Effect of Accelerated Stress Testing Conditions on Combined Chemical and Mechanical Membrane Durability in Fuel Cells
Bibliographic record
Abstract
Understanding membrane degradation induced by combined chemical and mechanical stresses is critical to designing durable polymer electrolyte membrane fuel cells. Accelerated stress tests (ASTs) are usually designed and carried out to study membrane degradation and identify stresses leading to it. In this work, a customized small-scale fuel cell fixture designed for in situ X-ray computed tomography (XCT) imaging is utilized to study the impact of different AST conditions on combined chemical and mechanical membrane durability. The XCT imaging technique allows the acquisition of a tomographic dataset yielding an integrated 3D image stack, which in turn, is used to analyze and compare global membrane degradation mechanisms. It was identified that cell temperature and relative humidity (RH) strongly influence the chemical membrane degradation rate, whereas the mechanical degradation rate was promoted by RH cycles with high amplitude and short period, which were dynamically diagnosed through a single frequency electrochemical impedance spectroscopy technique developed to track membrane hydration. When applied consecutively, the high chemical and mechanical stress intensities produced a joint chemo-mechanical failure mode with distinct evidence of chemical (thinning) and mechanical (fatigue-fracture) contributions in a relatively short time. The proposed AST is thus recommended for chemo-mechanical membrane durability evaluation in fuel cells.
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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) | 0.001 | 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".