Mitigation of Mechanical Membrane Degradation in Fuel Cells by Controlling Electrode Morphology: A 4D In Situ Structural Characterization
Bibliographic record
Abstract
Mechanical degradation is a critical mechanism responsible for the operational failure of fuel cell membranes. In addition to the membrane’s intrinsic durability, component interactions play a crucial role in this degradation process. This work investigates the interaction and associated impact of electrode morphology on membrane failure under pure mechanical degradation conditions by utilizing 4D in situ visualization by X-ray computed tomography. Using periodic identical-location imaging, membrane damage progression is monitored and compared for electrodes with high and low initial crack density. Membrane fracture is found to be significantly curtailed through minimization of ab initio crack density in the cathode catalyst layer. Hydration-dehydration cycles, however, still introduce early electrode cracking which, as an intermediate step, exclusively governs the subsequent initiation and propagation of membrane cracks. Two distinct membrane failure mechanisms are identified that are characterized by: (i) permanent buckling deformation of the catalyst coated membrane; and (ii) direct membrane fracture from electrode cracks without buckling. The buckling phenomenon is found to be strongly influenced by the microstructure of the gas diffusion media and has a dominant contribution towards the overall frequency and scale of membrane fracture. Additionally, the effect of hydration on the in situ size and geometry of fracture features is demonstrated.
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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".