Suppr超能文献

基于多孔共价有机框架的氢键纳米阱用于金的精确识别与分离

Porous Covalent Organic Framework Based Hydrogen-Bond Nanotrap for the Precise Recognition and Separation of Gold.

作者信息

Qiu Jikuan, Xu Chang, Xu Xianhui, Zhao Yingjie, Zhao Yang, Zhao Yuling, Wang Jianji

机构信息

Collaborative Innovation Center of Henan Province for Green Manufacturing of Fine Chemicals, School of Chemistry and Chemical Engineering, Key Laboratory of Green Chemical Media and Reactions, Ministry of Education, Henan Normal University, Xinxiang, Henan, 453007, P. R. China.

出版信息

Angew Chem Int Ed Engl. 2023 Apr 17;62(17):e202300459. doi: 10.1002/anie.202300459. Epub 2023 Mar 16.

Abstract

Utilizing weak interactions to effectively recover and separate precious metals in solution is of great importance but the practice remains a challenge. Herein, we report a novel strategy to achieve precise recognition and separation of gold by regulating the hydrogen-bond (H-bond) nanotrap within the pore of covalent organic frameworks (COFs). It is found that both COF-HNU25 and COF-HNU26 can efficiently capture Au with fast kinetics, high selectivity, and uptake capacity. In particular, the COF-HNU25 with the high density of H-bond nanotraps exhibits an excellent gold uptake capacity of 1725 mg g , which is significantly higher than that (219 mg g ) of its isostructural COF (COF-42) without H-bond nanostrap in the pores. Importantly, the uptake capacity is strongly correlated to the number of H-bonds between phenolic OH in the COF and [AuCl ] in water, and multiple H-bond interactions are the key driving force for the excellent gold recovery and reusability of the adsorbent.

摘要

利用弱相互作用有效地回收和分离溶液中的贵金属具有重要意义,但实践中仍然是一项挑战。在此,我们报告了一种通过调节共价有机框架(COFs)孔内的氢键(H键)纳米阱来实现金的精确识别和分离的新策略。研究发现,COF-HNU25和COF-HNU26都能以快速的动力学、高选择性和吸附容量有效地捕获金。特别是,具有高密度H键纳米阱的COF-HNU25表现出1725 mg g的优异金吸附容量,显著高于其孔中没有H键纳米结构的同构COF(COF-42)的吸附容量(219 mg g)。重要的是,吸附容量与COF中酚羟基与水中[AuCl]之间的氢键数量密切相关,多个氢键相互作用是吸附剂实现优异金回收和可重复使用性的关键驱动力。

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