The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Enhancing the Activity of Fuel-cell Reactions by Designing Three-dimensional Nanostructured Architectures: Catalyst-modified Carbon−Silica Composite Aerogels
Abstract
We have developed highly active electrocatalytic nanostructured architectures constructed from carbon−silica composite aerogels in which the carbon is modified with nanoscopic electrocatalysts. Our procedure is general and can be applied to any preformed catalyst-modified carbon powder. The aerogel architecture locks in an electronic path through the carbon guest as well as a continuous, three-dimensional mesoporous transport path for fuel molecules, solvent, and ions. The electrocatalytic activity for methanol oxidation at colloidal-Pt-modified carbon−silica composite aerogels increases by 4 orders of magnitude per gram of Pt over that at native Pt-modified carbon powder. Supported catalysts derived from direct adsorption of colloidal Pt too large to enter the micropores of the carbon are more active per gram of Pt than those prepared using impregnation techniques. A further improvement in mass-normalized activity is achieved by appropriately annealing (temperature and atmosphere) Pt-modified carbon−silica composite aerogels to increase the size of the supported Pt to >3 nm but <4 nm.
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Cites
Particle Size Effects for Oxygen Reduction on Highly Dispersed Platinum in Acid Electrolytes
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Metadata
- Authors
- Anderson, M. L., Stroud, R. M., & Rolison, D. R.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-01-05
- Volume
- 2
- Issue
- 3
- Pages
- 235-240
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2030343210
- Cited by
- 198
- References
- 1
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
