Effects of Hardware Design and Operation Conditions on PEM Fuel Cell Water Flooding
Bibliographic record
Abstract
In this paper, membrane electrode assemblies were constructed using catalyst-coated membranes to investigate proton-exchange membrane fuel cell water flooding. Two major fuel cell hardware variations, namely flowfield design and Teflon loading of the gas diffusion layer (GDL), were tested to explore their effects on water flooding. A flowfield with triple serpentine flow channels showed heavier water flooding than that with single serpentine flow channels. Increasing the Teflon loading in the GDL reduced water flooding effectively. Several fuel cell operating conditions, including air stoichiometry, current density, relative humidity (RH), backpressure, and temperature, were also tested to identify their effects on water flooding. It was observed that the water flooding severity increased with decreasing air stoichiometry, as well as with increasing temperature, RH, backpressure, and current density. Among these operation conditions, air stoichiometry (or air flow rate) and RH played more important roles in reducing water flooding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".