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https://www.selleckchem.com/products/KU-60019.html Melt spun ribbons of Mn53Al45C2 and Mn52Al46C2 have been synthesized by rapid quenching of the melt with the purpose of monitoring the ε-τ phase transformation to show technologically feasible ways to increase magnetic parameters and to illustrate the viability of these alloys as the next generation of rare earth (RE)-free magnets. By differential scanning calorimetry (DSC), activation energies and temperatures of onset of the ε-τ phase transformation were obtained. Structural analysis was performed using X-ray diffraction (XRD) and the resulting XRD patterns were quantitatively assessed using full profile Rietveld-type analysis. Appropriate annealing was performed in order to enable the ε-τ phase transformation. While hcp ε-phase was found to be predominant in the as-cast samples, after appropriate annealing, the tetragonal τ-phase, the one that furnishes the relevant magnetic response, was found to be predominant with an abundance of about 90%. The data suggested a mechanism of hcp ε-phase decomposition controlled by the segregation towards the interfacial regions, having the rate of transformation governed by antiphase boundary diffusion processes. Magnetic measurements of annealed sample Mn53Al45C2, consisting of predominant tetragonal τ-phase, showed high values of magnetization and increased coercivity, consistent with an energy product of about 10 MGOe, similar with previously reported magnetization measurements, providing further insight into the realization of future class of RE-free low-cost permanent magnets.Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parame

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