Transition metals, such as chromium (Cr) and manganese (Mn) doped zinc oxide (ZnO) magnetic nanoparticles, were synthesized via sole gel auto-combustion method. The prepared magnetic (Zn 1−(x+y) Mn x Cr y O, where x, y = 0, 0.02, 0.075) nanoparticles were calcined in an oven at 6000°C for 2 hours. The morphologies of the nanoparticles were investigated using different techniques. X-ray diffraction (XRD) analysis revealed that the hexagonal wurtzite structure of the synthesized nanoparticles was unaffected by doping concentration. The crystallite size measured by Scherrer formula was in the range of 32 nm to 38 nm at different doping concentrations. Nanosized particles with well-defined boundaries were observed using a field emission scanning electron microscopy (FE-SEM). Fourier transform infrared (FT-IR) spectra showed a wide absorption band around 1589 cm −1 in all the samples, corresponding to the stretching vibration of zinc and oxygen Zn-O bond. A blue shift in optical band gaps from 3.20 eV for ZnO to 3.08 eV for Zn 0.85 Mn 0.075 Cr 0.075 O nanoparticles was observed in diffuse reflectance spectra, which was attributed to the sp-d exchange interactions. The field-dependent magnetization M-H loops were measured using vibrating sample magnetometer (VSM). The VSM results revealed diamagnetic behavior of the ZnO nanoparticles which changed into ferromagnetic, depending on the doping concentration and particle size. The compositions of Zn, Cr, Mn and O in the prepared samples were confirmed by using the energy dispersive X-ray spectroscopy (EDX). Our results provided an interesting route to improve magnetic properties of ZnO nanoparticles, which may get significant attention for the fabrication of magnetic semiconductors.

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In this paper, we present a variation of Genetic Algorithm (GA) for finding the Optimized shortest path of the network. The algorithm finds the optimal path based on the bandwidth and utilization of the network. The main distinguishing element of this work is the use of "2-point over 1point crossover". The population comprises of all chromosomes (feasible and infeasible). Moreover, it is of variable length, so that the algorithm can perform efficiently in all scenarios. Rankbased selection is used for cross-over operation. Therefore, the best chromosomes crossover and give the most suitable offsprings. If the resulting offsprings are least fitted, they are discarded. Mutation operation is used for maintaining the population diversity. We have also performed various experiments for the population selection. The experiments indicate that random selection method is the most optimum. Hence, the population is selected randomly once the generation is developed. In this paper, we have shown the results using a smaller network; however the work for larger network is in progress.