Investigating the Synthesis and Characterization of a Novel "Green" H₂O₂-Assisted, Water-Soluble Chitosan/Polyvinyl Alcohol Nanofiber for Environmental End Uses.
Md Nahid PervezGeorge K StyliosPublished in: Nanomaterials (Basel, Switzerland) (2018)
The present work highlights the formation of a novel green nanofiber based on H₂O₂-assisted water-soluble chitosan/polyvinyl alcohol (WSCHT/PVA) by using water as an ecofriendly solvent and genipin used as a nontoxic cross-linker. The 20/80 blend ratio was found to have the most optimum uniform fiber morphology. WSCHT retained the same structure as WISCHT. The prepared nanofibers were characterized by Scanning electron microscopy (SEM), Fourier transform spectroscopy (FTIR), Thermo gravimetric analysis (TGA), Differential scanning calorimeter (DSC), X-ray diffraction (XRD), Water Contact Angle (WCA) and Ultraviolet-visible spectroscopy (UV-vis). During electrospinning, the crystalline microstructure of the WSCHT/PVA underwent better solidification and after cross-linking there was an increase in the melting temperature of the fiber. Swelling ratio studies revealed noticeable increase in hydrophilicity with increase of WSCHT, which was further demonstrated by the decrease of contact angle from 64.74° to 14.68°. WSCHT/PVA nanofiber mats exhibit excellent UV blocking protection with less than 5% transmittance value and also showed improved in vitro drug release properties with stable release for longer duration (cross-linked fibers) and burst release for shorter duration (uncross linked) fibers. Finally our experimental data demonstrates excellent adsorption ability of Colour Index (C.I.) reactive black 5 (RB5) due to protonated amino groups.
Keyphrases
- water soluble
- electron microscopy
- high resolution
- drug delivery
- drug release
- aqueous solution
- mass spectrometry
- white matter
- single molecule
- alcohol consumption
- hyaluronic acid
- magnetic resonance imaging
- multiple sclerosis
- ionic liquid
- room temperature
- big data
- climate change
- computed tomography
- machine learning
- risk assessment
- case control