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Record W4391640401 · doi:10.1149/ma2023-02167mtgabs

Utilizing Direct Membrane Deposition to Improve the Performance of Forward-Bias Bipolar Membrane CO<sub>2</sub> Electrolysers

2023· article· en· W4391640401 on OpenAlexaff
Tartela Alkayyali, Ali Shayesteh, Harrison Mar, Fatemeh Arabyarmohammadi, Rui Kai Miao, Colin P. O’Brien, Edward H. Sargent, Nana Zhao, David Sinton

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council CanadaUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsMembraneDeposition (geology)Materials scienceEnvironmental scienceChemistryGeology

Abstract

fetched live from OpenAlex

The electrochemical conversion of CO2 to multi-carbon products (C2+), such as ethylene and ethanol, is an attractive technology towards achieving net zero carbon emission goals. Among the available configurations of CO2 electrolysers, those employing a bipolar membrane (BPM) in forward-bias mode (f-BPM) reduce CO2 loss to the anode – a problem commonly faced in traditional anion exchange membrane (AEM) electrolysers. Therefore, carbon efficiency (the percent of input CO2 that is converted to C2+ products) and system operational costs can be improved. In f-BPM electrolysers, (bi)carbonate ions move through the AEM then combine with protons moving through the cation exchange membrane (CEM) to regenerate CO2 and H2O at the AEM|CEM interface. Recent reports have implemented a porous AEM structure or an added AEM|CEM interface channel to aid the movement of CO2 from the AEM|CEM interface to the cathode and, thus, avoid blistering. Despite the advantage of f-BPM CO2 electrolysers, their performance is limited by the parasitic H2 evolution reaction (>20% Faradaic efficiency, or FE) and the low C2+ FE (<40%) at industrially relevant reaction rates (≥200 mA cm-2). We attribute this performance limitation to the unintended gaps between the used AEM and CEM. Inadequate contact between the AEM and CEM is anticipated to reduce the extent of local CO2 regeneration and promote unwanted proton crossover to the cathode; both phenomena could be contributing to the commonly observed FEs. In this report, we enhanced the contact between the AEM and CEM through the use of direct membrane deposition (DMD) over a Cu-based cathode. The DMD approach enabled modular control over the AEM and CEM thicknesses and structures, which improved ion and gas transport. Through electrochemical impedance spectroscopy, the DMD system was observed to improve the mass transport by >58% compared to a system that is similar to current f-BPM electrolysers (i.e., control system). The facilitated mass transport resulted in a reduction of the operating cell potential (by 0.84 V) and an enhancement of the C2+ FE to 65% (from 29% in the control system) at 300 mA cm-2. The flexibility of the DMD approach also enabled the fabrication of asymmetric BPMs, resulting in a record low H2 FE of 12% at 300 mA cm-2. We also demonstrated a 79% single-pass CO2 conversion (SPC) with 67% C2+ FE, resembling the highest simultaneous SPC and C2+ FE achievement at high current density (300 mA cm-2)among current f-BPM electrolysers. The DMD benefit was also applicable to other types of CO2 electrolysers, such as CO2-to-CO silver catalyst electrolysers, in which the CO FE was boosted to 90% (from 65% in the control system) at 90 mA cm-2. Figure 1

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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