Modeling the Effect of Low Pt Loading Cathode Catalyst Layer in Polymer Electrolyte Fuel Cells. Part II: Parametric Analysis
Bibliographic record
Abstract
A parametric analysis is presented using a previously validated 1D model for a cathode catalyst layer (CL). The results show that maximum power density at low Pt loading can be maximized with relatively thin CLs (thickness ∼ 2 μ m) featuring a high carbon volume fraction (low ionomer-to-carbon weight ratio, I/C) compared to high Pt loading CLs. The shift of the optimal carbon volume fraction (I/C ratio) is caused by the dominant role of the local oxygen transport resistance at low Pt loading, which is lowered by a reduction of the average ionomer film thickness (better ionomer distribution among carbon particles). In contrast, at high Pt loading, higher porosity and pore radius (lower carbon volume fraction) is beneficial due to an increase of bulk effective diffusivity despite thickening of ionomer films. Moreover, the results show that performance at low Pt loading is significantly improved with increasing mass-specific activity. The effect of average saturation and ionomer permeability on performance at low Pt loading is lower compared to dry CL composition and mass-specific activity.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".