Impact of Liquid Water on Reactant Mass Transfer in PEM Fuel Cell Electrodes
Bibliographic record
Abstract
The breakthrough conditions (capillary pressure and liquid water saturation) in a fibrous gas diffusion medium (GDM) used in polymer electrolyte membrane (PEM) fuel cell electrodes have been studied experimentally by two independent techniques and numerically by pore network modeling. Experiments show that treatment of the GDMs with a hydrophobic polymer coating reduces the water saturation at a breakthrough by 50%. Invasion percolation modeling is employed to simulate the breakthrough process and to determine mass-transfer rates through the partially saturated network. This model shows that the water saturation at breakthrough is drastically reduced when a microporous layer (MPL) is incorporated into the GDM, agreeing with experiments. However, the simulations yield limiting currents significantly higher than those observed in practice whether or not an MPL is present. Further calculations to include the contribution of condensation to water saturation within the GDM also result in unrealistically high limiting currents and suggest that mass-transfer resistance in the catalyst layer that is not included in the model plays an important role. If condensation is the principal mode for water accumulation within the GDM, simulations show that the MPL has only a small impact on liquid water distribution and does not improve performance, contrary to expectation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".