High performance hierarchically nanostructured graphene oxide/covalent organic framework hybrid membranes for stable organic solvent nanofiltration
Source abstract
Abstract
Abstract Stable nanofiltration membranes with excellent tolerance to organic solvents and harsh chemical environment are in high demand from petrochemical, pharmaceutical and oil refinery industries. As promising nanofiltration membrane candidates, graphene oxide lamellar membranes suffer from poor flux as well as instability under practical application conditions. Incorporating additional nanopores were believed to be an effective way to improve solvent transportation while enabling efficient molecular sieving. Herein, as a proof of concept, we designed and prepared a hierarchically nanostructured (i.e. with nanopores, micropores, mesopores) graphene oxide/covalent organic framework (GO/COF) hybrid membrane by intercalating imine-based COF nanoparticles. The imine-based COF nanoparticles with inside hollow structure and in-plane nanopores (1.8 nm) as well as abundant edge functional groups become superhydrophilic, providing additional fast transport nanochannels for water and organic solvents molecules in GO matrix. The hierarchically nanostructured GO/COF hybrid membrane shows excellent water (59 L m − 2 h − 1 bar−1) and organic solvents (60 and 51 L m − 2 h − 1 bar−1 of methanol and ethanol, respectively) permeate flux at 1 bar and superior organic dyes rejection rates for Methylene blue (320 g/mol) and Congo red (697 g/mol) in ethanol solvent of 99% and 99.82%, respectively. Additionally, intercalating COF could greatly reduce the GO matrix swelling to enhance membrane stability upon exposure to water as well as strong acidic/base solution, enabling a high filtration performance during long-term (48 h) operation.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Membrane Separation Technologies · Covalent Organic Framework Applications · Membrane Separation and Gas Transport
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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