Wound healing activities of PVA-urea composites from Ormocarpum cochinchinense

The aim of this work is to prepare ternary blended polyvinyl alcohol (PVA)-urea hydrogels containing Ormocarpum cochinchinense, Cinnamomum zeylanicum and antibiotic cephalexin by freezing-thawing method in order to assess the wound healing qualities. In addition to being a synthetic polymer, PVA is a recyclable and biocompatible artificial polymer blend that has attracted a lot of interest in biological applications. The freezing-thawing process with PVA-urea blend is used to make hydrogel film. Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and swelling investigations were carried out for the composite membranes. Biological studies involving antibacterial, antifungal, cytotoxicity and wound healing activities were also carried out for the composite membranes. The composite membrane developed has a lot of potential for wound dressing and other applications.

Experimental Investigation on the Influence of Graphene Nanoplatelets Dispersion on the Thermal Conductivity of Sunflower Oil

In the past decades, considerable efforts have been made for the development of energy-efficient and eco-friendly convective heat transfer and lubricating agents because of growing energy demands, precision manufacturing, miniaturization and sustainability issues. In this study, different concentrations of graphene–sunflower oil nanofluid were prepared and their thermal conductivity was experimentally investigated and compared with the correlations of similar researches found in the literature. The morphology of graphene nanoplatelets was appraised by X-ray diffractometer (XRD) and scanning electron microscope (SEM). The results show that the thermal conductivity of nanofluid was enhanced with temperature and nanoparticles weight fraction. The nanoconvection at high temperatures, less meandering mobility of graphene nanoplatelets and high kinematic viscosity of graphene nanofluids at low temperatures were identified as the key factors for the thermal conductivity enhancement. Further, the concentration and temperature-dependent theoretical correlation were proposed for estimating the thermal conductivity of graphene nanofluids using backpropagation algorithm of artificial neural network (ANN) with the minimum margin of deviation.