双功能
材料科学
电池(电)
纳米技术
水准点(测量)
氧化物
氧还原反应
催化作用
可扩展性
电化学
石墨烯
计算机科学
阴极
电催化剂
析氧
纳米颗粒
活动站点
电子结构
氧还原
纳米结构
基质(化学分析)
晶体管
密度泛函理论
纳米复合材料
材料设计
氧气
还原(数学)
作者
Xiaoran Zheng,Sajjad S. Mofarah,Richard F. Webster,Jinqiang Zhang,Claudio Cazorla,Yan Nie,Shuhao Wang,Yue Jiang,Thibault Cosseron De Villenoisy,Yì Wáng,Yu Yao,Tingwen Zhao,Liming Dai,Shery L. Y. Chang,Chuan Zhao,Dewei Chu,L. R. Sheppard,Pramod Koshy,Charles C. Sorrell
标识
DOI:10.1038/s41467-026-69849-4
摘要
Research on high-entropy oxides generally is limited to elemental and structural interpretations applied to the performance of cathodes. However, there are only limited data on the principles of increasing disorder in terms of structural, electronic, and atomic mechanisms as materials convert from ordered to disordered. Zn-air battery cathodes are limited by slow kinetics, imbalanced oxygen evolution reaction charging, imbalanced oxygen reduction reaction discharging, and scalability (through the necessity of benchmark noble metals). The present work pioneers the engineering of multilevel disorder in high-entropy oxides, thereby transforming an intrinsically inactive matrix into a highly active cathode. Systematic modification of the disorder through increasing number of cations leads to the abrupt development of structural (2D defects), electronic (semimetallic conductivity), and atomic (low-coordination Ce) disorder. This multilevel disorder engineering of high-entropy oxides results in MnNiCoFe-CeO2 catalysts with stable active sites, rapid and balanced bifunctional (oxygen evolution/reduction reaction) performance, thereby promising Zn-air battery efficiencies and electrochemical durabilities greater than those of the benchmark materials). The role of disorder in high-entropy oxides in electrocatalysis and zinc–air batteries remain unclear. Here, the authors induce controlled multilevel structural, electronic and atomic disorder to create new active sites, enabling robust, balanced oxygen catalysis and efficient zinc–air batteries.
科研通智能强力驱动
Strongly Powered by AbleSci AI