The Effect of Ionomer Thin Films in Ionomer- and Water-Filled Agglomerate Models
Bibliographic record
Abstract
The catalyst layer of a polymer electrolyte fuel cell is commonly represented in mathematical models as an agglomerate structure of carbon catalyst-support particles. There are two prevailing assumptions for the structure of the agglomerates. The first is that the pores are filled with perfluorosulfonated-ionomer (PFSI). The second is that the pores are hydrophilic and are flooded only with liquid water during operation. The objective of this work is to develop numerical models for single water-filled and ionomer-filled agglomerates in a cathode catalyst layer of a polymer electrolyte membrane fuel cell (PEMFC), and investigate the properties of oxygen transport, proton transport, and reaction kinetics. The two models provide different solutions for the distribution of oxygen and protons, and produce a different reaction profile within the agglomerate. Previous numerical water-filled ionomer models in the literature have neglected the effect of the ionomer thin film. Therefore, the results obtained for both ionomer and water-filled models could not be easily compared. In this article, the equations developed relate the assumed structure of the agglomerates to the structure of the catalyst layer (CL). Results compare the effect of the thin film thickness in the two different types of agglomerates and relate the phenomena occurring within the agglomerates to overall catalyst layer performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".