Suppr超能文献

用于新兴药物污染物吸附的碳的机理与反应活化研究

Mechanistic and reactional activation study of carbons destined for emerging pharmaceutical pollutant adsorption.

作者信息

Samghouli Nora, Bencheikh Imane, Azoulay Karima, Jansson Stina, El Hajjaji Souad

机构信息

Laboratory of Spectroscopy, Molecular, Modeling, Materials, Nanomaterials, Water and Environment, (LS3MNWE), Department of Chemistry, Faculty of Sciences, Mohammed V University in Rabat, Av IbnBattouta, B.P. 1014, 10000, Rabat, Morocco.

Department of Chemistry, Umeå University, SE-901 87, Umeå, Sweden.

出版信息

Environ Monit Assess. 2025 Feb 10;197(3):259. doi: 10.1007/s10661-025-13685-4.

Abstract

In this review, several factors have been collected from previous studies on emerging pharmaceutical pollutant adsorption to explain and describe the mechanisms and determine the reactions involved: X-ray Photoelectron Spectroscopy (XPS), Fourier Transform Infrared Spectroscopy (FTIR), and the Boehm titration are the most used characterization techniques to determine activated carbons' surface functional groups. Some studies have confirmed that the specific surface area and the pore structure are not more important than the functional groups present in the adsorbent surface to explain the amount of adsorption obtained and to describe correctly the interaction between the adsorbent-adsorbate. After the analysis of several studies, we concluded that to have good adsorption, it is necessary to choose the right treatment with the right activating agent to obtain the appropriate functions that will enhance the adsorption process. In addition, the functions that can react with the pharmaceutical pollutants are the oxygenated functions such as hydroxyl function, carboxylic function, and carbonyl function.

摘要

在本综述中,我们从先前关于新兴药物污染物吸附的研究中收集了几个因素,以解释和描述其机制,并确定其中涉及的反应:X射线光电子能谱(XPS)、傅里叶变换红外光谱(FTIR)和 Boehm 滴定法是用于确定活性炭表面官能团的最常用表征技术。一些研究证实,比表面积和孔隙结构在解释吸附量以及正确描述吸附剂与吸附质之间的相互作用方面,并不比吸附剂表面存在的官能团更重要。在对多项研究进行分析后,我们得出结论,要实现良好的吸附效果,有必要选择合适的活化剂进行适当的处理,以获得能够增强吸附过程的合适官能团。此外,能够与药物污染物发生反应的官能团是含氧官能团,如羟基官能团、羧基官能团和羰基官能团。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9760/11811452/2c88b0d6446b/10661_2025_13685_Fig1_HTML.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