Adsorption of uranium (VI) by metal-organic frameworks and covalent-organic frameworks from water

吸附 铀 化学 海水 金属有机骨架 废水 浓缩铀 环境化学 核能 人体净化 工艺工程 废物管理 环境科学 有机化学 环境工程 材料科学 冶金 生态学 工程类 地质学 海洋学 生物
作者
Douchao Mei,Lijia Liu,Bing Yan
出处
期刊:Coordination Chemistry Reviews [Elsevier BV]
卷期号:475: 214917-214917 被引量:354
标识
DOI:10.1016/j.ccr.2022.214917
摘要

As we all know, energy and environment are two everlasting themes for the development of society. Nuclear power source, as a clean energy that is easy to be stored, has been rapidly developed in the past few decades. Because uranium is the main nuclear fuel, mining uranium from seawater is essential. Besides, uranium-containing wastewater discharged by nuclear industry also pose a serious threat for ecological environment. Considering the radioactivity and toxicity of uranium, it is urgent for us to remove U(VI) from wastewater. To achieve these ends, various uranium adsorption materials have been developed. Among them, metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) have aroused wide concern owing to the advantages of high specific surface areas, abundant active adsorption sites and controllable pore structure. However, there is huge room for MOFs and COFs in the application of uranium treatment. Herein, we provide a comprehensive review on MOFs and COFs for the enrichment and removal of U(VI) from seawater and wastewater, including synthetic approach, influencing factors, possible adsorption mechanism, as well as the performance comparison with other materials. In addition, the problem of current research is pointed out and the future direction about MOFs and COFs in uranium treatment is discussed. Noteworthy, a novel recurrent neural network (RNN) model is creatively put forward to connect the adsorption and detection of U(VI). More interestingly, the deep machine learning (ML) algorithm can replace the use of inductively couple plasma optimal emission spectrometry (ICP-OES). The goal of this paper is to provide guidance for the synthesis of novel MOFs and COFs U-adsorbents and broaden their application in the treatment of U(VI).
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
咔咔完成签到 ,获得积分10
刚刚
小萌兽发布了新的文献求助10
刚刚
赘婿的应助被幸福语海采纳,获得10
刚刚
chen完成签到,获得积分10
1秒前
杨梦宜发布了新的文献求助10
1秒前
伏玉完成签到,获得积分10
1秒前
橙子发布了新的文献求助10
1秒前
沉默的小笼包完成签到,获得积分10
2秒前
2秒前
森一发布了新的文献求助10
2秒前
麻花完成签到,获得积分10
2秒前
霸气的金针菇的应助被小兰采纳,获得10
2秒前
霸气鞯完成签到,获得积分10
3秒前
3秒前
4秒前
林夕完成签到,获得积分10
4秒前
YuuTuss发布了新的文献求助10
5秒前
一往而深发布了新的文献求助10
6秒前
张晶晶完成签到,获得积分10
6秒前
6秒前
腼腆的立辉完成签到,获得积分10
6秒前
6秒前
6秒前
7秒前
852的应助被霸气鞯采纳,获得10
7秒前
8秒前
Ran发布了新的文献求助10
8秒前
8秒前
lle发布了新的文献求助10
9秒前
深情安青的应助被pingpang采纳,获得10
9秒前
9秒前
Carrie完成签到,获得积分20
9秒前
青衫烟雨客完成签到 ,获得积分10
9秒前
今后的应助被甜甜的棉花糖采纳,获得10
11秒前
DW的应助被明理含之采纳,获得10
11秒前
派大星发布了新的文献求助150
11秒前
Carrie发布了新的文献求助10
12秒前
12秒前
12秒前
bkagyin的应助被橙子采纳,获得10
12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
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 1000
Электротехническая система магнитожидкостной герметизации валов регулируемых электродвигателей 800
日本語と中国語及びチベット・ビルマ語の語源関連性に関する分析 800
Practical Manual of Forensic Psychophysiology: Methodology, Execution, and Fields of Application 800
Holistic Discourse Analysis, Second Edition by Robert E. Longacre 666
The Basics of Practical Optimization, Second Edition 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 有机化学 物理 生物化学 化学工程 内科学 复合材料 催化作用 心理学 人工智能 细胞生物学 光电子学 无机化学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 7974098
求助须知:如何正确求助?哪些是违规求助? 9438765
关于积分的说明 21058556
捐赠科研通 7550360
什么是DOI,文献DOI怎么找? 3373938
关于科研通互助平台的介绍 2508872
邀请新用户注册赠送积分活动 2406996