Publications by authors named "Abdulkader S Hanbazazah"
Various fillers such as zeolites, metal-organic framework, carbon, metal framework, graphene, and covalent organic framework have been incorporated into the polymers. However, these materials are facing issues such as incompatibility with the polymer matrix, which leads to the formation of non-selective voids and thus, reduces the gas separation properties. Recent studies show that hexagonal boron nitride (h-BN) possesses attractive characteristics such as high aspect ratio, good compatibility with polymer materials, enhanced gas barrier performance, and improved mechanical properties, which could make h-BN the potential candidate to replace conventional fillers.
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- This study focuses on improving dialysis membranes to better resist protein fouling and promote biocompatibility to address health issues related to dialysis, such as various vascular diseases.
- It examines polysulfone (PSF)-based membranes, highlighting their properties like flow rate and solute clearance, and discusses advancements in commercially available options.
- The goal is to develop high flux PSF membranes that minimize oxidative stress and stabilize blood pressure, addressing the growing demand for more effective and safer dialysis solutions.
Article Synopsis
- * Proton exchange membrane fuel cells (PEMFCs) are particularly noted for their potential to lower reliance on fossil fuels, with the membrane acting as a barrier and facilitator for proton exchange.
- * While Nafion membranes are widely used due to their durability, they are expensive and can degrade over time, leading to exploration of alternative membranes to enhance performance and longevity.
The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).
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