Highly Efficient Low-Pt-Based Electrocatalysts with Pt Single-Atom Active Sites for Oxygen Reduction Reaction
Bibliographic record
Abstract
Platinum (Pt) has been preferred as the most viable catalytic material to accelerate the sluggish oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs) 1,2 . However, the scarcity of Pt on earth makes it expensive, resulting in a concomitantly high cost for the commercial implementation of fuel cells. In this respect, significant efforts have been made worldwide to reduce Pt loading while retaining and even improving their high catalytic performance of Pt-based ORR electrocatalysts 3,4 . One of the effective approaches to improve the utilization efficiency of Pt atoms is to reduce the size of Pt nanoparticles to clusters or even to single atoms. Smaller Pt particles have higher fraction of low-coordinated surface Pt atoms which can be directly involved in the ORR process 5-7 . Herein, based on nitrogen-doped active carbon (Black Pearls 2000, denoted as NBP), a carbon-supported highly dispersed Pt nanocatalyst (Pt 1 @Pt/NBP) with low Pt content of 4.96 wt.% was prepared simply using a mild hydrothermal method. Results of electrochemical measurement and physical characterization reveal that, a large number of atomically dispersed Pt sites (Fig.1a) can remarkably enhance the ORR activity of Pt 1 @Pt/NBP electrocatalysts with a higher half-wave potential ( E 1/2 = 0.827 V) than that of Pt/BP (Fig.1b, E 1/2 = 0.791 V) , commercial Pt/C (Fig.1c, E 1/2 = 0.811 V) and the previously reported Pt 1 -N /BP catalysts 6 . This novel low-Pt electrocatalysts can be one of the promising alternatives to traditional Pt-based catalysts for the application in PEMFCs with its exceedingly improved Pt utilization and superhigh performance. Acknowledgements Work was funded by the national research foundation of Korea Grant, Korean government (MSIT) (NRF-2019M3D1A1079297) References (1)Steele, B. C. H.; Heinzel, A. Nature 2001, 414, 345. (2)Wang, Y.-J.; Zhao, N.; Fang, B.; Li, H.; Bi, X. T.; Wang, H. Chemical Reviews 2015, 115, 3433. (3)Stamenkovic, V. R.; Fowler, B.; Mun, B. S.; Wang, G.; Ross, P. N.; Lucas, C. A.; Markovic, N. M. Science 2007, 315, 493. (4)Huang, X.; Zhao, Z.; Cao, L.; Chen, Y.; Zhu, E.; Lin, Z.; Li, M.; Yan, A.; Zettl, A.; Wang, Y. M.; Duan, X.; Mueller, T.; Huang, Y. Science 2015, 348, 1230. (5)Cheng, H.; Cao, Z.; Chen, Z.; Zhao, M.; Xie, M.; Lyu, Z.; Zhu, Z.; Chi, M.; Xia, Y. Nano Letters 2019, 19, 4997. (6)Liu, J.; Jiao, M.; Lu, L.; Barkholtz, H. M.; Li, Y.; Wang, Y.; Jiang, L.; Wu, Z.; Liu, D.-j.; Zhuang, L.; Ma, C.; Zeng, J.; Zhang, B.; Su, D.; Song, P.; Xing, W.; Xu, W.; Wang, Y.; Jiang, Z.; Sun, G. nature commonications 2017, 8, 15938. (7)Zhang, H.; An, P.; Zhou, W.; Guan, B. Y.; Zhang, P.; Dong, J.; Lou, X. W. Science Advances 2018, 4, eaao6657. 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".