journal article·2018-05

Influence of graphene oxide sheets on the pore structure and filtration performance of a novel graphene oxide/silica/polyacrylonitrile mixed matrix membrane

Qiao Liu, Lin Li, Xin Jin, Chunlei Wang, Tonghua Wang

Journal of Materials Science· Volume 53· Issue 9
Publisher page

Abstract

The novel graphene oxide (GO)/silica (SiO2)/polyacrylonitrile (PAN) mixed matrix membranes (MMMs) with high filtration flux and excellent antifouling performance were designed and fabricated in situ by the method of non-solvent induced phase separation (NIPS) from the precursor of PAN hybridized with GO, tetraethoxysilane and 3-aminopropyltriethoxysilane. The influences of GO sheets on the pore and chemical structure, hydrophilic nature and filtration performance of derived GO/SiO2/PAN MMMs were investigated by the scanning electron microscopy, field emission scanning electron microscopy, atomic force microscopy, energy-dispersive X-ray, Fourier transform infrared spectrometer, pure water contact angles and filtration performance. Results indicated that in situ incorporation of GO sheets and SiO2 molecules into PAN matrix via NIPS reconstructs the porous structure of derived GO/SiO2/PAN MMMs with the upright finger-like holes, porous bottom, thinner top layer and high porosity. The spontaneous surface migration or segregation of hydrophilic GO sheets and SiO2 molecules as well as their synergistic interaction occurred during NIPS greatly ameliorate the top surface structure and property of derived membranes with smoother surface, uniform pore structure and good hydrophilicity. The derived GO/SiO2/PAN MMMs exhibit a high water filtration flux of 387 L m−2 h−1 with the bull serum albumin rejection rate up to 99% and significant enhancement of antifouling performance.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Membrane Separation Technologies · Graphene and Nanomaterials Applications · Graphene research and applications

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

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