Morphological Characteristics of Catalyst Layer Defects in Catalyst-Coated Membranes in PEM Fuel Cells
Bibliographic record
Abstract
Catalyst layer defects and irregularities in catalyst-coated membrane (CCM) electrodes affect the lifetime of polymer electrolyte membrane fuel cells (PEMFCs) during their operation. Thus, catalyst layer defects are important concerns for fuel cell manufacturers and prompt the development of quality control systems with the aim of fabricating defect-free electrodes. Consequently, the objective of this study is to gain a fundamental understanding of the morphological changes of real catalyst layer defects that have developed during CCM production. In this paper, missing catalyst layer defects (MCLD) formed during the decal transfer process are investigated through a nondestructive method using reflected light microscopy. The geometric features of the defects are quantified, and their growth is measured at regular time intervals from beginning-of-life (BOL) to end-of-life (EOL) until the OCV has dropped by 20% of its initial value as per a DOE-designed protocol. Overall, two types of degradation are observed: surface degradation caused by catalyst erosion and crack degradation caused by membrane mechanical deformation. Furthermore, catalyst layer defects formed during the decal transfer process were found to exhibit a higher growth rate at middle-of-life (MOL-1) and stabilize by EOL. This type of study will provide manufacturers with baseline information to allow them to select and reject CCMs, ultimately increasing the lifetime of fuel cell stacks.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".