Characterization of Membrane Degradation Growth in Fuel Cells Using X-ray Computed Tomography
Bibliographic record
Abstract
Perfluorosulfonic acid ionomer membranes are subjected to simultaneous chemical and mechanical degradation under fuel cell operation. Despite the importance of membrane durability, the understanding of its structural degradation and failure modes has been considerably restricted by conventional 2D imaging. In this work, non-invasive micro X-ray computed tomography (XCT) is adopted to visualize the 3D membrane decay at different life stages during combined chemical and mechanical degradation. A detailed survey exhibits damage density of 6 and 10 cracks per mm 2 observed at the near-final and final end of life stages respectively. Through-thickness membrane cracks with unbranched I-shaped cracks and Y- and X- shaped cracks with one and two branches respectively are observed. The observed damage development at each life stage is correlated to supplementary diagnostic data including hydrogen leak rate, open circuit voltage, and tensile strength. In particular, large X-shaped cracks formed due to embrittlement from underlying chemical degradation are deemed to have a critical impact on the eventual failure development by facilitating large hydrogen leaks. Overall, the comprehensive 3D perspective enabled by XCT imparts new knowledge pertaining to the degradation process, and could also be extended to other fuel cell failure modes and degradation mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".