMD approach provides flexibility to tune the properties of the composite and optimize the AEM:CEM ratio for low resistance and low H 2 evolution. Compared to a standard f-BPM, the DMD approach reduced the H 2 Faradaic efficiency by 2-fold (25% vs 12%, respectively), reduced mass transport resistance by over 50%, decreased full-cell potential by 0.84 V, increased the selectivity toward multicarbon products by over 2-fold (29% vs 65%, respectively), and achieved >17% in multicarbon product energy efficiency at 300 mA cm –2 .

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 score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations28
Published2023
Admission routes2
Has abstractyes

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