Fuel Cell Membrane Durability Implications of Locally Absent Microporous Layer
Bibliographic record
Abstract
Although modern reinforced fuel cell membranes are designed for high durability, membrane degradation can incidentally be accelerated by its interaction with manufacturing related physical non-uniformities in other fuel cell subcomponents. It is therefore important to identify and understand the implications of these non-uniformities in order to improve quality control for fuel cell manufacturing. The present work investigates the chemo-mechanical membrane durability impacts of discrete sites of missing microporous layer (MPL) with controlled dimensions and locations as the target non-uniformity. Accelerated stress testing is utilized to vividly simulate stresses, with advanced failure monitoring via in situ electrochemical diagnostics and four-dimensional X-ray computed tomography. Progressive local membrane deformation and thinning driven by exposed protruding carbon fibers underneath the missing MPL is discovered to cause critical electrode shorting failure. This exclusively occurs under flow plate lands, and is more severe with increasing missing MPL dimensions. Catalyst layer cracks are formed at missing MPL sites under flow channels, but with marginal impact on membrane durability. The missing MPL sites also have no evident impact on chemical membrane degradation. In conclusion, missing MPL is deemed as a potential risk to long-term membrane durability due to mechanical stress concentration, and should be avoided during fabrication.
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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 | 0.000 |
| 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".