Effectiveness factor of Pt utilization in cathode catalyst layer of polymer electrolyte fuel cells
Bibliographic record
Abstract
In this work, we analyze effectiveness factors of Pt utilization in perfluorosulfonate ionomer (PFSI) bonded thin film cathode catalyst layers of polymer electrolyte fuel cells. We define the effectiveness factor of Pt utilization as the apparent rate of current conversion exhibited by a specific catalyst layer design divided by the ideal rate obtained if all Pt atoms were used equally in electrochemical reactions at the specified electrode overpotential and externally provided reactant concentrations. This definition includes statistical factors at all relevant scales as well as non-uniformities of reaction rate distributions under operation. Our model is based on the random composite agglomerated morphology of the catalyst layer. It accounts for the interplay of transport phenomena and electrochemical kinetics. At the mesoscopic scale, limited effectiveness of Pt utilization in agglomerates is mainly an electrostatic effect. We determined spatial distributions of effectiveness factors of agglomerates in the through-plane direction, and thereafter calculated overall effectiveness factors of the cathode catalyst layer. Our results show that small agglomerate radius, low operating current density, high operating temperature, and high oxygen partial pressure result in high effectiveness factors of Pt utilization. Finally, we compared PFSI-bonded thin film cathode catalyst layers with ultrathin two-phase cathode catalyst layers in terms of effectiveness factors. Including the surface to volume atom ratio of Pt nanoparticles, the two different types of structures exhibit similar effectiveness factors of Pt utilization, which are found to be distinctly below 10%.Key words: polymer electrolyte fuel cells, fuel cell modeling, cathode catalyst layer, Pt utilization, effectiveness factor.
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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 | 0.000 |
| 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 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".