Size-Controlled Electronic Structure Tuning in Ru–CrO x Heteronanoclusters
Xinxu Zhang, Xinran Zhou, Guo Li, Jiahao Wei, Qi Zhao, Yonghui Li
Tianjin University
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Cluster–cluster heterostructures (CCheteros) are emerging as promising catalysts for alkaline hydrogen evolution reaction (HER), benefiting from coupled interfaces and functional complementarity. Among various design parameters, cluster size can be experimentally controlled and plays a pivotal role in the structure–activity relationship. However, the impacts of cluster sizes on interfacial electronic structures remain underexplored. Herein, in this paper, 20 Ru–CrO x CCheteros obtained by systematically varying the sizes of Ru and CrO x clusters are analyzed via first-principles simulations to derive size-dependent interfacial electronic properties based on the experimentally validated Ru–CrO x CChetero, an effective HER catalyst in alkaline media. The results reveal that the interfacial electronic properties arise from nonlinear and cooperative effects of both cluster sizes. Compared to CCheteros with small Ru clusters (<15 Ru atoms), those with larger Ru clusters exhibit saturated electronic structure features, such as formation energy, binding energy, work function, and d-band center, indicating diminished tunability. Thus, maintaining a limited Ru cluster size is essential for electronic modulation. Enlarging Ru clusters increases the dipole magnitude and aligns it more closely with the interface normal, whereas increasing CrO x cluster size tends to orient the dipole away from the interface normal. Furthermore, the d -band centers may be pinned when the Ru cluster in CCheteros is too large (>30 Ru atoms) and leading to a nontunable surface adsorption capacity. In contrast, with smaller Ru clusters, increasing CrO x size downshifts the d -band center, suggesting the reduction of the adsorption capability. As a result, tuning cluster sizes may be an experimentally feasible way in designing high-performance CCheteros for surface science.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
工程Electrocatalysts for Energy Conversion
Machine Learning in Materials Science · CO2 Reduction Techniques and Catalysts
参考文献 29
此处列出前 3 条
引用本文 1
按被引量排序,此处列出前 3 条