Decay in Mechanical Properties of Catalyst Coated Membranes Subjected to Combined Chemical and Mechanical Membrane Degradation
Bibliographic record
Abstract
Abstract The mechanical stability of catalyst coated membranes (CCMs) is an important factor for the overall durability and lifetime of polymer electrolyte fuel cells. In this article, the evolution of the mechanical properties of degraded CCMs is comprehensively assessed. A combined chemical and mechanical accelerated stress test (AST) was applied to simulate field operation and rapidly generate partially degraded CCM samples for tensile and expansion experiments under both room and fuel cell conditions. The tensile results indicated significant reductions in ultimate tensile strength, toughness, and fracture strain as a function of AST cycles, accompanied by a mild increase in elastic modulus. The increased brittleness and reduced fracture toughness of the CCM, caused primarily by chemical membrane degradation, is expected to play an important role in the ultimate failure of the fuel cell. The expansion tests revealed a linear decay in hygrothermal expansion, similar in magnitude to the loss of mechanical strength. The decline in CCM sensitivity to environmental changes leads to non‐uniform swelling and contraction that may exacerbate local degradation. Interestingly, the hygrothermal expansion in the late stages of degradation coincided with the fracture strain, which correlates to in situ development of fractures in chemically weakened membranes.
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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) | 0.002 | 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".