Improved Decal Transfer Method to Reduce Membrane Damage from Foreign Particles in Membrane Electrode Assembly
Bibliographic record
Abstract
Foreign particles unintentionally embedded in the membrane electrolyte assembly may be detrimental to polymer electrolyte fuel cell durability by dissolution of contaminants or puncture of the membrane. The presence of incidental particles may also affect the fuel cell production cost by imposing more stringent and costly quality control equipment and cleanroom facilities to the manufacturers. The present work aims to understand the impact of foreign particles deposited at the membrane—catalyst layer interface on the decal transfer process and the quality of the resulting catalyst coated membrane. Additionally, this work explores process related opportunities to mitigate material damage from said particles. Several samples are fabricated by specifically placing representative silica particles on the membrane surface subsequently laminated with catalyst layer using different decal transfer procedures. Non-destructive 3D X-ray computed tomography reveals that the model particles substantially penetrate the membrane during regular decal transfer conditions, leading to a vulnerable membrane state or even complete puncture. However, a tuned decal transfer method with modified pressure application rate and optimized supporting layers is shown to reduce membrane damage up to 69%. Additionally, finite element modeling shows that the tuned method can reduce membrane stress during fuel cell operation and thus benefit durability.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".