Assessment of Cathode Catalyst Characteristics to Enhance High Current Density Performance in PEMFCs
Bibliographic record
Abstract
At AFCC continuous effort has been made to improve the power density of fuel cell stack for automotive application in the past decade*. The unique challenges in durability posed by the need for much higher power generation per catalyst loading, kW/mg-Pt is still a major driver and a minimum viable stack functionality for mass production. This presentation will focus on one of the ways to meet the above criterion using mature Pt/C technology (10 to 50 wt% Pt on a high surface area carbon) for oxygen reduction in the cathode. The catalysts were custom made by Tanaka Kikinzoku Kogyo K.K to maximize and stabilize the Pt nanoparticles activity during their life cycle. The graph shows the linear dependency of a mix of the above catalysts characteristics such as Pt surface area (CO Chemisorption), Pt crystallite size (XRD), and catalyst surface area (N2 BET) as a function of Pt wt% on Carbon. With more in-depth analyses, preliminary optimization of catalyst layer for each of the above catalysts will be performed and the results will be discussed. These activities would facilitate the understanding of the effect of catalyst characteristics, catalyst structure (ionomer, catalyst layer thickness, EPSA, etc.), and electrode Pt loading (0.25 and 0.15 mg-Pt/cm2) on the high current density performance up to 3 A/cm2. Identifying the above parameters would leverage further development in maximizing the fuel cell functionalities and improving the stack durability. *Reference:AFCC accomplishments, http://www.afcc-auto.com/company/about-us/ 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".