Using Edge-Type Reference Electrodes to Estimate Anode and Cathode Overpotentials in Alkaline Exchange Membrane Fuel Cells and Electrolysers
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".