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

A Novel A.I Enhanced Reservoir Characterization with a Combined Mixture of Experts -- NVIDIA Modulus based Physics Informed Neural Operator Forward Model

We have developed an advanced workflow for reservoir characterization, effectively addressing the challenges of reservoir history matching through a novel approach. This method integrates a Physics Informed Neural Operator (PINO) as a forward model within a sophisticated Cluster Classify Regress (CCR) framework. The process is enhanced by an adaptive Regularized Ensemble Kalman Inversion (aREKI), optimized for rapid uncertainty quantification in reservoir history matching. This innovative workflow parameterizes unknown permeability and porosity fields, capturing non-Gaussian posterior measures with techniques such as a variational convolution autoencoder and the CCR. Serving as exotic priors and a supervised model, the CCR synergizes with the PINO surrogate to accurately simulate the nonlinear dynamics of Peaceman well equations. The CCR approach allows for flexibility in applying distinct machine learning algorithms across its stages. Updates to the PINO reservoir surrogate are driven by a loss function derived from supervised data, initial conditions, and residuals of governing black oil PDEs. Our integrated model, termed PINO-Res-Sim, outputs crucial parameters including pressures, saturations, and production rates for oil, water, and gas. Validated against traditional simulators through controlled experiments on synthetic reservoirs and the Norne field, the methodology showed remarkable accuracy. Additionally, the PINO-Res-Sim in the aREKI workflow efficiently recovered unknown fields with a computational speedup of 100 to 6000 times faster than conventional methods. The learning phase for PINO-Res-Sim, conducted on an NVIDIA H100, was impressively efficient, compatible with ensemble-based methods for complex computational tasks.Comment: 55 pages, 46 figure

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

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

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

Resistivity–thermopower correlation derived temperature-dependent transport behaviour of MnxZn1−xFe2O4 nanoparticles

AbstractFerrite material nanoparticles comprised of manganese and zinc were chemically synthesized by the co-precipitation method. The designated ferrite X-ray diffraction peaks and characteristic ferrite absorption bands in Fourier transform infrared absorption spectra confirmed the formation of a spinel structure. Determination of the full width at half maximum values of the X-ray diffraction peaks and the corresponding calculations using the Scherrer formula suggested the generation of nano-grains. Micrographs obtained using a transmission electron microscope confirmed the nano-scale dimensions of the particles. Deviations in the characteristic resistivity and thermopower values in response to ambient sample temperature variations were experimentally observed and used for correlation-derived temperature-dependent transport behaviour analysis. Samples with a concentration x=0.8 and 1.0 showed high thermopower values at reasonably low temperatures with moderate specific resistance