The Control and Effect of Pore Size Distribution in AEMFC Catalyst Layers
Bibliographic record
Abstract
Catalyst ink for anion-exchange catalyst coated membranes based on FuMA-Tech FAA-3 membranes and ionomer typically requires high-boiling solvents. Here, we investigate the disproportionate effect of even small quantities of high-boiling solvent in the catalyst ink on the catalyst layer microstructure. High porosity in the mesoporous regime, 20–100 nm, is found to be an essential characteristic of effective anion-exchange catalyst layers for increasing membrane hydroxide ion conductivity and reducing mass-transport losses. High porosity in the nanoporous regime (<20 nm pore diameter) facilitates improvements in the kinetic region of polarization curves at the expense of mass-transport losses. New strategies are introduced to improve the control of distribution of pore sizes in the catalyst layer and to increase the mesoporosity. Beginning-of-life power densities for O 2 /H 2 anion exchange membrane fuel cells (AEMFCs), under zero backpressure, were accordingly increased from 276 to 428 mW·cm −2 , placing it among the highest AEMFC power densities reported in the literature under the conditions studied, representing a significant improvement over previously reported performances for FAA-3. The study highlights a need to develop anion-exchange solid polymer ionomers soluble in low-boiling solvents for preparing catalyst inks, and a more rigorous evaluation of porosimetry data and catalyst layer preparation methods for AEMFCs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".