Noor Badshah

Sn doping induced enhancement in the activity of ZnO nanostructures against antibiotic resistant S. aureus bacteria

Highly ionic metal oxide nanostructures are attractive, not only for their physiochemical properties but also for antibacterial activity. Zinc oxide (ZnO) nanostructures are known to have inhibitory activity against many pathogens but very little is known about doping effects on it. The antibacterial activity of undoped ZnO and tin (Sn) doped ZnO nanostructures synthesized by a simple, versatile, and wet chemical technique have been investigated against Escherichia coli, methicillin-resistant Staphylococcus aureus, and Pseudomonas aeruginosa bacterial strains. It has been interestingly observed that Sn doping enhanced the inhibitory activity of ZnO against S. aureus more efficiently than the other two bacterial strains. From cytotoxicity and reactive oxygen species (ROS) production studies it is found that Sn doping concentration in ZnO does not alter the cytotoxicity and ROS production very much. It has also been observed that undoped and Sn doped ZnO nanostructures are biosafe and biocompatible materials towards SH-SY5Y Cells. The observed behavior of ZnO nanostructures with Sn doping is a new way to prevent bacterial infections of S. aureus, especially on skin, when using these nanostructures in creams or lotions in addition to their sunscreen property as an ultraviolet filter. Structural investigations have confirmed the formation of a single phase wurtzite structure of ZnO. The morphology of ZnO nanostructures is found to vary from spherical to rod shaped as a function of Sn doping. The excitation absorption peak of ZnO is observed to have a blue shift, with Sn doping leading toward a significant tuning in band gap.

Structural, morphological, Raman, optical, magnetic, and antibacterial characteristics of CeO2 nanostructures

In this study, CeO2 nanostructures were synthesized by a soft chemical method. A hydrothermal treatment was observed to lead to an interesting morphological transformation of the nanoparticles into homogeneous microspheres composed of nanosheets with an average thickness of 40 nm. Structural analysis revealed the formation of a single-phase cubic fluorite structure of CeO2 for both samples. A Raman spectroscopic study confirmed the XRD results and furthermore indicated the presence of a large number of oxygen vacancies in the nanosheets. These oxygen vacancies led to room-temperature ferromagnetism (RTFM) of the CeO2 nanosheets with enhanced magnetic characteristics. Amazingly, the nanosheets exhibited substantially greater antibacterial activity than the nanoparticles. This greater antibacterial activity was attributed to greater exposure of high-surface-energy polar surfaces and to the presence of oxygen vacancies.

Multigrid Method for Solution of 3D Helmholtz Equation Based on HOC Schemes

A higher order compact difference (HOC) scheme with uniform mesh sizes in different coordinate directions is employed to discretize a two- and three-dimensional Helmholtz equation. In case of two dimension, the stencil is of 9 points while in three-dimensional case, the scheme has 27 points and has fourth- to fifth-order accuracy. Multigrid method using Gauss-Seidel relaxation is designed to solve the resulting sparse linear systems. Numerical experiments were conducted to test the accuracy of the sixth-order compact difference scheme with Multigrid method and to compare it with the standard second-order finite-difference scheme and fourth-order compact difference scheme. Performance of the scheme is tested through numerical examples. Accuracy and efficiency of the new scheme are established by using the errors norms .

On segmentation of images having multi-regions using Gaussian type radial basis kernel in fuzzy sets framework

Abstract Segmentation of images having multi-objects with intensity inhomogeneity and noise is always challenging. In this paper, we propose a new model for segmentation of images having multi-objects with varying intensity. In the proposed model we develop a novel kernel metric which is based on generalized averages. To ensure its applicability in noisy images we use Gaussian type radial basis kernel. To speed up the convergence and to get global optima of the proposed model, we express energy functional of our model in fuzzy Pseudo level set formulation. The proposed model works well in images having multi-objects with intensity inhomogeneity and noise. Our proposed model also works very well in images having maximum, minimum or average intensity background. Instead of length term we use Gaussian smoothing for regularization of Pseudo level set (fuzzy membership function). Experimental results show better performance of the proposed model over existing state of the art models qualitatively and quantitatively (Jaccard similarity).