四方晶系
奥斯特瓦尔德成熟
纳米线
催化作用
相(物质)
材料科学
纳米技术
化学工程
肺表面活性物质
晶体结构
化学
结晶学
有机化学
工程类
作者
Suli Liu,Zengsong Zhang,Jianchun Bao,Ya‐Qian Lan,Wenwen Tu,Min Han,Zhihui Dai
摘要
The rich phase structure of Cu2Se may provide good opportunities for modulating and optimizing their catalytic properties. However, chemical synthesis of Cu2Se nanostructures with different crystal phases still is a key challenge, and their catalytic application in energy fields has not been studied. In this paper, pure tetragonal and cubic phases Cu2Se nanowires (NWs) that assembled by small nanocubes have been controllably synthesized via a simple solid–liquid phase chemical transformation method, i.e., thermal treatment of presynthesized Cu NWs in Se precursor solution containing proper surfactant and 1-octadecene. Besides reaction temperature and ripening time, the functional groups and alkyl chain length of used surfactant greatly affect the kinetics of the transformation reaction and phase structure control of Cu2Se NWs. The trioctylphosphine is found to be the optimal surfactant, which not only accelerates the transformation reaction but also improves the stable temperature of tetragonal phase Cu2Se NWs about 80 °C compared with that of their bulk counterparts. Electrochemical tests reveal that both the obtained Cu2Se NWs can be used to catalyze oxygen reduction reaction (ORR) in alkaline media. But the catalytic performance of tetragonal phase Cu2Se NWs is much higher than that of cubic phase ones, which is even better than that of commercial Pd/C and some reported non-Pt electrocatalysts. The diverse catalytic performances of those Cu2Se NWs result from their distinct spatial arrangement means of Cu and Se atoms that lead to different adsorption and activation of O2 molecules approaches, as evidenced by electrocatalytic dynamic experiments. The ORR on tetragonal phase Cu2Se NWs abides by the direct 4e– mechanism, whereas that on cubic phase ones complies with dual-path mechanism comprising both 2e– and 4e– pathways.
科研通智能强力驱动
Strongly Powered by AbleSci AI