After the major radioactivation structures (RPV, Core, SG, etc.) due to neutron irradiation from the nuclear fuel in the reactor are permanently shut down, numerous nuclides that emit alpha-rays, beta-rays, gamma-rays, etc. exist within the radioactive structures. In this study, nuclides were selected to evaluate the source term for worker exposure management (external exposure) at the time of decommissioning. The selection of nuclides was derived by sequentially considering the four steps. In the first stage, the classification of isotopes of major nuclides generated from the radiation of fission products, neutron-radiated products, coolant-induced corrosion products, and other impurities was considered as a step to select evaluation nuclides in major primary system structures. As a second step, in order to select the major radionuclides to be considered at the time of decommissioning, it is necessary to select the nuclides considering their half-life. Considering this, nuclides that were less than 5 years after permanent suspension were excluded. As a third step, since the purpose of reducing worker exposure during decommissioning is significant, nuclides that emit gamma rays when decaying were selected. As a final step, it is a material made by radiation from the fuel rod of the reactor and is often a fission product found in the event of a Severe accident at a nuclear power plant, and is excluded from the nuclide for evaluation at the time of decommissioning is excluded. The final selected Co-60 is a nuclide that emits high-energy gamma rays and was classified as a major nuclide that affects the reduction of radiation exposure to decommissioning workers. In the future, based on the nuclide selection results derived from this study, we plan to study the evaluation of worker radiation exposure from crud to decommissioning workers by deriving evaluation results of crud and radioactive source terms within the reactor core.

범밀도이론함수(Density Functional Theory, DFT) 기반의 제일원리전산모사는 기저상태의 DFT 에너지를 구하는데 많은 시간소요 및 전산자원을 소모하였다. 이러한 막대한 전산자원의 소모는 DFT 계산에서 고려할 원자수를 수 백개 이 하로 제한되게 되었으며, 이를 해결하기 위해서는 전자구조 계산이 아닌 원자의 환경 내에 원자간 상호작용을 정의 (Force Field, 힘장)하고 이를 통해 주어진 조성 혹은 구조에 따른 에너지를 빠르게 예측 할 수 있어야 한다. 본 논문에서 는 Behler-Parrinello가 제시한 인공신경망 모델을 활용해 인공지능 다원계 힘장을 개발하고 코발트-구리 산화물의 조성에 따른 에너지를 예측하고 안정한 구조를 탐색하는 연구를 수행하였다. 인공신경망 기술로 부터 구리-코발트 산화물에 대 해 15.7 meV/atom의 에너지 오차와 단위거리당 힘 103.6 meV/Å의 정확도를 가지는 인공신경망 포텐셜을 개발하였다. 이 방법으로 빠르고 정확하게 CuCoO 표면구조의 산소 결함률에 따른 생성에너지를 계산할 수 있었고, 에너지 컨벡스 홀을 도시 조성에 따른 안정한 구조를 예측하였다.

This study aimed to investigate the changes in the γ-aminobutyric acid (GABA) content of bitter melon (Momordica charantia L.) cultivated from different regions, with different harvest times and at various maturation stages. Methods for observing the changes in GABA content were validated by determining the specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), and precision and accuracy using the HPLC-FLD system. Results showed high linearity in the calibration curve with a coefficient of correlation (R2) of 0.9999. The LOD and LOQ values for GABA were 0.29 and 0.87 μg/mL, respectively. The relative standard deviations for intra- and inter-day precision of GABA were less than 5%. The recovery rate of GABA was in the range of 98.77% to 100.50%. The average content of GABA was 0.93 mg/g and Cheongju showed highest GABA content of 1.88 mg/g. As the time of harvest increased from May to September, the GABA content decreased from 1.56 to 0.86 mg/g. Also, maturation of the bitter melon fruit was associated with a decreased in GABA content.