Study of Cathode Catalyst Layers for Anion Exchange Membrane Fuel Cells Using Fe-N-C Catalyst and a Novel Polymer Electrolyte
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 ionomer is soluble in lower alcohols such as 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 0.45 mg cm-2 platinum catalyst on the anode, respectively. Pt loaded on carbon black (Pt/CB, TEC10E50E), supplied from TKK Japan, was used as the platinum electrocatalyst. Figure 2 shows the results of current-voltage (I-V) measurements of the cell using the Fe-N-C catalyst under back pressures from 0 to 100 kPag (same pressure on both sides) at 60 oC, 100% RH; anode H2 (100 mL min-1); cathode O2 (100 mL min-1). The cell using the Fe-N-C catalyst showed hysteresis in the I-V curves between increasing and decreasing current. The degree of hysteresis decreased with increasing the back pressure. Figure 3 shows the results obtained when the back pressure of the anode was 100 kPag and the back pressure of the cathode was varied. The hysteresis decreased as the cathode back pressure increased. 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. By increasing the back pressure, liquid water would be generated more easily which is advantageous since cathodic reaction in AEMFCs requires water. We found that supplying liquid water to the cathode lead to decrease of the hysteresis when using the QPAF-4 polymer and Fe-N-C catalyst as the cathode. Acknowledgements 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. Reference Ono, T. Kimura, A. Takano, K. Asazawa, J. Miyake, J. Inukai, K. Miyatake, J. Mater. Chem. A, 5, 24804 (2017). 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 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) | 0.000 | 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".