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The mechanical properties, water absorption, biodegradation, multiwalled carbon nanotubes (MWCNTs) recovery and reusability of chitosan/oxidized MWCNTs nanocomposites were investigated. The highest Young's modulus (E) was obtained by the nanocomposites with 0.1 wt.% MWCNTs, while further increase of MWCNTs loading decreases the tensile strength (TS) and E. The water absorption and degradation rate of the nanocomposites were decreased by the loading of MWCNTs; 89.7% of MWCNTs were recovered by physical base separation. Thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and tensile test results showed that the recovered MWCNTs displayed properties similar to the oxidized MWCNTs, suggesting the possibility of reuse and recycle.

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This study focuses on the development of an asymmetric membrane comprised of an electrospun carboxyl‐functionalised multi‐walled carbon nanotube (COOH‐MWCNT)/poly (vinyl alcohol) (PVA) composite nanofibre layer on a dense PVA separation layer (MCOOH‐MWCNT). The electrospun nanofibre layer in this study acts as a “pre‐selective layer” instead of its common function as a membrane support. In addition, COOH‐MWCNT, a hydrophilic filler is integrated in the nanofibres to further enhance the membrane pervaporation separation performance. Immense improvement was observed in the pervaporation performance of the resultant asymmetric membranes in the dehydration of aqueous 1,4‐dioxane solutions, owing to the presence of the electrospun composite nanofibres as a hydrophilic layer. The resultant asymmetric membrane showed an increase of nearly 80% in water permeation flux as compared to that of the dense PVA membrane alone, and the separation factor was improved from 392.65 to 605.35. The parameters estimated using Rautenbach model showed that the dehydration of aqueous 1,4‐dioxane solutions via pervaporation is dominantly governed by sorption process. The permeation flux, transport coefficient of water and 1,4‐dioxane of the electrospun asymmetric membrane showed a good agreement in between the experimental data and those predicted using Rautenbach model.