Porous Graphene Layers on Pt Catalyst for Long-Term Stability of Fuel Cell Electrode
Bibliographic record
Abstract
With the development of various energy technologies, much interest is focused on the feasibility and efficiency study of energy devices such as fuel cells, batteries, supercapacitors and water electrolysis systems. Among them, polymer electrolyte fuel cell (PEFC), which convert hydrogen into electric energy with zero emission of pollutants, is one of the most promising environmentally friendly technologies. Despite innumerable studies for more than half a century, degradation of the key component, membrane electrode assembly (MEA), is still a big obstacle for the commercialization of PEFC system. For the high performance and long-term stability of Pt/C electrode catalyst, the agglomeration of Pt particles and dissolution or detachment of Pt particles from carbon support have to be improved. For this purpose, we adopted a new shape of nano-carbon material for the surface modification of Pt catalyst. Graphene has been an attractive two-dimensional carbon allotrope having large surface area and electronic conductivity. Graphene also shows high flexibility and mechanical strength so that it can be used for large number of applications such as flexible display or printable electronics, etc [1-2]. In most cases, people need large-area graphene films with little or no defect for high thermal and electronic conductivity. Also, people need to transfer the as-prepared graphene film for each application. All of these processes are not easy or simple, and they are also labor-intensive processes. However, in the case of catalysis, porous graphene films can be synthesized via very simple one-step process and can be used as effective protective layers for Pt catalysts. In this study, we developed porous graphene films in order to improve the long-term stability of Pt catalysts maintaining the high performance of them. The graphene films were synthesized by single-step vaporization process, where the number of graphene layers and the defects in their structure are manipulated by temperature and composition of the precursors. In this process, the amounts of structural defects, pyridine was simultaneously introduced to the vaporization process, which is much easier and cost-effective compared to the conventional NH3-treatment at high temp [3]. Consequently, our Pt/C catalysts coated with porous graphene films showed similar initial activity compared with the commercial catalysts (Pt 40wt%, Johnson Matthey) showing more than 150% higher long-term stability [4]. [1] A.K. Geim and K. S. Novoselov, Nature Mater. 6 (2007) 183. [2] X. L. Li, G. Y. Zhang, X. D. Bai, X. M. Sun, X. R. Wang, E. Wang and H. J. Dai, Nature Nanotech. 3 (2008) 538. [3] Y. Wang, Y. Shao, D. W. Matson, J. Li and Y. Lin, ACS nano 4(4) (2010) 1790. [4] H. Kim, A. Robertson, S. O. Kim, J. M. Kim and J. H. Warner, ACS nano 9(6) (2015) 5947.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".