Local Mass Transport Resistance of Low-Loaded PEM Fuel-Cell Catalyst-Layers
Bibliographic record
Abstract
To this day, the content of noble metals in the catalyst layers of proton exchange membrane fuel cells (PEMFCs) is still accounting for up to 60 % of the total stack cost and is therefore needed to be reduced for large-scale applications as automobile industry. Though a lot of effort was put into fabrication of catalyst-layers (CLs) with sufficiently low platinum loading, the performance below a certain content starts to decay rapidly. Within the CL, a complex interplay of mass and charge transport takes place, making the identification and quantification of the limiting parameters especially difficult and laborious. Finally, this resistance was found to be due to a local mass transport resistance scaling inversely with the electrochemical active surface area (ECSA). In this presentation, the results for the local transport resistance measured in-situ via our unique hydrogen limiting current setup for CLs of varying loading, ionomer content and fabrication are shown. The effects of temperature and relative humidity on this intrinsic property are discussed, allowing a deeper understanding of the processes occurring close to the catalyst surface. Though water production, oxide effects and possible peroxide formation can make oxygen limiting currents hard to quantify in detail, data is included, allowing a direct comparison of the transport properties and pathways of both reactants. The data provided shows the potential of this measurement technic to identify the transport limitations in a fast and easy way, which helps to optimize the electrode structure, composition and fabrication for several operation-conditions and therefore applications. Acknowledgements This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Fuel Cell Technologies Program of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and by CRADA agreement LB08003874 between Lawrence Berkeley National Laboratory and Toyota Motor Company. 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".