Inductively coupled plasma mass spectrometry using isotope dilution (ID-ICP-MS) with liquid-liquid extraction was used for determining ultra-trace tellurium (Te ) in steels to improve the sensitivity, accuracy, and precision of the analysis. Single quadrupole-type ICP-MS cannot be used to determine the trace amount of Te because of contribution of mass spectrometric interference by Xe. To overcome this, tandem mass-filter-type ICP-MS (ICP-MS/MS) was used for the determination of Te. Pretreatment by liquid-liquid extraction was also employed to compensate for the decrease in signal intensity due to the use of ICP-MS/MS. The relative standard deviations of the resulting abundances defined by repeated analysis from separated three steel samples were <1.3%. Furthermore, the determined values of Te in standard reference materials were close to the reference values. Thus, the developed determination method is useful for the analysis of ultra-trace Te in steels.

SYNOPSIS: In recent years, remarkable advances have been made in statistical analyses based on deep learning techniques. Applied studies of deep learning have been reported in various industrial fields, and those of the iron and steel industry are no exception. The production of iron and steel requires a variety of processes, such as processing of ingredients, iron making, casting, and rolling.As a result, the data acquired from them are diverse, and various tasks exist for which deep learning algorithms can assist. Hence, providing a summary of the application is helpful not only for researchers specializing in information science to grasp the current trend of applied studies on deep learning techniques, but also for researchers specializing in each field of the iron and steel making industry to understand what types of deep learning techniques are being utilized in other specialized fields. Therefore, in this paper, we summarize current studies on the application of deep learning in the iron and steel making field by organizing them into several categories of processes and analytical methodologies. Furthermore, based on the results, we discuss future perspectives on the development of deep learning techniques in this field.