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Unexpected Kirkendall effect in twinned icosahedral nanocrystals driven by strain gradient 被引量:1
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作者 Jingbo Huang Yucong Yan +6 位作者 Xiao Li xurong qiao Xingqiao Wu Junjie Li Rong Shen Deren Yang Hui Zhang 《Nano Research》 SCIE EI CAS CSCD 2020年第10期2641-2649,共9页
Nanoscale Kirkendall effect has been widely used for rationally fabricating high-quality hollow nanocrystals, but often requires the intrinsic diffusion coefficient of out-diffusion materials higher than that of in-di... Nanoscale Kirkendall effect has been widely used for rationally fabricating high-quality hollow nanocrystals, but often requires the intrinsic diffusion coefficient of out-diffusion materials higher than that of in-diffusion components. Here we demonstrate an unexpected Kirkendall effect that occurs in diffusing intrinsically faster Cu atoms into Pd icosahedra, leading to the formation of PdCu alloyed hollow nanocrystals. The control experiment with Pd octahedra replacing icosahedra indicates the critical role of twin boundaries in facilitating such unexpected Kirkendall effect. In addition, geometric phase analysis and density functional theory calculation show that out-diffusion of Pd atoms in the icosahedra is faster than in-diffusion of Cu atoms, particularly through the twin boundaries, upon the strain gradient with an inward distribution from tensile to compressive strains. The unexpected Kirkendall effect is also found in the interdiffusion of Ag and Pd atoms in Pd icosahedra. Our finds break the limitation of the intrinsic diffusion coefficient for the synthesis of hollow nanocrystals through Kirkendall effect and are expected to enormously enrich the family of hollow nanocrystals which have shown great potential in broad areas, such as fine chemical production, energy storage and conversion, and environmental protection. This work also provides a deep understanding in the diffusion behavior of atoms upon the strain gradient. 展开更多
关键词 Kirkendall effect diffusion coefficient INTERDIFFUSION chemical potential icosahedral nanocrystals strain gradient
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