Unconventional Synthesis of Hierarchically Twinned Copper as Efficient Electrocatalyst for Nitrate-Ammonia Conversion
ADVANCED MATERIALS(2024)
摘要
Twin boundary (TB) engineering provides exciting opportunities to tune the performance levels of metal-based electrocatalysts. However, the controllable construction of TB greatly relies on surfactants, blocking active sites, and electron transfer by surfactants. Here, a surfactant-free and facile strategy is proposed for synthesizing copper (Cu) nanocatalysts with dense hierarchical TB networks (HTBs) by the rapid thermal reductions in metastable CuO nanosheets in H2. As revealed by in situ transmission electron microscopy, the formation of HTBs is associated with the fragmentation of nanosheets in different directions to generate abundant crystal nuclei and subsequently unconventional crystal growth through the collision and coalescence of nuclei. Impressively, the HTBs endow Cu with excellent electrocatalytic performance for direct nitrate-ammonia conversion, superior to that of Cu with a single-oriented TB and without TB. It is discovered that the HTBs induce the formation of compressive strains, thereby creating a synergistic effect of TBs and strains to efficiently tune the binding energies of Cu with nitrogen intermediates (i.e., NO2*) and thus promote the tandem reaction process of NO3--to-NO2- and subsequent NO2--to-NH3 electrocatalysis. This work demonstrates the crucial role of HTBs for boosting electrocatalysis via the synergistic effect of TBs and strains. A surfactant-free and facile strategy is developed for synthesizing copper (Cu) nanocatalysts with dense hierarchical twin boundary networks (HTBs) by the rapid thermal reductions in metastable CuO nanosheets in H2. It is found that the HTBs can significantly promote the Cu-catalyzed reduction of nitrate to generate ammonia.image
更多查看译文
关键词
hierarchical twin boundary,lattice strain,nitrate reduction reaction,surfactant-free synthetic method,tandem electrocatalysis
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要