Inorganic nanoparticles have been dispersed in resin blends to prepare many nano-composite resin materials. This hybrid material exhibits the combined properties of inorganic nanoparticles such as mechanical strength, elasticity and thermal stability, and of the processibility and flexibility of organic polymers.

Previous studies reported that the addition of SiO₂ nanoparticles to PVC resin leads to enhanced sound resistance, and adding TiO₂ nanoparticles to PVC resin can enhance its whiteness and prevent UV degradation of resin products. However, there are very few polymer materials with both SiO₂ and TiO₂ nanoparticles.

Addition of SiO₂ and TiO₂ nanoparticles together to the PVC resin will result in its lower degradation of by UV light and better resistance to degradation than adding either of them alone.

For the purpose of using PVC nano-composite as a raw material instead of rubber that is often used as a conveying belt material, Jang Un Hui, a researcher at the Institute of Analysis, prepared new nano-composites by mixing SiO₂ and TiO₂ nanoparticles with PVC resin, and carried out differential scanning calorimetry (DSC) under non-isothermal conditions in order to investigate the crystallization behavior and kinetics of the newly prepared PVC/Nano SiO₂-TiO₂ composites.

The analysis results showed that the crystallization behavior of PVC/Nano SiO₂-TiO₂ composite with 3-5% SiO₂ and TiO₂ nanoparticles is good.

For more information, please refer to her paper “Study on the non-isothermal crystallization kinetics of PVC/NanoSiO₂-TiO₂ composite resin by DSC” in “Journal of Polymer Research” (SCI).

The FFC method has been widely used to prepare metal powders and alloy powders from their oxides in molten salts.

Many researchers have employed several electrochemical techniques to study proper mechanisms and experimental conditions for it.

However, there have been no studies reported on the method of preparing Dy-Fe alloy powders from Dy₂O₃-Fe₂O₃ mixtures in molten CaCl₂ by the FFC method.

Kim Pyong Hun, a section head at the Institute of Analysis, has conducted a study to prepare Dy-Fe alloy powders from Dy₂O₃-Fe₂O₃ mixtures in molten CaCl₂ by the FFC method.

In the Dy₂O₃-Fe₂O₃ mixtures for his study, the ratio of Dy to Fe was 1:2. He used the cyclic voltammetry and constant voltage electrolysis.

Three cathodic current peaks were observed in the cyclic voltammetric curves, which explains that the reduction mechanism of Dy₂O₃–Fe₂O₃ mixtures goes through several steps.

The suitable electrolysis conditions for the preparation of alloy DyFe₂ from Dy₂O₃–Fe₂O₃ mixtures in molten CaCl₂ were 2.8 V, 850 ℃ and 20 h.

You can find more information in his paper “Preparation of Dy-Fe Alloy Powders from Dy₂O₃–Fe₂O₃ Mixtures in Molten CaCl₂ by FFC Method” in “Transactions of the Indian Institute of Metals” (SCI).

Whenever new viruses came into being, mankind has acquired various knowledge including their nature and transmission, and the symptoms and treatment of the diseases, and made vaccines and established treatment tactics.

One of the important issues to cope with infectious diseases is prediction of virus transmission by mathematical modeling. Based on mathematical modeling, it has been widely studied in the world. Mathematical modeling for predicting future epidemics makes it possible to predict the number of medical personnel and beds, medical equipment, medical drug supplies, etc. that are needed to prepare at hospitals. It also helps find a suitable approach for medical service through simulations.

The parameters included in this mathematical model are the key elements that characterize the epidemic of diseases. Therefore, it is important to set the parameters realistically.

Kim Ju Hyong, a researcher at the Faculty of Biomedical Engineering, proposed a method for determining the parameters involved in mathematical models of epidemics using genetic algorithms, and verified its validity.

To test the validity of this method, he built a SEIHRD model and compared the accuracy of the simulation with unknown parameters determined when the underlying data were prepared in various forms.

The results show that the proposed method is a recursive one for simply determining unknown parameters in a mathematical model study considering the spread of epidemics.

For more information, please refer to his paper “A method to determine the unknown parameters of mathematical model of epidemic transmission by genetic algorithm” in “Network Modeling Analysis in Health Informatics and Bioinformatics” (SCI).