Covalent Organic Framework Membrane with Turing Structures for Deacidification of Highly Acidic Solutions

渗透 材料科学 化学工程 共价键 反渗透 废水 工业废水处理 有机化学 共价有机骨架 色谱法 化学 废物管理 生物化学 工程类
作者
Lihong Zhou,Xiaofeng Li,Kecheng Cao,Zhimin Jia,Honghan Long,Yang Li,Guohong Tao,Ning Liu,Jie Zhang,Lijian Ma,Lihong Zhou,Xiaofeng Li,Kecheng Cao,Zhimin Jia,Honghan Long,Yang Li,Guohong Tao,Ning Liu,Jie Zhang,Lijian Ma
出处
期刊:Advanced Functional Materials [Wiley]
卷期号:32 (9) 被引量:45
标识
DOI:10.1002/adfm.202108178
摘要

Abstract The deacidification of highly acidic industrial wastewater, such as the spent fuel reprocessing feed liquid, is of great significance for the smooth progress of the subsequent treatment processes. Herein, a novel covalent organic framework (COF) membrane with bidirectional anisotropic Turing structures is synthesized for the first time and used for deacidification. The COF membrane presents linear and hydrophobic properties on one side and porous and superhydrophilic properties on the other side, which makes the water permeability of hydrophilic side 45 times higher than that of the hydrophobic side and effectively prevents the reverse osmosis of water. Impressively, the COF membrane has excellent thermal, acid, and irradiation stabilities, and an extraordinarily high ability to intercept metal ions in a complex multi‐ion solution containing 5 m HNO 3 , with the interception rate up to 100%. However, H + can easily pass through the membrane with the permeation flux of 1015.7 mol L −1 m −2 , which realizes the efficient deacidification of high acidic solutions. This study provides not only a new idea for the design and preparation of Turing‐structural membranes, but also a feasible strategy for the deacidification and process simplification of spent fuel reprocessing feed liquid and other industrial wastewater with high acidity.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
米味锅巴完成签到,获得积分10
1秒前
默默发布了新的文献求助10
1秒前
1秒前
bbb完成签到,获得积分10
1秒前
皮蛋solo粥完成签到,获得积分10
2秒前
2秒前
4秒前
笨笨的完成签到,获得积分10
6秒前
177发布了新的文献求助10
7秒前
8秒前
8秒前
molihuakai应助772829采纳,获得10
8秒前
www完成签到 ,获得积分10
9秒前
小猫完成签到,获得积分10
10秒前
xieting完成签到,获得积分10
10秒前
11秒前
11秒前
11秒前
11秒前
乐乐应助科研通管家采纳,获得10
12秒前
12秒前
FashionBoy应助科研通管家采纳,获得10
12秒前
英俊的铭应助科研通管家采纳,获得10
12秒前
科研通AI2S应助科研通管家采纳,获得10
12秒前
Akim应助科研通管家采纳,获得40
12秒前
orixero应助科研通管家采纳,获得10
12秒前
Owen应助科研通管家采纳,获得10
12秒前
Nexus应助科研通管家采纳,获得10
12秒前
12秒前
ww完成签到 ,获得积分10
12秒前
Lucas应助邹邹采纳,获得10
12秒前
地球发布了新的文献求助10
13秒前
丘比特应助zzz采纳,获得10
14秒前
15秒前
15秒前
俭朴的翠柏完成签到,获得积分20
17秒前
忙碌的陈陈陈陈完成签到,获得积分10
17秒前
天天快乐应助哈哈哈嗝采纳,获得10
17秒前
17秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443568
求助须知:如何正确求助?哪些是违规求助? 8257414
关于积分的说明 17586727
捐赠科研通 5502247
什么是DOI,文献DOI怎么找? 2900923
邀请新用户注册赠送积分活动 1877976
关于科研通互助平台的介绍 1717534