The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Predicted Trends of Core−Shell Preferences for 132 Late Transition-Metal Binary-Alloy Nanoparticles
Abstract
Transition-metal alloyed nanoparticles with core-shell features (shell enrichment by one of the metals) are becoming ubiquitous, from (electro-)catalysis to biomedical applications, due to their size control, performance, biocompatibility, and cost. We investigate 132 binary-alloyed nanoparticle systems (groups 8 to 11 in the Periodic Table) using density functional theory (DFT) and systematically explore their segregation energies to determine core-shell preferences. We find that core-shell preferences are generally described by two independent factors: (1) cohesive energy (related to vapor pressure) and (2) atomic size (quantified by the Wigner-Seitz radius), and the interplay between them. These independent factors are shown to provide general trends for the surface segregation preference for atoms in nanoparticles, as well as semi-infinite surfaces, and give a simple correlation (a "design map") for the alloying and catalytic behavior. Finally, we provide a universal description of core-shell preference via tight-binding theory (band-energy differences) that (i) quantitatively reproduces the DFT segregation energies and (ii) confirms the electronic origins and correlations for core-shell behavior.
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Cites
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,638
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Accurate and simple analytic representation of the electron-gas correlation energy
Perdew, J. P., & Wang, Y. (1992) · Physical Review B · doi:10.1103/physrevb.45.13244 · Cited by 25,089
Introduction to Solid State Physics
Kittel, C., & Fan, H. Y. (1957) · American Journal of Physics · doi:10.1119/1.1934457 · Cited by 22,240
Introduction to Solid State Physics
Kittel, C., & Dexter, D. L. (1953) · American Journal of Physics · doi:10.1119/1.1933590 · Cited by 13,240
Wold, S., Esbensen, K., & Geladi, P. (1987) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(87)80084-9 · Cited by 11,606
Improved Oxygen Reduction Activity on Pt 3 Ni(111) via Increased Surface Site Availability
Stamenkovic, V. R., Fowler, B., Mun, B. S., Wang, G., Ross, P. N., Lucas, C. A., & Marković, N. M. (2007) · Science · doi:10.1126/science.1135941 · Cited by 4,421
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Esposito, D. V., Hunt, S. T., Kimmel, Y. C., & Chen, J. G. (2012) · Journal of the American Chemical Society · doi:10.1021/ja208656v · Cited by 538
Liu, Z., Jackson, G. S., & Eichhorn, B. W. (2010) · Angewandte Chemie International Edition · doi:10.1002/anie.200907019 · Cited by 192
Wagener, P., Jakobi, J., Rehbock, C., Chakravadhanula, V. S. K., Thede, C., Wiedwald, U., Bartsch, M., Kienle, L., & Barcikowski, S. (2016) · Scientific Reports · doi:10.1038/srep23352 · Cited by 150
Iridium–Nickel Nanoparticle-Based Aerogels for Oxygen Evolution Reaction
Park, S., Utsch, N., Carmo, M., Shviro, M., & Stolten, D. (2022) · ACS Applied Nano Materials · doi:10.1021/acsanm.2c04000 · Cited by 16
Metadata
- Authors
- Wang, L.-L., & Johnson, D. D.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-09-14
- Volume
- 131
- Issue
- 39
- Pages
- 14023-14029
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W1991114008
- Cited by
- 260
- References
- 30
- FWCI
- 5.42
- Type
- journal article
- Language
- en
- Open access
- closed
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