硅醇
催化作用
丙烷
化学
活化能
钴
锚固
弯曲分子几何
失真(音乐)
计算化学
脱氢
化学物理
过渡金属
屏障激活
多相催化
结晶学
过渡状态
势能
结合能
物理化学
材料科学
立体化学
活动站点
作者
Yumeng Fo,Baiting Long,Xiangyang Ji,Rui Lin,Shaojia Song,Meizan Jing,Zhen Zhao,Jian Liu,Weiyu Song
标识
DOI:10.1021/acscatal.6c04632
摘要
Abstract Zeolite-confined isolated cobalt catalysts exhibit promising performance in propane dehydrogenation (PDH), while their catalytic performance varies in the orders of magnitude despite most studies claim isolated Co sites are active sites. It is believed that these sites are stabilized by silanols generated by framework defects, which are normally considered as anchoring site. The present study shows that separation between silanol groups has a significant impact on the PDH activity of isolated cobalt sites. Here, a Co–O2 motif, in which Co(II) is anchored by two isolated internal silanols with varying separations, is employed to explore how the relative arrangement of silanol anchoring sites influences PDH performance. By varying the separation between the two silanols, the O–Co–O angle (θ) was tuned from bent to nearly linear configurations. The calculated C–H activation barriers show a positive correlation with the O–Co–O angle of the propane-adsorbed state, θC3H8*. Distortion/interaction (Activation Strain) analysis reveals that this barrier trend mainly arises from the distortion penalty required to reach the transition state. Smaller-θ sites require less angular contraction of the Co–O2 motif and enable stronger Co-centered charge reorganization during C–H cleavage. Experimental comparison provides qualitative support for this geometry-reactivity relationship: Co/S-1 catalysts with silanol-nest environments, which are associated with smaller-θ Co–O2 geometries in model structures, exhibit higher PDH performance and lower apparent activation energy than terminal-silanol samples. These results identify θ as a physically meaningful descriptor that links the geometric response of silanol-anchored Co(II) sites to propane C–H activation.
科研通智能强力驱动
Strongly Powered by AbleSci AI