Quality Implications of Foreign Metallic Particles in the Membrane Electrode Assembly of a Fuel Cell
Bibliographic record
Abstract
Foreign metallic particles unintentionally trapped within the membrane electrode assembly (MEA) may adversely affect quality and yield of high-volume fuel cell production, for instance by damaging the membrane or releasing metallic cation contaminants. The present work aims to understand the impacts of 55 ± 5 μm Fe and SS316L metallic particles present at the membrane - cathode catalyst layer (CCL) interface during fuel cell fabrication, conditioning, and diagnostics. In-situ X-ray computed tomography imaging of particle-laden MEAs within a customized small-scale fuel cell fixture reveals that Fe particles undergo complete dissolution within the first air starve cycle of the conditioning phase. After dissolution, legacy particles are observed to incur considerable damage within the MEA, including void spaces at the membrane-CCL interface, membrane thinning, CCL cracks, and membrane rupture. In stark contrast, the SS316L particles feature negligible dissolution during fuel cell conditioning and diagnostics and remain largely intact, merely causing membrane-CCL delamination in their vicinity. Post-operation chemical analysis by laser ablation inductively coupled plasma mass spectrometry indicates Fe ion concentrations in the range of 800–950 ppm and 10–25 ppm for the Fe and SS316L laden MEAs, respectively, which correlates to visual observations of particle dissolution and slight reductions in fuel cell performance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".