(Invited) Effect of Water Management for Cathode Catalyst Layers Using a Non-Noble Metal Catalyst and a Novel Polymer Electrolyte on Cell Performance Hysteresis in Anion Exchange Membrane Fuel Cells
Bibliographic record
Abstract
In this work, we examined the cell performance of membrane-electrode assemblies (MEAs), for anion exchange membrane fuel cells (AEMFCs), using a non-noble metal catalyst and a novel polymer electrolyte. The chemical structure of quaternized poly(arylene perfluoroalkylene), i.e., QPAF-4 (ion exchange capacity: IEC = 2.1 meq g-1) developed in this laboratory1 is shown in Figure 1. Since this membrane is soluble in methanol, it is also suitable for use as an electrode binder. The non-precious metal catalyst cell was loaded with 0.45 mg cm-2 Fe-N-C catalyst supplied by Pajarito Powder on the cathode and 0.20 mgPt cm-2 platinum catalyst on the anode. An MEA in which platinum was loaded (0.20 mgPt cm-2) on both the anode and cathode was prepared for comparison. Pt loaded on carbon black (Pt/CB, TEC10E50E), supplied from TKK Japan, was used as the platinum electrocatalyst. Figure 2 shows a comparison of Fe-N-C and Pt/CB for their I-V performances at 60 oC, 100% RH, 0 kPag; anode H2 (100 mL/min); cathode O2 (100 mL/min). The cell using the Fe-N-C catalyst exhibited large hysteresis in the I-V curve, i.e., a large difference in potential between increasing and decreasing current. Figure 3 shows a comparison of Fe-N-C and Pt/CB for their I-V performances at 60 oC, 100% RH, 100 kPag; anode H2 (100 mL/min); cathode O2 (100 mL/min). The hysteresis of the I-V performance for the cell using the Fe-N-C cathode decreased with increasing back pressure. The cell exhibited slightly lower open circuit voltage and similar IV performance compared with those using Pt/CB. At the present stage, the loading amount of Fe-N-C catalyst, ionomer content, and porosity of the catalyst layer have not yet been optimized. We investigated whether the hysteresis originates from the anode or cathode. Based on the results of various I-V measurements, we conclude that the hysteresis is related to water supplied to the cathode using the Fe-N-C catalyst. Further details will be given in this presentation. We found from these results that the water management is essential, due to its requirement for the cathode reaction, for high-performance AEMFCs. Finally, these results demonstrate the viability of the use of low-cost materials such as non-noble metal catalysts for the AEMFC. Acknowledgement This project was partly supported by NEDO Japan through funds for the “Advanced Research Program for Energy and Environmental Technologies” and the Japan Society for the Promotion of Science (JSPS) and the Swiss National Science Foundation (SNSF) under the Joint Research Projects (JRPs) program. References 1. H. Ono, T. Kimura, A. Takano, K. Asazawa, J. Miyake, J. Inukai, K. Miyatake, J. Mater. Chem. A, 5, 24804 (2017). 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".