Water Phenomena in PEFCs As the Origin of the Pt Loading Effect: A Comprehensive Modelling Study
Bibliographic record
Abstract
The highly prioritized objective of research on polymer electrolyte fuel cells is to make highly performing and stable catalyst layers with drastically reduced platinum loading. Achieving this objective demands an understanding of the impact of composition and porous structure of the electrode layers on the water balance in the cell. Experimental studies 1-3 have shown a marked increase in the resistance to oxygen diffusion when the Pt content of the cathode catalyst layer was lowered 4 . We present a water balance model to explain these trends. Figure 1 illustrates modeling domain and processes considered. The set of 1D continuity and flux equations is formulated and solved for water distribution and fluxes in catalyst layers, diffusion media and flow fields. Model solutions reveal the impact of structure, composition, and operating conditions on the water transport phenomena and performance. Reducing the Pt loading induces a drastic shift of the balance between rates of water production and vaporization. This leads to a build-up of the liquid water pressure in catalyst layer and gas diffusion layer on the cathode side that raises the liquid water accumulation and thereby drastically diminishes the effective oxygen diffusivity. Our model unravels the fine details of this interplay and it helps identify strategies for minimizing the Pt loading without running into this water trap References: [1] A. Kongkanand et al., ACS Catal., 6, 1578-1583, (2016) [2] J. Owejan et al. J. Electrochem. Soc. 160, F824-F833, (2013) [3] M. Wilson, J. Electrochem. Soc., 139. L28-L39, (1992) [4] T. Muzaffar et al., “Advanced Energy Materials”, in Review Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".