吸附
吸附
背景(考古学)
危险废物
纳米复合材料
可重用性
化学工程
材料科学
环境化学
化学
纳米技术
有机化学
废物管理
计算机科学
地质学
工程类
古生物学
软件
程序设计语言
作者
Francesco Calore,Elena Badetti,Alessandro Bonetto,Anna Pozzobon,Antonio Marcomini
标识
DOI:10.1016/j.emcon.2024.100303
摘要
and polyfluoroalkyl substances (PFAS) are a class of ubiquitous, persistent, and hazardous pollutants that raise concerns for human health and the environment. Typically, PFAS removal from water relies on adsorption techniques using conventional sorption materials like activated carbons (ACs) and ion exchange resins (IERs). However, there is a continuous search for more efficient and performing adsorbent materials to better address the wide range of chemical structures of PFAS in the environment, to increase their selectivity, and to achieve an overall high adsorption capacity and faster uptake kinetics. In this context, results from the application of non-conventional sorption materials (i.e., readily available biological-based materials like proteins and advanced materials like nanocomposites and cyclodextrins) are reported and discussed in consideration of the following criteria: i) removal efficiency and kinetics of legacy PFAS (e.g., PFOA, PFBA) as well as newly-introduced and emerging PFAS (e.g., GenX), ii) representativity of environmental conditions in the experimental setup (e.g., use of environmentally relevant experimental concentrations), iii) regenerability, reusability and applicability of the materials, and iv) role of the material modifications on PFAS adsorption. From this review, it emerged that organic frameworks, nano(ligno)cellulosic-based materials, and layered double hydroxides are among the most promising materials herein investigated for PFAS adsorption, and it was also observed that the presence of fluorine- and amine-moieties in the material structure improve both the selectivity and PFAS uptake. However, the lack of data on their applicability in real environments and the costs involved means that this research is still in its infancy and need further investigation.
科研通智能强力驱动
Strongly Powered by AbleSci AI