Degradation and Deformation Patterns in Reinforced Fuel Cell Membranes Subjected to Chemo-Mechanical Stress Testing
Bibliographic record
Abstract
Thin, reinforced membranes have become the norm for modern fuel cell technology due to favorable performance and durability, yet fuel cell durability studies involving such membranes are relatively scarce in the literature. The present work aims to thoroughly analyze the progressive degradation and deformation patterns of a reinforced membrane subjected to a combined chemo-mechanical accelerated stress test (AST) within a small-scale fuel cell. To achieve this, an in-house designed small-scale, X-ray transparent fuel cell is subjected to a chemo-mechanical AST and the morphology of its components is monitored in 4D using X-ray computed tomography. The findings demonstrate detectable patterns in membrane degradation and deformation that are influenced by factors such as the location of channels and distance from inlet. Additionally, it is shown how membrane creep under the lands increases the risk of failure. Compared to non-reinforced membranes, this work demonstrates that reinforced membranes can achieve very high long-term durability under intensified chemical and mechanical stress, provided that adjacent electrode components are smooth and free from irregular features and assembled in a way that the membrane has adequate mechanical support. The present results can serve as a reference to determine whether any non-uniform feature may alter the degradation.
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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".