A review of performance improvement strategies for graphene oxide-based and graphene-based membranes in water treatment
Source abstract
Abstract
In the past few decades, due to the rapid development of industry and the rapid growth of population, emissions of pollutants to the environment have increased dramatically, and the demand for drinking water is also increasing. Water treatment is a matter of concern because it is directly related to the health of humans and wildlife. Graphene and its derivatives have potential applications in seawater desalination and wastewater treatment due to their unique pore structure and ionic molecular sieving separation capabilities. Graphene, graphene oxide (GO), and reduced graphene oxide (rGO) can be formulated into nanoporous materials and composites with tunable properties that can be optimized for water filtration. Methods for perforating graphene include ion etching/ion bombardment and electron beam nanometer engraving, which are briefly introduced in this paper. Graphene-based composites further expand the capabilities of graphene in seawater desalination and wastewater treatment, by introducing new features and properties. In this review, the performance improvement of graphene-based separation membranes in decontamination and desalination in recent years is reviewed in detail. This review focuses on improving the performance of graphene-based membranes for separation, decontamination, and seawater desalination applications, by discussing how various modifications and preparation methods impact important performance properties, including water permeance, selectivity, rejection of solutes, membrane mechanical strength, and antifouling characteristics. We also discuss the outlook for future development of graphene-based membranes.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Graphene research and applications · Membrane Separation Technologies · Graphene and Nanomaterials Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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