Gamma-ray spectroscopy, which is an appropriate method to identify and quantify radionuclides, is widely utilized in radiological leakage monitoring of nuclear facilities, assay of radioactive wastes, and decontamination evaluation of post-processing such as decommissioning and remediation. For example, in the post-processing, it is conducted to verify the radioactivity level of the site before and after the work and decide to recycle or dispose the generated waste. For an accurate evaluation of gamma-ray emitting radionuclides, the measurement should be carried out near the region of interest on site, or a sample analysis should be performed in the laboratory. However, the region is inaccessible due to the safety-critical nature of nuclear facilities, and excessive radiation exposure to workers could be caused. In addition, in the case of subjects that may be contaminated inside such as pipe structures generated during decommissioning, surveying is usually done over the outside of them only, so the effectiveness of the result is limited. Thus, there is a need to develop a radiation measurement system that can be available in narrow space and can sense remotely with excellent performance. A liquid light guide (LLG), unlike typical optical fiber, is a light guide which has a liquid core. It has superior light transmissivity than any optical fiber and can be manufactured with a larger diameter. Additionally, it can deliver light with much greater intensity with very low attenuation along the length because there is no packing fraction and it has very high radiation resistant characteristics. Especially, thanks to the good transmissivity in UV-VIS wavelength, the LLG can well transmit the scintillation light signals from scintillators that have relatively short emission wavelengths, such as LaBr3:Ce and CeBr3. In this study, we developed a radiation sensor system based on a LLG for remote gamma-ray spectroscopy. We fabricated a radiation sensor with LaBr3:Ce scintillator and LLG, and acquired energy spectra of Cs-137 and Co-60 remotely. Furthermore, the results of gamma-ray spectroscopy using different lengths of LLG were compared with those obtained without LLG. Energy resolutions were estimated as 7.67%, 4.90%, and 4.81% at 662, 1,173, and 1,332 keV, respectively for 1 m long LLG, which shows similar values of a general NaI(Tl) scintillator. With 3 m long LLG, the energy resolutions were 7.92%, 5.48%, and 5.07% for 662, 1,173, and 1,332 keV gamma-rays, respectively.

In this study, the positions of Cs-137 gamma ray source are estimated from the plastic scintillating fiber bundle sensor with length of 5 m, using machine learning data analysis. Seven strands of plastic scintillating fibers are bundled by black shrink tube and two photomultiplier tubes are used as a gamma ray sensing and light measuring devices, respectively. The dose rate of Cs-137 used in this study is 6 μSv·h−1. For the machine learning modeling, Keras framework in a Python environment is used. The algorithm chosen to construct machine learning model is regression with 15,000 number of nodes in each hidden layer. The pulse-shaped signals measured by photomultiplier tubes are saved as discrete digits and each pulse data consists of 1,024 number of them. Measurements are conducted separately to create machine learning data used in training and test processes. Measurement times were different for obtaining training and test data which were 1 minute and 5 seconds, respectively. It is because sufficient number of data are needed in case of training data, while the measurement time of test data implies the actual measuring time. The machine learning model is designated to estimate the source positions using the information about time difference of the pulses which are created simultaneously by the interaction of gamma ray and plastic scintillating fiber sensor. To evaluate whether the double-trained machine learning model shows enhancement in accuracy of source position estimation, the reference model is constructed using training data with one-time learning process. The double-trained machine learning model is designed to construct first model and create a second training data using the training error and predetermined coefficient. The second training data are used to construct a final model. Both reference model and double-trained models constructed with different coefficients are evaluated with test data. The evaluation result shows that the average values calculated for all measured position in each model are different from 7.21 to 1.44 cm. As a result, by constructing the double-trained machine learning model, the final accuracy shows 80% of improvement ratio. Further study will be conducted to evaluate whether the double-trained machine learning model is applicable to other data obtained from measurement of gamma ray sources with different energy and set a methodology to find optimal coefficient.

Effect of sulfation processes on the physicochemical properties of ZrO2 and TiO2 nanoparticles were thoroughly investigated. SO4/ZrO2 and SO4/TiO2 catalysts were synthesized to identify the acidity character of each. The wet impregnation method of ZrO2 and TiO2 nanoparticles was employed using H2SO4 with various concentrations of 0.5, 0.75, and 1 M, followed by calcination at 400, 500, and 600 °C to obtain optimum conditions of the catalyst synthesis process. The highest total acidity was found when using 1 M SO4/ZrO2-500 and 1 M SO4/TiO2-500 catalysts, with total acidity values of 2.642 and 6.920 mmol/ g, respectively. Sulfation increases titania particles via agglomeration. In contrast, sulfation did not practically change the size of zirconia particles. The sulfation process causes color of both catalyst particles to brighten due to the presence of sulfate. There was a decrease in surface area and pore volume of catalysts after sulfation; the materials’ mesoporous structural properties were confirmed. The 1 M SO4/ZrO2 and 1 M SO4/TiO2 catalysts calcined at 500 °C are the best candidate heterogeneous acid catalysts synthesized in thus work.

