铜
原位
透射电子显微镜
催化作用
扫描电子显微镜
材料科学
扫描透射电子显微镜
化学工程
化学
冶金
纳米技术
复合材料
有机化学
工程类
作者
Xiansheng Li,Henrik Eliasson,Walid Dachraoui,Rolf Erni
标识
DOI:10.1016/j.apsusc.2025.162321
摘要
• In situ gas-cell STEM was employed to directly observe copper nanoparticle dynamics in commercial Cu/ZnO/Al 2 O 3 catalysts during CO 2 hydrogenation relevant conditions. • Copper nanoparticles sintering is dominated by Ostwald ripening under high temperatures and reactive atmospheres. • Copper exhibits higher mobility than zinc, leading to phase separation and disruption of the copper-zinc synergism. • Despite accelerated conditions due to electron beam effects, our observations are consistent with deactivation phenomena seen in industrial catalysts. Commercial Cu/ZnO/Al 2 O 3 (CZA) catalysts used for CO 2 hydrogenation to methanol are prone to deactivation. However, information on their structural evolution and sintering mechanisms during deactivation remains scarce. In this study, we employed in situ scanning transmission electron microscopy (STEM) to investigate the dynamic behavior of copper nanoparticles (NPs) in CZA under different treatment and reaction conditions, offering real-time insights into sintering and migration mechanisms that are difficult to capture through conventional ex situ methods. We directly observed both particle coalescence and Ostwald ripening under high temperatures and reactive atmospheres, revealing that while coalescence was rare, Ostwald ripening was a dominant factor in copper migration, particularly in N 2 and O 2 environments and at high temperatures. These findings suggest that copper-zinc separation, due to higher copper mobility, plays a crucial role in the deactivation of CZA catalyst, especially under accelerated test conditions. While our experimental conditions merely mimic industrial settings, the trends we observed provide critical insights into the mechanisms of copper growth and deactivation, outlining strategies to enhance catalyst durability in methanol synthesis.
科研通智能强力驱动
Strongly Powered by AbleSci AI