In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module ‘Matminer’, 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the ‘Fe’ compositional ratio. The feature importance method provided by ‘scikit-learn’ was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

The Republic of Korea (ROK), as a member state of the IAEA, is operating the State’s System of Accounting for and Control (SSAC) and conducting independent national inspections. Furthermore, an evaluation methodology for the material unaccounted for (MUF) is being developed in ROK to enhance capabilities of national inspection. Generally, physical and chemical changes of nuclear material are unavoidable due to the operating system and structure of facilities, an accumulation of material unaccounted for (MUF) has been issued. IAEA developed statistical MUF evaluation method that can be applied to all facilities around the world and it mainly focuses on the diversion detection of nuclear materials in facilities. However, in terms of the national safeguard inspection, an evaluation of accountancy in facilities is additionally needed. Therefore, in this research, a new approach to MUF evaluation is suggested, based on the Guide to the Expression of Uncertainty in Measurement (GUM) that an evaluation of measurement uncertainty factors is straightforward. A hypothetical list of inventory items (LII) which has 6,118 items at the beginning and end of the material balance period, along with 360 inflow and outflow nuclear material items at a virtual fuel fabrication plant was employed for both the conventional IAEA MUF evaluation method and the proposed GUM-based method. To calculate the measurement uncertainty, it was assumed that an electronic balance, gravimetry, and a thermal ionization mass spectrometer were used for a measurement of the mass, concentration, and enrichment of 235U, respectively. Additionally, it was considered that independent and correlated uncertainty factors were defined as random factors and systematic factors for the ease of uncertainty propagation by the GUM. The total MUF uncertainties of IAEA (σMUF) and GUM (uMUF) method were 37.951 and 36.692 kg, respectively, under the aforementioned assumptions. The difference is low, it was demonstrated that the GUM method is applicable to the MUF evaluation. The IAEA method demonstrated its applicability to all nuclear facilities, but its calculated errors exhibited low traceability due to its simplification. In contrast, the calculated uncertainty based on the GUM method exhibited high reliability and traceability, as it allows for individual management of measurement uncertainty based on the facility’s accounting information. Consequently, the application of the GUM approach could offer more benefits than the conventional IAEA method in cases of national safeguard inspections where factor analysis is required for MUF assessment.

In this study, based on the saturation magnetic flux density experimental values (Bs) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict Bs using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R2 score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict Bs above the threshold value of Bs (1.4 T). After obtaining two ANN models optimized for the R2 and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

Although the Ti–6Al–4V alloy has been used in the aircraft industry owing to its excellent mechanical properties and low density, the low formability of the alloy hinders broadening its applications. Recently, laser-powder bed fusion (L-PBF) has become a novel process for overcoming the limitations of the alloy (i.e., low formability), owing to the high degree of design freedom for the geometry of products having outstanding performance used in hightech applications. In this study, to investigate the effect of bulk shape on the microstructure and mechanical properties of L-PBFed Ti-6Al-4V alloys, two types of samples are fabricated using L-PBF: thick and thin samples. The thick sample exhibits lower strength and higher ductility than the thin sample owing to the larger grain size and lower residual dislocation density of the thick sample because of the heat input during the L-PBF process.

In this study, an Al82Ni7Co3Y8 (at%) bulk metallic glass is fabricated using gas-atomized Al82Ni7Co3Y8 metallic glass powder and subsequent spark plasma sintering (SPS). The effect of powder size on the consolidation of bulk metallic glass is considered by dividing it into 5 m or less and 20–45 m. The sintered Al82Ni7Co3Y8 bulk metallic glasses exhibit crystallization behavior and crystallization enthalpy similar to those of the Al82Ni7Co3Y8 powder with 5 m or less and it is confirmed that no crystallization occurred during the sintering process. From these results, we conclude that the Z-position-controlled spark plasma sintering process, using superplastic deformation by viscous flow in the supercooled liquid-phase region of amorphous powder, is an effective process for manufacturing bulk metallic glass.

The Republic of Korea is implementing safeguards for domestic nuclear facilities through cooperation with the IAEA. But it is not to evaluate the material balance for the material unaccounted for, MUF in the bulk handling facility. Although the development of a material balance evaluation program is underway, there are no related regulations. The State Regulatory Authority, SRA is performing material balance evaluation, MBE on the facility based on the design information and material balance results of the facility. However, it is not possible to directly derive measurement uncertainty for the facility’s measurement equipment, which is an important variable of MBE. To solve this problem, it is trying to derive a method suitable for the domestic environment by investigating the some measurement uncertainty estimation methods and analyzing characteristics of them. In this study, the traditional measurement uncertainty estimation method, GUM method and GUM-S1 method were studied and the advantages and disadvantages were analyzed. Due to the problems mentioned above, the uncertainty quantification technique currently being used cannot be applied to the evaluation of the domestic material balance. Therefore, we are tying to apply them to the evaluation the domestic material balance through the above three methods or a combination of them appropriately. Through this continuing study, it is expected that it will be possible to present a plan to derive measurement uncertainty optimized for the domestic MBE environment.

