Effect of Cobalt Cation Concentration on PEMFC Electrode Performance
Bibliographic record
Abstract
Metal alloy catalysts, such as Pt-Co, reduce the activation energy of oxygen reduction reaction, leading to improved proton exchange membrane fuel cell (PEMFC) performance. However, leaching of non-noble elements contaminates the ionomer and membrane, which has a negative impact on the durability of PEMFCs [1,2]. For the commercial success of metal alloy catalysts, understanding the mechanisms of how cation contamination affects PEMFC performance is crucial. Here, we investigate the effect of cobalt cation contamination effects through intentional doping of decal electrodes. Electrochemical testing results are coupled with membrane conductivity and water uptake measurements, as well as impedance modeling to identify the mechanisms of performance loss. Our results provide a comprehensive understanding of how cation contamination affects performance, which can inform mitigation strategies and new materials development that can enable the use of metal alloy catalysts in PEMFCs. Acknowledgement This work was supported by the Hydrogen and Fuel Cell Technologies Office (HFTO), Office of Energy Efficiency and Renewable Energy, US Department of Energy (DOE) through the Million Mile Fuel Cell Truck (M2FCT) consortia, technology managers G. Kleen and D. Papageorgopoulos. Financial support for this work from the Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory (LANL) is gratefully acknowledged (Projects 2020200DR and 20210915PRD2). ChungHyuk Lee acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC). References Cai et al., ECS Trans., 69, 1047 (2015) P. Braaten et al., J. Electrochem. Soc., 166, F1337 (2019) 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 | 0.002 |
| 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 | 0.001 | 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".