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

吸附 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 清水 工艺工程 生化工程 废物管理 环境科学 有机化学 环境工程 材料科学 冶金 工程类 地质学 海洋学 生物 生态学
作者
Douchao Mei,Lijia Liu,Bing Yan
出处
期刊:Coordination Chemistry Reviews [Elsevier]
卷期号:475: 214917-214917 被引量:147
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
1秒前
科研通AI2S应助经竺采纳,获得10
2秒前
聪明的含蕾完成签到,获得积分10
4秒前
7秒前
9秒前
jack完成签到 ,获得积分10
9秒前
10秒前
请叫我托蒂完成签到,获得积分20
13秒前
林林发布了新的文献求助10
14秒前
小颜发布了新的文献求助10
14秒前
敬老院N号应助heqian采纳,获得30
14秒前
14秒前
15秒前
111发布了新的文献求助10
15秒前
不配.应助devil采纳,获得20
17秒前
善良冷松发布了新的文献求助10
17秒前
Muttu发布了新的文献求助10
19秒前
22秒前
22秒前
向雅发布了新的文献求助10
22秒前
23秒前
23秒前
呀呀呀呀完成签到,获得积分10
24秒前
26秒前
26秒前
共享精神应助请叫我托蒂采纳,获得10
27秒前
打打应助拼搏向上采纳,获得10
27秒前
只想发财完成签到,获得积分10
30秒前
小二郎应助111采纳,获得10
30秒前
30秒前
30秒前
善良冷松完成签到,获得积分20
30秒前
30秒前
乐观满天关注了科研通微信公众号
30秒前
123发布了新的文献求助10
32秒前
只想发财发布了新的文献求助10
32秒前
zl739860884完成签到 ,获得积分10
33秒前
小跳发布了新的文献求助10
35秒前
35秒前
ccc驳回了所所应助
35秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Le dégorgement réflexe des Acridiens 800
Defense against predation 800
Very-high-order BVD Schemes Using β-variable THINC Method 568
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3136127
求助须知:如何正确求助?哪些是违规求助? 2787029
关于积分的说明 7780244
捐赠科研通 2443154
什么是DOI,文献DOI怎么找? 1298899
科研通“疑难数据库(出版商)”最低求助积分说明 625294
版权声明 600870