Investigation of Dynamic Water Cluster and Droplet Interactions in Polymer Electrolyte Fuel Cells using Operando X-ray Tomographic Microscopy
Bibliographic record
Abstract
Efficient removal of the electrochemically produced water from the gas diffusion layer (GDL) in polymer electrolyte fuel cells is crucial for reducing mass transport losses and improving the efficiency at high current densities. Understanding the relationship between the water percolation through the GDL and droplet formation in the gas channel will allow the design of advanced GDL materials, which provide optimal water management. In this study, a catalyst-coated membrane with 8 individual active areas (0.06 mm2 each) is investigated using operando X-ray tomographic microscopy to study the transient development and interaction of multiple percolating water clusters in a GDL and droplet formation in the channel. The 4D imaging results at a time resolution of 1 Hz showed transient instabilities in the developed percolating water networks at various frequencies associated with break-through and spontaneous water drainage.
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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".