Dynamic Percolation and Droplet Growth Behavior in Porous Electrodes of Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
The percolating flow of liquid water in the gas-diffusion layer (GDL) of polymer electrolyte fuel cells (PEFCs) was studied ex situ using a simple water-injection experiment. Water was injected into the top of the sample so the droplet was free to detach from the bottom, allowing for uninterrupted study of the dynamic cycle of droplet appearance, growth, and detachment. Although droplets emerged from a single point on the GDL, the measured pressure response in the water phase was clearly not equivalent to a single needle. The behavior of the system was explained by the simultaneous filling and inflating of many menisci, resulting in extended periods with no droplet activity at the GDL surface, followed by the sudden eruption of a droplet at the breakthrough site as all interfaces deflated and their stored water was directed toward the droplet. A simple numerical model was presented that could qualitatively explain the observed behavior. Tests were performed on GDLs, with and without microporous layers (MPLs), and all observed behavior could be interpreted in terms of the proposed model. MPLs shifted the behavior to a more needlelike behavior which was consistent with the MPL reducing the number of invading liquid clusters in the system.
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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 | 0.001 |
| 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".