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Record W4416599835 · doi:10.1149/ma2025-02422077mtgabs

Using Edge-Type Reference Electrodes to Estimate Anode and Cathode Overpotentials in Alkaline Exchange Membrane Fuel Cells and Electrolysers

2025· article· W4416599835 on OpenAlexaff
Jiafei Liu, Jake Mouallem, A. Quintero

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAnodeCathodeElectrodeProton exchange membrane fuel cellMembraneElectrochemistryReference electrode

Abstract

fetched live from OpenAlex

Electrochemical systems based on anion exchange membranes (AEM) can operate effectively using non-precious group metal (non-PGM) catalysts, thereby allowing for critical cost-reductions in fuel cells and electrolysers. In alkaline systems, both electrodes might contribute significantly to overall cell potential losses, therefore numerous studies have emphasized the critical need for three-electrode setups to gain a better understanding of individual electrode behaviour [1,2]. In AEM-based electrochemical cells, three reference electrode (RE) configurations are commonly used: edge-type, sandwich-type, and salt-bridge-type. Most studies in operating fuel cells have utilized an edge-type RE, where the RE is positioned at the membrane edge near the active electrode. Edge-type REs are preferred because they are straightforward to implement, require no modifications to the cell configuration, and do not interfere with cell performance; however, a significant concern with edge-type REs is that they might be highly sensitive to the position of the active electrodes, as demonstrated in proton exchange membrane fuel cells [3-5]. Therefore, it is of paramount importance to develop a methodology to quickly fabricate cells with reference electrodes, and to study the role of active electrode alignment in predicted results. In this presentation, we proposed that inkjet-printing can be used to manufacture AEMFC and AEMWE catalyst-coated membranes (CCMs) with precise control over anode and cathode positioning, and to concurrently deposit multiple edge-type reference electrodes. To this end, AEMFC CCMs with well-aligned electrodes and four reference electrodes are inkjet printed, and characterized electrochemically. To study the effect of misalignment, AEMFC CCMs were also fabricated with purposely misaligned electrodes, where there is a 0.5 cm offset between anode and cathode. A two-dimensional membrane electrode assembly model is then developed to analyze how the measured potential in the reference electrode relates to the overpotentials observed in different parts of the electrode, and to study the effect of electrode misalignment. Finally, an AEMWE cell with a reference electrode is also fabricated and tested. Experimental AEMFC results at 60 0 C, 90% relative humidity and 150 kPa g of MEAs with aligned and misaligned electrodes, together with detailed 2D simulations, we show that: a) electrode misalignment can easily be detected by means of multiple electrodes; b) misalignment can result in large errors in the estimation of the overpotential; c) accurate gasketing can minimize the effects of misalignment; and, d) simulations are needed to determine the overpotential field and a mean overpotential. Furthermore, our results show that both in AEMFC and AEMWE hydrogen electrode losses are much larger than expected based on electrode kinetics and its origin requires further investigation. References: [1] S. Gottesfeld et al., J. Power Sources 375 (2018) 170–184. [2] A. Carlson et al. J. Electrochem. Soc. 168 (3) (2021) 034505. [3] Z. Liu et al. Electrochimica Acta 49 (6) (2004) 923–935. [4] A. Kulikovsky et al. J. Electrochem. Soc. 162 (8) (2015) F843. [5] J. H. Ohs et al. J. Electrochem. Soc. 159 (7) (2012) F181. 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)0.002
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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
Published2025
Admission routes1
Has abstractyes

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