多硫化物
材料科学
氧化还原
硫黄
动力学
催化作用
化学工程
电池(电)
密度泛函理论
硒
储能
降级(电信)
空位缺陷
电极
无机化学
纳米技术
活化能
格子(音乐)
化学动力学
硫族元素
能量密度
作者
Rong Jiang,Mingwei Jin,Changliang Du,Tinglu Song,Xilan Ma,Youqi Zhu,Chuanbao Cao,Meishuai Zou
标识
DOI:10.1002/aenm.202505028
摘要
ABSTRACT Rechargeable magnesium–sulfur (Mg─S) batteries are considered promising next‐generation energy storage solutions because of their high volumetric energy density. However, they often suffer from severe performance degradation due to the well‐known polysulfide shuttle effect and sluggish reaction kinetics. Defective materials are widely employed in metal‐sulfur battery systems due to their unique adsorptive and catalytic properties, which effectively address the challenges of polysulfide shuttle and sluggish conversion kinetics during charge‐discharge processes. Nevertheless, studies systematically correlating defect concentration with the adsorptive‐catalytic properties of electrodes remain scarce. In this study, Mo x V 1‐x Se 2 (x = 0‐0.1) with tunable selenium‐vacancy concentrations is employed as a model system to modulate its electronic structure and enhance catalytic performance. A quantitative correlation is further established between selenium‐vacancy concentration and adsorption‐catalytic properties to regulate sulfur redox kinetics. Experimental and theoretical findings indicate that a higher density of selenium vacancies effectively provides additional active sites and promotes electron accumulation, leading to reduced energy barriers for MgS nucleation/decomposition as well as faster kinetics in polysulfide conversion reactions. Thus, the Mo 0.075 V 0.925 Se 2 with abundant selenium vacancies concentrations exhibits exceptional performance as a sulfur host, delivering the highest reversible capacity (1127 mAh g −1 ) and remarkable cycling stability (200 cycles with ∼99.7% capacity retention). This study contributes to advancing the practical implementation of defect engineering with quantitative control for application in Mg─S batteries.
科研通智能强力驱动
Strongly Powered by AbleSci AI