Effect of Carbon Support and Synthesis Method on Pt NP Utilization and Activity
Bibliographic record
Abstract
A key requirement for the large-scale commercialization of proton exchange membrane (PEM) fuel cells is to produce highly active, durable catalysts, while minimizing the use of precious noble metals, especially platinum (Pt). Higher utilization of Pt catalyst can be realized by understanding the effect of the carbon support microstructure on the distribution and formation of Pt nanoparticles (NPs) and by optimizing the synthesis method. In this work, Pt NPs were fabricated using a simple alcohol reduction method and then varying the carbon support pore structure and surface properties. The catalysts were then tested for their activity in 1 M C2H5OH + 0.5 M H2SO4 solutions. For Pt NP preparation, either ethanol or butanol were used as the solvent and reductant. The ethanolic or butanolic solution containing H2PtCl6 was then added to the carbon support dispersed in the same solvent at room temperature, and the mixture was heated at the boiling point for 2 hrs. In the case of the ethanol solvent, an aqueous alkaline solution was added to ensure the completion of Pt reduction [1]. However, this step was not used during synthesis when using butanol, due to its higher reducing power. Three types of carbon supports were investigated, Vulcan carbon (VC), Ketjenblack (KB), and colloid-imprinted carbon powders (CICs). The structure and distribution of the Pt NPs were confirmed by X-ray powder diffraction (XRD) and transmission electron microscopy (TEM). Figure 1 shows the cyclic voltammograms of JM Pt/VC and Pt/CIC85 (i.e., 85 nm pore size), prepared by both the ethanol and butanol synthesis methods, in 0.5 M H2SO4. Even though the theoretical specific surface areas were 70-80 m2/gPt (particle size of 3-3.5 nm), the electrochemical active surface area (ECSA) was found to depend on the synthesis method and the carbon support. The ethanol oxidation activity of catalysts per Pt area for the three carbon supports is shown in Figure 2. A strong dependence on the carbon microstructure was observed, with Pt/CIC and Pt/KB, showing a higher activity than Pt/VC, likely due to their higher content of mesopores in the catalyst layer [2]. The correlation between catalyst activity and the carbon microstructure for each synthesis method will be discussed in detail. References: 1. J. Xie, Q. Zhang, L. Gu, S. Xu, P. Wang, J. Liu, Y. Ding, Y.F. Yao, C. Nan, M. Zhao, Y. You, Z. Zou, Nano Energy (2016) 21, 247–257. 2. T. Soboleva, X. Zhao, K. Malek, Z. Xie, T. Navessin, S. Holdcroft, ACS applied materials and interfaces (2010) 2, 2, 375–384. 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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".