Most of the C-14 produced is in the organic form, generated as methane (14CH4), methanol (14CH3OH), formaldehyde (14CH2O), and formic acid (14CO2H2). When analyzing C-14, it is transformed into the form of 14CO2, and its concentration is determined using LSC. Typical examples include the wet oxidation method, the combustion or Pyrolysis. The wet oxidation method uses strong acids and involves repeated operations, which generates large amounts of acid waste and secondary radioactive waste. The combustion method uses high temperatures, which requires an oxygen device. Pyrolysis also requires high temperature in a vacuum and catalysts. Catalysts are expensive because they are platinum-based. To compensate for these shortcomings, a C-14 analysis method using UV irradiation was developed. In this study, 100 mL of distilled water mixed with formic acid (CO2H2), potassium persulfate (K2S2O8), and silver nitrate (AgNO3) was irradiated with a 320-390 nm UV lamp to conduct a CO2 production reaction experiment. The UV range was measured using a photometer (UV Power puck II). The beaker was made of quartz in 150 mL size with three inlets : a temperature measurement, a sample inlet, and a collection tube connector. We changed the UV lamp used from a 450 W halogen lamp to a 100 W LED, which has a lower temperature and is safer. As a result of the experiment, CO2 bubbles were generated in the collection tube, due to the UV irradiation react, which uses oxidizer and catalysts. The maximum temperature of the solution irradiated with the LED UV lamp was less than 56°C. It confirmed the rate of bubble generation changed depending on the lamp distance, the amount of sample, oxidizer, and catalyst. In an experiment to confirm the reaction caused by heat, it was found that although a reaction occurred due to heat, the reaction was significantly lower than when using a UV lamp. The reproducibility experiment was conducted three times in total under the same conditions. It showed the same pattern. In the future, we plan to select mock samples, collect 14CO2 in Carbo- Sorb, and analyze them using LSC. The results of this research will be used as a technology to recover C-14 more safely and efficiently and will also be used to expand its application to the treatment of other wastes such as waste liquid and waste resin through simulated samples.

To achieve permanent disposal of radioactive waste drums, the radionuclides analysis process is essential. A variety of waste types are generated through the operation of nuclear facilities, with dry active waste (DAW) being the most abundant. To perform radionuclides analysis, sample pretreatment technology is required to transform solid samples into solutions. In this study, we developed a dry ashing-microwave digestion method and secured the reliability of the analysis results through a validity evaluation. Additionally, we conducted a comparative analysis of the radioactivity of 94Nb nuclides with and without the chemical separation process, which reduced the minimum detectable activity (MDA) level by more than 65-fold for a certain sample.

The success of machine learning approach to identify key correlation in large database is critically controlled by the reliability and accuracy of the data. Here, we demonstrate that rigorous material properties of radioactive nuclear fuels can be obtained by integrated approach of first principles calculations and the machine learning approach. The reliable database is established by density functional theory and molecular dynamics simulations, which is the input of the machine learning to analyze any correlation among the database. The outcomes are applied to evaluate thermodynamic, kinetic and electrochemical properties, which plays a key role for safe management of spent nuclear fuels.