목적 : 중국의 옵토메트리(optomerty) 관련 제도를 조사하고 분석하였으며, 중국 안경사 제도의 새로운 변화 와 발전을 고찰하고자 하였다. 방법 : 중국의 옵토메트리(optometry) 관련 정보를 중국학술정보원(China National Knowledge Infrastructure, 中國知网), 중국안광협회(China Optometric and Optical Association, COOA), 바이두(Baidu), 중국인민공 화국교육부(Ministry of Education of the People's Republic of China) 및 학술연구정보서비스(RISS)를 통해 검색하고 수집하였다. 결과 : 중국의 옵토메트리스트(optometrist)는 안경 험광원(5등급) 및 정배공(4등급)으로 구분되며, 등급별로 업무 범위와 시험제도가 나뉜 것으로 나타났다. 시험내용은 국가인력자원보장부가 규정한 안경 험광원 및 정배공 시험제도를 따르고 있다. 안경 험광원 및 정배공 직업 자격의 규정이 시대적인 변화에 따라 ‘자격평가용’에서 ‘기능 등급인정용’으로 바뀌었으며, 핵심적 변화로 주최기관 또한 정부에서 관련 업계 내의 권위와 공신력을 갖추고 사 회적 인지도가 비교적 높은 민간기관으로 바뀐 것으로 나타났다. 결론 : 중국의 옵토메트리(optometry) 제도는 주최가 정부에서 민간기관으로 변화되었으며, 직종에 따라 업무, 등급 및 시험방법을 명확하게 구분하고 있다. 하지만 통일된 국가시험이 없고, 표준화 과정을 거치지 않아 평가에 일관성이 부족하다. 향후 중국 안경 시장의 큰 수요를 충족시키고 전문성을 강화하기 위해서 현행 제도를 엄격하 게 관리할 필요가 있다고 판단된다.

최근 살진균제는 세계 식량 안보에 없어서는 안될 필수 요소이며, 그 사용량은 증가하고 있다. 살진균제는 직접적 또는 간접적으로 곤충에 영 향을 미쳐 유전자 및 분자 수준의 변화를 일으킨다. 곤충은 다양한 해독 매커니즘을 통해 살진균제를 포함한 농약으로부터 유발되는 활성산소 (ROS) 독성을 제거한다. 본 연구는 살진균제 캡탄의 비치명적 투여량(0.2, 2, and 20 μg/μL)을 주입 후 갈색거저리의 유충에서 해독효소의 mRNA 발현량을 분석했다. 갈색거저리의 전사체 분석을 통해 해독 매커니즘 관련 유전자인 퍼옥시다제(POX), 카탈라제(CAT), 슈퍼옥사이드 디스뮤타제(SOD) 및 글루타티온-S-트랜스퍼라제(GST)를 발굴하였다. 처리 24시간 후 TmPOX5 mRNA가 유의하게 증가한 것으로 나타났다. 처리 3 시간 후 TmSOD4의 mRNA가 유사하게 증가하였다. 또한 2 μg/μL 처리 24시간 후 TmCAT2의 mRNA 가 유의하게 증가하였다. 캡탄 노출 후 TmGST1 및 TmGST3의 mRNA 발현량도 증가하였다. 결론적으로, TmPOX5 및 TmSOD4 유전자는 갈색거저리에서 캡탄 노출에 대한 바이오마커 또는 생체이물 센서로 작용할 수 있음을 시사한다.

Green synthesis of graphene using leaf extracts as sustainable reducing and capping agents is a thrust area of research owing to its simplicity, eco-friendly nature and the ease of access to graphene. Moringa (Moringa oleifera Lam) plant is widely cultivated in India owing to its important medicinal and nutritional values. Inspired by these reports, herein we report a simple, green and economic synthesis of graphene, Moringa leaf extract employed reduced graphene oxide (MRGO) with excellent dye removal efficiencies. The MRGO is prepared by refluxing a mixture of aqueous dispersion of graphene oxide and Moringa leaf extract for 10 h. Further, we elucidated the role of synthesized MRGO in the removal of methylene blue (MB) and rhodamine B (RB) dyes through the sonochemical method. This as-synthesized material exhibited excellent dye removal efficiencies of about 93% and 87% against MB and RB dyes, respectively. Graphene with huge surface area expedited the better adsorption of dye molecules, thus, facilitated the better removal of the latter. Therefore, the superior dye removal efficiencies of MRGO were attributed to its adsorption capacity. This cost-effective synthetic approach of MRGO nanomaterial has a great potential for the innovative applications in water purification technology and find its place in further industrialization.