Towards enhanced fouling resistance of PVC ultrafiltration membrane using modified montmorillonite with folic acid
Source abstract
Abstract
Abstract The main aim of this study is to develop the highly permeable and fouling resistant polyvinyl chloride (PVC) ultrafiltration membranes consisting different amounts of natural and modified montmorillonites with folic acid (Mt-FA). Mt. was modified with folic acid to achieve the better dispersion in the PVC matrix. Mt-FA was confirmed by XRD, FTIR and TGA. The effect of the Mt. and Mt-FA on the membrane morphology, porosity, hydrophilicity, roughness, mechanical strength, pure water flux and antifouling property in humic acid removal were investigated. XRD patterns revealed the formation of exfoliated clay mineral layers in 0.5 and 1.0 wt% loading of Mt-FA and the intercalated structure for the upper concentration of Mt-FA and the PVC/Mt-0.5 membrane. All the nanocomposite membranes showed higher hydrophilicity and roughness than the neat PVC membrane. The results demonstrated that the pure water flux, porosity and mechanical strength of the membranes were enhanced with the addition of Mt-FA up to a loading of 1.5 wt%. The PVC/Mt-FA-1.0 membrane exhibited high flux recovery ratio (FRR) and humic acid rejection of 92% and 90%, respectively. This was due to the exfoliated structure, surface hydrophilicity and electrostatic repulsion between negative charge of Mt-FA and humic acid. In addition, the reusability of the optimum membrane was evaluated for the filtration of the surface water in five permeation-cleaning cycles and the results confirmed that the modified membrane with 1.0 wt% Mt-FA can offer the excellent FRR as well as the reversible fouling ratio (RFR) in the filtration process.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Membrane Separation Technologies · Graphene and Nanomaterials Applications · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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