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Preparation of Miscible PVA/PEG Blends and Effect of Graphene Concentration on Thermal, Crystallization, Morphological, and Mechanical Properties of PVA/PEG (10wt%) Blendopen access

Authors
Falqi, Fahad H.Bin-Dahman, Osamah A.Hussain, M.Al-Harthi, Mamdouh A.
Issue Date
Sep-2018
Publisher
Hindawi
Keywords
POLYVINYL ALCOHOL/STARCH BLENDS; POLY(VINYL ALCOHOL); OPTICAL-PROPERTIES; CROSS-LINKING; NANOCOMPOSITES; COMPOSITES; BEHAVIOR; GLYCOL; POLYMERS; GLYCEROL
Citation
International Journal of Polymer Science, v.2018, pp 1 - 11
Pages
11
Indexed
SCIE
SCOPUS
Journal Title
International Journal of Polymer Science
Volume
2018
Start Page
1
End Page
11
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/8060
DOI
10.1155/2018/8527693
ISSN
1687-9422
1687-9430
Abstract
Water-soluble polymers such as poly(vinyl alcohol) (PVA) and poly(ethylene glycol) (PEG) and their nanocomposites with graphene were prepared by using a solution mixing and casting technique. The effect of different PEG loadings was investigated to determine the optimum blend ratio. The films were characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analyzer (TGA) methods. Also, the mechanical properties including tensile strength and elongation at break were measured using a universal tensile testing machine. FTIR results confirmed the formation of the H-bond between PEG and PVA. DSC studies revealed that PEG has a significant plasticization effect on PVA as seen by the drop in the glass transition temperature (T-g). The blend with 10 wt% PEG loading was found to be the optimum blend because of good compatibility as shown by FTIR and SEM results and improved thermal properties. PVA/PEG (10%) nanocomposites were prepared using graphene as a nanofiller. It was found that the elongation at break increased by 62% from 147% for the PVA/PEG (10%) blend to 209% for the nanocomposite with graphene loading of 0.2 wt%. The experimental values of tensile strength were compared using the predictive model of Nicolais and Narkis.
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