硫醚
薄膜复合膜
界面聚合
反渗透
聚酰胺
膜
氯
氯化物
化学
化学工程
核化学
高分子化学
有机化学
单体
聚合物
生物化学
工程类
作者
Sujiao Cao,Gang Zhang,Chen Xiong,Shengru Long,Xiaojun Wang,Jie Yang
标识
DOI:10.1016/j.memsci.2018.07.052
摘要
In this study, chemically reductive thioether units were introduced into the polyamide (PA) layer of a thin film composite (TFC) reverse osmosis (RO) membrane to enhance its chlorine resistance. The sulphur moieties could be oxidized to partially capture chlorine, therefore protecting the membrane from being chlorinated. The thioether units were introduced into the PA layer by partially replacing trimesoyl chloride (TMC) with 4, 4′-thiodibenzoyl chloride (TDC) in the organic phase during interfacial polymerization. Both ATR-FTIR and XPS investigations revealed that the thioether units had been incorporated into the PA layer successfully. In addition, the introduced thioether units were also uniformly dispersed, as confirmed by the STEM-EDS results. The introduction of the thioether units influenced the morphology of the membrane significantly, which in turn markedly impacted its performance. The introduction of thioether groups was found to have a positive effect on improving the chlorine resistance of the TFC membrane. Taking the 60%-TDC-TFC membrane as an example, the chlorine exposure (95 ppm h) at which the normalized flux fell below 1 was much higher than that of the pure membrane (34 ppm h) when chlorinated in an acidic environment. Under alkaline conditions, the chlorine exposure (30,430 ppm h) at which the normalized rejection decreased to less than 1 was approximately three times of that of the pure membrane (10,839 ppm h). One step of the potential mechanism revealed by the ATR-FTIR and XPS results involved the incorporated thioether units consuming part of the chlorine and undergoing oxidation. Another involved the fact that both the produced sulfoxide and sulfone groups could form hydrogen bonds with the neighbouring hydrogens, which would replace the hydrogen bonds destroyed by chlorination.
科研通智能强力驱动
Strongly Powered by AbleSci AI