Engineering and Testing of CCM Modifications for Improved Operational Flexibility, Durability and Performance of Fuel Cells and Electrolyzers
Bibliographic record
Abstract
The performance, durability and cost of PEM fuel cell and electrolyzer systems still requires further improvements before they can be widely adopted. Along with improved electrocatalysts, low cost interface modifications and intermediate layers are important to improve the operation of catalyst coated membranes (CCMs) and meet commercial requirements. In this presentation we present some examples of the modification of conventional commercial CCM and MEA modifications with performance benefits. This would include for example the effect of improving the microporous layer (MPL) / catalyst layer (CL) interface by reducing the gaps to improve power density1, the improvement of cross-over and operational flexibility with a thin electrolessly deposited catalyst layer at the membrane surface 2, and modification of the porous transport layer (PTL) / catalyst interface3. A new testing method for the evaluation of commercial CCMs was used in this work which can accelerate design and testing of these new and modified CCMs. This new method uses a Modified Rotating Disk Electrode (MRDE)4 which allows electrodes and CCMs to be tested up to high current densities, e.g., 2 A/cm2, and eliminates the variability and issues associated with thin film RDE testing. Figure 1 shows an example of testing the oxygen evolution reaction (OER) performance for different PTLs with a commercial CCM using the MRDE. The MRDE testing is also useful for carrying out accelerated degradation (ADT) testing of CCMs for fuel cell or electrolyzer applications5 and has the potential to be used as a quality control tool for CCM manufacturing lines. References: L. Daniel, A. Bonakdarpour and D.P. Wilkinson, Fuel Cells, 20(2), F1-F7 (2020) L.Daniel, A. Bonakdarpour and D.P. Wilkinson, ACS Applied Nano Materials, 2, 3127-3137 (2019); J. of Power Sources, 471, 228418 (2020) M. Kroschel, A. Bonakdarpour, J.T.H. Kwan, P. Strasser and D.P. Wilkinson, Electrochim. Acta, 317, 722-736 (2019) J. T. H. Kwan, A. Bonakdarpour, G. Afonso, and D. P. Wilkinson, Electrochim. Acta, 258, 208–219 (2017). P.J. Petzold, J.T.H. Kwan, A. Bonakdarpour, and D.P. Wilkinson, J. Electrochem. Soc., 168(2), 026507 (2021) Figure 1: The effect of different titanium current collector meshes on OER performance using a commercial IrO2-based CCM obtained by the MRDE tool. Inset shows the pictures of different Ti meshes examined. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".