journal article·2022-08

A high-flux polydopamine/reduced graphene oxide/MOF-5 composite membrane via a mussel-inspired method for dye wastewater purification

Yucheng Liu, Rui Liu, Wenbo Chen, Mingyan Chen, Wenwen Tu, Meng Zhu, Wei He, Zhi Huang, Shumeng Liu

Journal of Materials Science· Volume 57· Issue 31
Publisher page

Abstract

A novel polydopamine/reduced graphene oxide/MOF-5 (PDA/RGO/MOF-5) composite membrane with high flux, outstanding dye separation performance and anti-fouling ability was prepared by synthesizing the polydopamine-modified GO/MOF-5 composite material and aggregating it on a commercial cellulose acetate (CA) membrane substrate through the vacuum filtration process. The prepared samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. Besides, scanning electron microscopy, transmission electron microscopy and atomic force microscopy were used to explore the structural characteristics of the membrane. The results showed that the permeation performance of the hybrid membrane was greatly enhanced due to the increase in the spacing between RGO layers. The flux for methylene blue (MB, cationic dye) and Congo red (CR, anionic dye) could reach 3439 ± 208 L·m−2·h−1 and 2484 ± 52 L·m−2·h−1, respectively, which were much higher than those of other polymer membranes reported so far. Moreover, the membrane material also exhibited an ideal dye rejection rate (99 ± 0.07% for MB and 97 ± 0.3% for CR). After three fouling-washing cycle experiments, the composite membrane still maintained an excellent flux recovery rate (FRR) of more than 85 ± 2%. Overall, it can be predicted that the PDA/RGO/MOF-5 membrane prepared by us has great potential in wastewater purification.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Membrane Separation Technologies · MXene and MAX Phase Materials · Graphene research and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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