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We prepared a catalyst-coated wire-mesh filter by thermal plasma spray method in order to develop a new type of carbon soot particulate matter (PM) removal component. The halftone plate of a SUS316 mesh was newly used as a substrate for a PM oxidation catalyst. A thermal plasma-spray method was applied to form a Ni-Pd alloy catalytic layer on the filter. As for the performace of Ni-Pd supported filter, the PM oxidation started from approximately 420°C, and the combustion at the catalytic surface took place at around 500-540°C, and finally a noncatalytic oxidation proceeded at around 650°C. The catalyst lowered a peak temperature around 110-220°C than that of only PM. Furthermore, the catalytic performance was maintained after a 5 times-repeated PM oxidation test. By the characterization of the catalyst layer, it was found that Pd particles were dispersed in a matrix of Ni and segregated on the surface and upper part of the plasma-sprayed film on the wire. The surface morphology did not change after the repeated cycle test. In this study, we found the availability of the plasma-spray catalyzed mesh filter was fabricated and as an environmental material with PM removal catalytic function. [ABSTRACT FROM AUTHOR]

A method for evaluating the plasma motion on the solar surface is developed by a combination of numerical simulation and machine learning. On the solar surface, we can observe the thermal convection of plasma fluid. Since thermal convection relates to several phenomena such as magnetic field generation and coronal heating, the estimation of convection velocity is essential. While we can evaluate the line-of-sight velocity with the Doppler effect, the horizontal velocity field cannot be directly obtained. On the other hand, magnetohydrodynamic numerical simulations have been developed and can reproduce solar convection. We can get precise quantities such as velocities and magnetic fields on the solar surface in the simulations. The horizontal velocity, which is challenging to observe, is also obtained in the numerical simulations. Here, we construct a neural network to evaluate the horizontal velocity field on the solar surface with the observable quantities such as radiation intensity and the corresponding horizontal velocity field obtained from the simulation and applying them to the observation. The correlation coefficient between the horizontal velocities obtained by the neural network and the simulated data is 0.84 only with radiative intensity and 0.90 with radiative intensity, the line-of-sight velocity, and the line-of-sight-magnetic field. Even when we apply the network to the observation data, the correlation coefficient of 0.6 is kept. [ABSTRACT FROM AUTHOR]