Measurements of Dendritic Growth Velocities in Undercooled Melts of Pure Nickel Under Static Magnetic Fields

Dendritic growth velocities in undercooled melts of pure Ni have been intensively studied over the past fifty years. However, the literature data are at marked variance with the prediction of the widely accepted model for rapid dendritic growth both at small and at large undercoolings. In the present work, bulk melts of pure Ni samples of high purity were undercooled by glass fluxing treatment under a static magnetic field. The recalescence processes of the samples at different undercoolings were recorded using a high-speed camera, and were modeled using a software to determine the dendritic growth velocities. The present data confirmed the effect of melt flow on dendritic growth velocities at undercoolings below 100 K. A comparison of the present data with previous measurements on a lower purity material suggested an effect of impurities on dendritic growth velocities at undercoolings larger than 200 K as well

Dendritic growth velocities in undercooled melts under static magnetic fields

Dendritic growth in undercooled melts has been an interesting topic for metallurgists, physicists and mathematicians. In recent years, attention has been focused on the effects of melt flow on dendritic growth. Significant thermoelectric currents form in undercooled growth due to the presence of relatively large thermal gradients. Numerical simulations showed that the application of a static magnetic field exerts a complex influence on melt flow due to Lorentz force, damping and thermoelectrically driven convection, affecting growth kinetics in undercooled metallic melts. To verify the simulated results, bulk melts of high purity nickel were undercooled using the glass fluxing technique under static magnetic fields of up to 6 T. A high-speed camera was used for in situ monitoring of the recalescence process of the undercooled samples. The dendritic growth velocities at different melt undercoolings were calculated by simulating the recorded images of the recalescence process. The measured data confirms the predicted effect of heat and mass transport through thermoelectric magnetohydrodynamics flow on dendritic growth

Modeling of tip kinetics of undercooled Ti dendrites with consideration of forced flow and oxygen impurity effects

Tip velocities of undercooled dendrites in electromagnetically levitated melt droplets of pure Ti were modeled using a theory on three-dimensional dendritic growth with fluid flow and a dilute solute. The modeling shows that a forced flow due to electromagnetic stirring depresses tip velocities of the dendrites at low undercoolings and that its effect becomes negligible at high undercoolings. In contrast, an oxygen impurity dissolved in liquid Ti depresses tip velocities of the dendrites over a broad range of undercooling while it coarsens tip radii. Such modeling results allowed for reconciliation of discrepancies in literature data by considering an oxygen impurity effect. The modeling also predicts that the effect of the oxygen impurity becomes insignificant when its concentration is reduced below 50 ppm in atomic fraction

Li-rich and super Li-rich giants produced by element diffusion

Context. About 0.2-2% of giant stars are Li-rich, whose lithium abundance (A(Li)) is higher than 1.5 dex. Among them, near 6% are super Li-rich with A(Li) exceeding 3.2 dex. Meanwhile, the formation mechanism of these Li-rich and super Li-rich giants is still under debate. Aims. Considering the compact He core of red giants, attention is paid to the effect of element diffusion on A(Li). In particular, when the He core flash occurs, the element diffusion makes the thermohaline mixing zone extend inward and connect to the inner convection region of stars. Then, a large amount of 7Be produced by the He flash can be transferred to stellar surface, finally turning into 7Li. Thus, the goal of this work is to propose the mechanism of A(Li) enrichment and achieve the consistency between the theoretical and observation data. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we simulate the evolution of low-mass stars, with considering the effects of element diffusion on the Li abundances. The timescale ratio of Li-rich giants to normal giants is estimated by population synthesis method. Then we get the theoretical value of A(Li) and make a comparison with observations. Results. Considering the influence of element diffusion in the model results in the increase of lithium abundance up to about 1.8dex, which can reveal Li-rich giants. Simultaneously, introducing high constant diffusive mixing coefficients (Dmix) with the values from 10e11 to 10e15in the model allows A(Li) to increase from 2.4 to 4.5dex, which can explain the most of Li-rich and super Li-rich giant stars. The population synthesis method reveals that the amount of Li-rich giants among giants is about 0.2-2%, which is consistent with observation estimated levels

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We propose a l0 sparsity based approach to remove additive white Gaussian noise from a given image. To achieve this goal, we combine the local prior and global prior together to recover the noise-free values of pixels. The local prior depends on the neighborhood relationships of a search window to help maintain edges and smoothness. The global prior is generated from a hierarchical l0 sparse representation to help eliminate the redundant information and preserve the global consistency. In addition, to make the correlations between pixels more meaningful, we adopt Principle Component Analysis to measure the similarities, which can be both propitious to reduce the computational complexity and improve the accuracies. Experiments on the benchmark image set show that the proposed approach can achieve superior performance to the state-of-the-art approaches both in accuracy and perception in removing the zero-mean additive white Gaussian noise

Recent Progress on the Application of Nano-Biosensors for the Detection of Foodborne Pathogenic Bacteria

The contamination of foodborne pathogenic bacteria poses great threats to human health. Conventional foodborne pathogen detection methods suffer from the shortcomings of time-consuming, cumbersome, and non-real time. Hence, it is of great significance to explore a sensitive, safe, simple and economical method for foodborne pathogen detection. Compared with the traditional methods, nano-biosensor-based detection assays possess merits such as exceptional sensitivity, high selectivity, real-time detection, low consumption and low limit of detection. In this article, these nano-biosensor-based methods are summarized, and the principles and advantages of single-, dual- and multi-mode detection are comprehensively discussed. Meanwhile, an outlook on the application of nano-biosensors for the detection of foodborne pathogens is given with the goal of providing a reference for the optimization of the existing detection methods for foodborne pathogenic bacteria