化学
曲面(拓扑)
电子
化学物理
纳米技术
核物理学
几何学
物理
数学
材料科学
作者
Xingfan Zhang,Akira Yoko,Yi Zhou,W.S.S. Jee,Álvaro Mayoral,Taifeng Liu,Jingcheng Guan,You Lü,Thomas W. Keal,John Buckeridge,Kakeru Ninomiya,Maiko Nishibori,Susumu Yamamoto,Iwao Matsuda,Tadafumi Adschiri,Osamu Terasaki,Scott M. Woodley,C. Richard A. Catlow,Alexey A. Sokol
摘要
The exceptional performance of ceria (CeO2) in catalysis and energy conversion is fundamentally governed by its defect chemistry, particularly oxygen vacancies. The formation of each oxygen vacancy (VO••) is assumed to be compensated by two localized electrons on cations (Ce3+). Here, we show by combining theory with experiment that while this 1 VO••: 2Ce3+ ratio accounts for the global charge compensation, it does not apply at the local scale, particularly in nanoparticles. Hybrid quantum mechanical/molecular mechanical (QM/MM) defect calculations, together with synchrotron X-ray photoelectron spectroscopy (XPS) measurements, show that electrons have a strong preference to localize and segregate on surfaces, which can overcome the trapping force from the VO•• sites in the bulk. At a given Fermi level, the surface VO•• tends to trap more electrons than those in bulk, resulting in a higher Ce3+ to VO•• ratio on surfaces than that in the bulk, driven by the preferential localization of electrons and enhanced VO••-Ce3+coupling. Large-scale unbiased Monte Carlo simulations on ceria nanoparticles confirmed this trend and further show that the surface segregation of electrons is more pronounced at low reduction levels and in smaller nanoparticles. In highly reduced ceria nanoparticles, however, the enhanced repulsive interactions lead to a less significant extent of defect heterogeneity or even reverse the location preference of defects in some nanoparticles. Our findings underscore the need to consider both the overall nonstoichiometry and local defect behavior in easily reducible oxides, with direct relevance to their performance in catalytic and energy applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI