Progression in the Morphology of Fuel Cell Membranes upon Conjoint Chemical and Mechanical Degradation
Bibliographic record
Abstract
Ionomer membranes used to separate the electrodes in polymer electrolyte fuel cells are known to degrade both chemically and mechanically during regular fuel cell operation and may ultimately result in lifetime-limiting failure. The objective of the present work is to understand the effects of combined chemical and mechanical stresses on the mesoscale morphology of the membrane and its role in the overall degradation process. The mesoscale effects of sulfonic acid group loss and fluoride release on the phase segregated morphology of the membrane are analyzed using contrast-enhanced transmission electron microscopy and energy dispersive X-ray spectroscopy. The end-of-life ionic domain size of the ionomer is shown to be substantially enlarged compared to the pristine membrane state. Elemental mapping overlayed with the binary ionic and non-ionic morphology reveals mesoscopic void regions in the degraded material that are depleted of ionomer fluorine and carbon and considered susceptible to micro-crack initiation. A larger, severely degraded void region is also identified which contains evidence of hygrothermal stress induced localized ionomer crazing as a potential nucleation site for macroscopic fracture development. The synergetic effects of chemical and mechanical degradation on the progressive changes in the observed mesoscale morphology are discussed.
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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".