Double-layer capacitors (DLCs) are developed with high surface electrodes to achieve a high capacitance value. In the present work, the initial bulk concentration of 1 mol/m3 and 3 mol /m3 are selected to show the consequential effects on the performance of a double-layer capacitor. A 1D model of COMSOL Multiphysics has been developed to analyze the electric field and potential in cell voltage, the electric displacement field and polarization induced by the field, and energy density in a double-layer structure. The electrostatics and the electric circuit modes in COMSOL are used to simulate the electrochemical processes in the double-layer structure. The analytical analysis of a double-layer capacitor with different initial bulk concentrations is investigated by using Poisson-Nernst-Plank equations. From the simulation results, the differential capacitance changes as a function of compact layer thickness and initial bulk concentration. The energy density varies with the differential capacitance and voltage window. The values of energy density are dominated by the interaction of ions in the solution and electrode surface.

Since 2017, the Korea Institute of Nuclear Nonproliferation and Control (KINAC) has been implementing State System of Accounting for and Control of Nuclear Materials (SSAC) training courses for the nuclear Newcomer States. This IAEA SSAC course for Newcomer States aims to provide overall concepts and techniques, particularly on nuclear material accountancy and control systems, and address future challenges with regard to developing new nuclear power plants. Due to the restricted travels and limited in-person access to training and facilities from the COVID-19 pandemic, however, a new software was developed to substitute a technical tour on bulk handling facility (BHF) of the training course, and the course was favorably shifted to online in 2021. This newly built training software allows participants to follow each step of the technical process at a virtual bulk handing facility, and provides a video tour for such conditions where the software is found difficult to operate. Another feature of the development is a security function that prevents access of unauthorized users to the software. The achievement is expected to strengthen the SSAC of Newcomer States and ensure the practical implementation of safeguards from the initial stage of their novel nuclear power program through cooperation with IAEA. This contribution of the Republic of Korea (ROK) as one of the leading countries in the field of nuclear nonproliferation will further extend the partnership between IAEA and ROK and promote cooperation with the Newcomer States.

Artificial graphites have been used in various applications, for example, as anode materials for Li-ion batteries, C/C composites, and electrodes for aluminum smelting, due to their unique mechanical strength and high thermal and electrical conductivity. Artificial graphites can be manufactured by a series of kneading, molding, carbonization and graphitization processes with an additional impregnation process. In this study, the influence of the process variables in the kneading and carbonization/graphitization process on the properties of the resulting carbon block was systemically investigated. During the kneading process, the optimum kneading temperature was 90 °C higher than the softening point of the binder pitch; thus, the binder pitch reached its maximum fluidity. On the other hand, during the carbonization and graphitization process, the structural properties of carbon blocks prepared at different heat treatment temperatures were examined and their structural change and evolution were closely described according to the temperature and divided into low-temperature carbonization and high-temperature carbonization/graphitization. Based on this study, we expect to provide a better understanding of setting the parameters for thermally conductive carbon block manufacturing.

The high level of lithium storage in synthetic porous carbons has necessitated the development of accurate models for estimating the specific capacity of carbon-based lithium-ion battery (LIB) anodes. To date, various models have been developed to estimate the storage capacity of lithium in carbonaceous materials. However, these models are complex and do not take into account the effect of porosity in their estimations. In this paper, a novel model is proposed to predict the specific capacity of porous carbon LIB anodes. For this purpose, a new factor is introduced, which is called normalized surface area. Considering this factor, the contribution of surface lithium storage can be added to the lithium stored in the bulk to have a better prediction. The novel model proposed in this study is able to estimate the lithium storage capacity of LIB anodes based on the porosity of porous carbons for the first time. Benefiting porosity value (specific surface area) makes the predictions quick, facile, and sensible for the scientists and experts designing LIBs using porous carbon anodes. The predicted capacities were compared with that of the literature reported by experimental works. The remarkable consistency of the measured and predicted capacities of the LIB anodes also confirms the validity of the approach and its reliability for further predictions.

Bulk graphite is manufactured using graphite scrap as the filler and phenolic resin as the binder. Graphite scrap, which is the by-product of processing the final graphite product, is pulverized and sieved by particle size. The relationship between the density and porosity is analyzed by measuring the mechanical properties of bulk graphite. The filler materials are sieved into mean particle sizes of 10.62, 23.38, 54.09, 84.29, and 126.64 μm. The bulk graphite density using the filler powder with a particle size of 54.09 μm is 1.38 g/cm3, which is the highest value in this study. The compressive strength tends to increase as the bulk graphite density increases. The highest compressive strength of 43.14 MPa is achieved with the 54.09 μm powder. The highest flexural strength of 23.08 MPa is achieved using the 10.62 μm powder, having the smallest average particle size. The compressive strength is affected by the density of bulk graphite, and the flexural strength is affected by the filler particle size of bulk graphite.