Model-Driven Membrane Electrode Assembly Design for High-Performing Open-Cathode Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
Open-cathode fuel cells use air cooling to effectively reduce system cost. However, due to the challenging hygrothermal environment, they generally suffer from low performance compared to conventional, liquid-cooled cells. A pre-validated, three-dimensional computational model is used in the present work to determine the effects of different sub-component designs, namely the polymeric membrane, composition of the cathode catalyst layer (CCL), and structure of the cathode microporous layer (CMPL), on the performance of an open-cathode fuel cell. This comprehensive parametric study performed on a total of 90 cases shows the increment in current density to be 7% and 31% by improvising the membrane and CCL design, respectively, at 0.6 V. A steep increase of 87% is also achieved by strategically modifying the CMPL design at 0.4 V operation. An overall increment of 119% and 131% in current density is achieved for the best membrane electrode assembly (MEA) design at 0.6 and 0.4 V, respectively, as compared to the baseline design. These improvements are achieved by collective improvements in kinetics, oxygen mass transport, ohmic resistance, self-heating, and water retention in the ionomer phase. The proposed MEA design could facilitate open-cathode fuel cell stacks with 2× higher power output or 56% lower weight and materials cost for a given power demand.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".