Magnetic Properties of Textured Nanocrystalline Mn-Zn Ferrite Thin Films Fabricated by Pulsed Laser Deposition.

MnxZn1-xFe2O4 nanoparticles were chemically synthesized by co- precipitating metal ions in alkaline aqueous solutions. The XRD peaks match up to spinel ferrites without any multi phase indication and clear visibility of ferrite FT-IR absorption bands confirm single phase spinal formation. Particle size derived from XRD data is authenticated by TEM micrographs. Thin films fabricated from this material on quartz substrate by pulse laser deposition were characterised using XRD. The XRD data revealed formation of spinel structure with a reasonable degree of texture. AFM analysis confirms nano granular film morphology with dimensions comparable to that of target grain. Magnetic data obtained from textured nanocrystalline Mn-Zn ferrite thin film measurements made known enhanced coercivity. The observed enhanced coercivity is explained with due consideration of film texture and surface disorder that originated from Mn concentration specific initial adsorption prior to nucleation, resulting in directional film growth

Genotoxic effect of manganese and nickel doped zinc ferrite (Mn0.3Ni0.3Zn0.4Fe2O4) nanoparticle in Swiss albino mouse Mus musculus

Manganese and Nickel doped Zinc Ferrite (MNZF) nanoparticle Mn0.3Ni0.3Zn0.4Fe2O4 is used in fabrication of room temperature (25-30ºC) NH3 gas sensor in large scale industries. However, there are no studies available on its toxic effects. Hence, in the present study, we assessed the genotoxic effect of various doses (125, 250 and 500 mg/kg) of the MNZF nanoparticle (NP) in Swiss albino mice Mus musculus employing the chromosomal aberration test, micronucleus test and single cell gel electrophoresis assay (comet assay). The NP was orally gavaged for 15 consecutive days. Dose-dependent study was conducted at 24 h after the last dose of gavage and time-dependent response was studied for 250 mg/kg at 24, 48 and 72 h of treatment. All the parameters employed showed a statistically significant dose-dependent increase of genetic damage indicating the genotoxic effect of this NP in Swiss albino mice. Proper precautions should be undertaken on handling this NP to avoid contact with it either through respiration or ingestion

Genotoxic effect of manganese and nickel doped zinc ferrite (Mn0.3Ni0.3Zn0.4Fe2O4) nanoparticle in Swiss albino mouse Mus musculus

25-32Manganese and Nickel doped Zinc Ferrite (MNZF) nanoparticle Mn0.3Ni0.3Zn0.4Fe2O4 is used in fabrication of room temperature (25-30ºC) NH3 gas sensor in large scale industries. However, there are no studies available on its toxic effects. Hence, in the present study, we assessed the genotoxic effect of various doses (125, 250 and 500 mg/kg) of the MNZF nanoparticle (NP) in Swiss albino mice Mus musculus employing the chromosomal aberration test, micronucleus test and single cell gel electrophoresis assay (comet assay). The NP was orally gavaged for 15 consecutive days. Dose-dependent study was conducted at 24 h after the last dose of gavage and time-dependent response was studied for 250 mg/kg at 24, 48 and 72 h of treatment. All the parameters employed showed a statistically significant dose-dependent increase of genetic damage indicating the genotoxic effect of this NP in Swiss albino mice. Proper precautions should be undertaken on handling this NP to avoid contact with it either through respiration or ingestion

Aerodynamic Flow Field Prediction across Geometric and Physical-Fluidic Variations using Data-Driven and Physics Informed Deep Learning Models

A study to analyze the efficacy of two novel, state-of-the-art deep learning methods used in flow-field prediction is performed. In the analysis of each method, the emphasis is given to the retainment of physical flow field features, generalizability, and computational costs, which are critical for the acceptance of such methods as tools for design space exploration in aerodynamic vehicles/components that involves rigorous analysis of fluid flows. A representative problem of prediction of flow-field over airfoils is chosen for this study. A traditional Computational Fluid Dynamics (CFD) approach involves solving for several datapoints and then interpolating between them using simpler techniques like a linear interpolation. This approach is computationally expensive as one often needs to create a dense database of CFD simulations to produce successful predictions and interpolations, with each simulation taking days of computational time. Using a neural network (NN), which is a non-linear function approximator, it is possible to offset this need to produce a dense dataset. To analyze this potential, we first tackle the problem in a data-driven approach by training the NN on CFD data. This approach is appealing as it has the potential to leverage the wide variety of available data in the community and built a model to aid the interpolation process. For a simpler case, we also show that using such a technique it is possible to reduce the size of the database the model is trained on. Such information is vital from the perspective of future database generation as it allows engineers to wisely sample the design space for generating the actual CFD simulations. In the second approach, we take up a relatively new methodology where a NN can be used for generating the forward simulations itself, replacing the CFD solver dependency. Such data-less, physics-informed neural networks (PINNs) are then parameterized by passing additional inputs to the layers, enabling the solution to a larger design space instead of a single simulation. Finally, we make important conclusions and recommendations on scenarios where such methods are found to be most useful and discuss possible challenges when using these methods as a design tool

Investigation of MHD micropolar flow between a stationary and a rotating disc: Keller-box solution

AbstractIn the present investigation, magnetohydrodynamic (MHD) micropolar fluid flow between a stationary disc and a rotating disc with a constant angular velocity is considered. The study investigates the effect of magnetic field and microrotation structure on the flow characteristics. The governing equations of motion are transformed to a system of nonlinear ordinary differential equations (ODEs) in dimensionless form using Von Karman’s similarity transformations. An algorithm based on implicit finite difference method-Keller-box Scheme is employed to solve the resulting similarity equations for various pertinent parameters. Numerical solutions of velocity profiles, pressure gradient and microrotation profiles are discussed, and presented through tables and graphs for various Magnetic parameter. Comparisons are made between the obtained results and previously reported findings in the literature. The successful validation against existing literature supports the effectiveness of the methodology employed in this investigation