聚酰胺
纳滤
界面聚合
纳米复合材料
材料科学
纳米材料
化学工程
膜
薄膜复合膜
聚合
纳米技术
复合材料
化学
聚合物
工程类
单体
生物化学
反渗透
作者
Xiaowen Huo,Yue Miao,Ziyang Guo,Zhiqiang Shi,Yanjun Jia,Haitao Wang,Na Chang
标识
DOI:10.1016/j.cej.2024.154488
摘要
Metal-organic frameworks (MOFs) is always a research hotspot as the modifier for fabrication of thin-film nanocomposite (TFN) membrane due to their excellent chemical tunability and suitable pore size. However, the traditional strategy for the involvement of MOFs into interfacial polymerization (IP) process has its limitations, as the addition of MOFs only serve to modulate the structure of the polyamide (PA) layer and to optimize the transport path of water molecules, rather than play an important role in selectively sieving solutes. To solve the problem that MOF embedded under the PA layer cannot directly participate in solute sieving, this study designed a "transmembrane" structure of MOF in the PA layer. Here, the feasibility of 1,3,5-Benzenetricarbonyl chloride modified UiO-66-(NH2)2 was verified based on Gibbs free energy calculations. By adding chlorinated UiO-66-(NH2)2 nanomaterials in the dual IP process, a high-throughput nanofiltration membrane was innovatively constructed. In contrast to conventional thin-film composite membrane prepared by only one IP process, the sieving mechanism no longer relied solely on the free volume of the PA chain segments. Instead, it was determined by the combination of the pore channels of UiO-66-(NH2)2 and the matrix gaps. Based on density functional theory (DFT), the calculated diffusion energy barriers of H2O and Na2SO4 in UiO-66-(NH2)2 and PA matrices reveal the reasons for the improvement of water flux and changes in retention rate of TFN membranes. In addition, the gap size between PA-MOFs was determined by comparing the solvation and de-solvation structures of dye molecules. This work provides a new theoretical basis for explaining the changes in the separation performance of TFN membranes, and offers new ideas for the development of novel TFN membrane structures.
科研通智能强力驱动
Strongly Powered by AbleSci AI