Suppr超能文献

基于甘油合成用于固氮的高效FeO电催化剂。

Glycerine-based synthesis of a highly efficient FeO electrocatalyst for N fixation.

作者信息

Wang Meng, Li Feifei, Liu Juan

机构信息

Kangda College of Nanjing Medical University Lianyungang 222000 China

出版信息

RSC Adv. 2020 Aug 11;10(49):29575-29579. doi: 10.1039/d0ra05831a. eCollection 2020 Aug 5.

Abstract

The electrochemical nitrogen reduction reaction (NRR) is a promising approach to convert N into high value-added NH. However, it is still a challenge to achieve an efficient electrocatalyst for the NRR. Herein, it is demonstrated that the FeO nanoparticles (NPs) generated from a glycerine-based synthesis can be applied as highly efficient catalysts for the NRR. The FeO NPs show good performance with a high NH yield (22 μg mg h) and a favorable Faradaic efficiency (FE) (3.5%) at -0.5 V reversible hydrogen electrode (RHE). The facile synthesis strategy and satisfactory electrochemical properties demonstrate the potential application of the as-synthesized FeO NPs for NRR.

摘要

电化学氮还原反应(NRR)是一种将氮转化为高附加值氨的有前景的方法。然而,实现用于NRR的高效电催化剂仍然是一项挑战。在此,证明了通过基于甘油的合成方法生成的FeO纳米颗粒(NPs)可作为用于NRR的高效催化剂。FeO NPs在-0.5 V可逆氢电极(RHE)下表现出良好的性能,具有高氨产率(22 μg mg⁻¹ h⁻¹)和良好的法拉第效率(FE)(3.5%)。这种简便的合成策略和令人满意的电化学性能证明了所合成的FeO NPs在NRR中的潜在应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/be4a/9055988/11e4c50ee19c/d0ra05831a-f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验