Four-Dimensional Identical-Location X-ray Imaging of Fuel Cell Degradation during Start-Up/Shut-Down Cycling
Bibliographic record
Abstract
In this work, local electrode degradation effects from start-up/shut-down cycling of polymer electrolyte fuel cells are visualized using X-ray tomographic imaging of specialized, miniature fuel cell hardware. This combination enables non-invasive in situ tracking of the same cathode catalyst layer domain throughout various degradation stages in four dimensions. Critical, localized regions are identified within the cathode catalyst layer where progressive structural deterioration occurs from carbon support corrosion leading to thinning and collapse of the material. A greater structural change is observed under the landing area than under the channels due to delayed resident gas purge. This finding differs from the results of voltage cycling accelerated stress test, where more structural change was observed under the channel area than the landing area. However, overall similarities in degradation and performance loss supports the use of voltage cycling for accelerated degradation studies. A direct correlation between the structural deterioration and the electrochemical performance reduction of the fuel cell is found. In addition, reduced reactant gas flow in a restricted anode flow channel enhances the local cathode degradation due to delayed gas purge. However, no influence on the degradation is observed in a cell with intentional anode/cathode channel misalignment, compared to generic test 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".