Combined Nitrogen Precursor Approach to Develop Cobalt-Based Non-Precious Catalysts for Polymer Electrolyte Fuel Cell Cathodes
Bibliographic record
Abstract
The cost of the platinum-based catalysts used in polymer electrolyte membrane fuel cells (PEFCs) is estimated to be almost half of the overall fuel cell stack cost under projected conditions of fuel cell vehicle mass production [1]. To reduce this economic constraint, the development of non-platinum group metal (non-PGM) cathode catalysts with high oxygen reduction reaction (ORR) activity is highly desirable. Our group and others have demonstrated immense progress in non-PGM catalyst development, with the specific class of pyrolyzed transition metal-nitrogen-carbon (M-N-C) catalysts achieving high activity [2,3]. In order to realize the successful implementation of these non-PGM catalysts into PEFC systems, the significant issue of operational durability must be addressed. The two most promising M-N-C catalyst technologies are based on either iron or cobalt, with the former conventionally providing enhanced ORR performance, approaching that of commercial platinum catalysts. From a system perspective, however, the presence of iron species in the catalyst layer is highly undesirable. This is owing to the well-known Fenton chemistry, resulting in the formation of highly reactive free-radical species that in turn leads to membrane and/or ionomer decomposition. To address this challenge, we prepared cobalt-based catalysts using the combined-precursor approach recently developed at Los Alamos National Laboratory [4]. This approach utilizes both polyacrylonitrile (PANI) and cyanamide (CM) as nitrogen/carbon precursors that are combined and pyrolyzed with a carbon support and cobalt salt. Individually, both PANI and CM precursors have been previously demonstrated capable of forming highly ORR-active catalysts. Through this approach, we found that the relatively low decomposition temperature ( ca. 260°C) of CM allows it to synergistically act as a pore-forming agent and yield high surface-area catalysts. This in turn leads to excellent H 2 -air fuel cell performance due to the reduced mass transport limitations in the cathode. In this work, Co-CM-PANI-C was formed and found to provide promising activity towards the ORR in acidic electrolyte. Particularly, it was determined that 8.0 wt.% cobalt in the precursor mixture provided optimal ORR activity through half-cell investigation ( Figure 1 ). A half-wave potential of ca. 0.74 V vs. RHE was achieved, with current investigations underway to understand and optimize the structure-performance-durability relationships. These results, along with performance and durability data from both half-cell and MEA evaluation, will be presented at the meeting. Acknowledgements Financial support for this was has been provided by the DOE-EERE through the Fuel Cells Technologies Office and by the Natural Sciences and Engineering Research Council of Canada (NSERC). References [1] B. James, J. Motton, W. Colella. Mass Production Cost Estimation of Direct H 2 PEM Fuel Cell Systems for Transportation Applications: 2013 Update . Strategic Analysis (2014) available online at: https://www1.eere.energy.gov/hydrogenandfuelcells/pdfs/sa_fc_system_cost_analysis_2012.pdf [2] F. Jaouen, E. Proietti, M. Lefevre, R. Chenitz, J. Dodelet, G. Wu, H. Chung, C. Johnston, P. Zelenay, Recent advances in non-precious metal catalysis for oxygen-reduction reaction in polymer electrolyte fuel cells. Energy & Environmental Science (2011) 4, 114-130. [3] Z. Chen, D. Higgins, A. Yu, L. Zhang, J. Zhang. A review on non-precious metal electrocatalysts for PEM fuel cells. Energy & Environmental Science (2011) 4, 3167-3192. [4] P. Zelenay. Non-Precious Metal Fuel Cell Cathodes: Catalyst Development and Electrode Structure Design. 2014 Hydrogen and Fuel Cells Program Annual Merit Review and Peer Evaluation Meeting (2014) available online at: http://www.hydrogen.energy.gov/pdfs/review14/fc107_zelenay_2014_o.pdf Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".