Haitao Xu, Hao Zhang, Yan Luo, Jingzhe Zhao, Feng Li
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引用次数: 0
摘要
用于生产高附加值有机化合物的甘油氧化反应(GOR)因其在缓解能源危机方面的潜力而备受研究关注。本文报道了一种简便的 NH4Cl 辅助电沉积策略来制造三维纳米森林阵列状中空纳米结构。通过将磷掺杂Co(OH)2纳米片与Cu2S纳米管阵列(P-Co(OH)2@Cu2S NTs/CF)相结合,开发出分层异质结,实现了对GOR的优化。优化后的 P-Co(OH)2@Cu2S NTs/CF 催化剂具有优异的活性,在 1.30 V 电位(相对于 RHE)下,甲酸法拉第效率 (FE) 为 97.40%。实验结果表明,在导电载体上生长的这种独特的中空纳米森林结构可以暴露出更多的活性位点,促进电子转移,从而表现出优异的 GOR 性能。这项工作为设计用于甘油电氧化的高活性、低成本中空异质结构电催化剂提供了新的机遇。
NH4Cl-Assisted Electrosynthesis of P-Doped Co(OH)2 Nanosheet on Cu2S Hollow Nanotube Arrays for Glycerol Electrooxidation.
The glycerol oxidation reaction (GOR) for producing high-value-added organic compounds is of great research interest due to its potential in alleviating the energy crisis. Herein, a facile NH4Cl-assisted electrodeposition strategy is reported to fabricate 3D nano-forest array-like hollow nanostructures. The hierarchical heterojunction by combining phosphorus doping Co(OH)2 nanosheets with Cu2S nanotube arrays (P-Co(OH)2@Cu2S NTs/CF) is developed to realize the optimization on GOR. The optimized P-Co(OH)2@Cu2S NTs/CF catalyst exhibits an exceptional activity with a formate Faradaic efficiency (FE) of 97.40% at a potential of 1.30 V (vs RHE). The experimental results indicate that this unique hollow nano-forest structure, grown on a conductive support, can expose more active sites and facilitate electron transfer, thereby demonstrating excellent GOR performance. This work provides new opportunities for the design of electrocatalysts of high-activity and low-cost hollow heterostructure electrocatalysts for glycerol electrooxidation.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.