For the sake of future generations, the management of radioactive waste is essential. The disposal of spent nuclear fuel (SNF) is considered an urgent challenge to ensure human safety by storing it until its radioactivity drops to a negligible level. Evaluating the safety of disposal facilities is crucial to guarantee their durability for more than 100,000 years, a period sufficient for SNF radioactivity to become ignored. Past studies have proposed various parameters for forecasting the safety of SNF disposal. Among these, radiochemistry and electrochemistry play pivotal roles in predicting the corrosion-related chemical reactions occurring within the SNF and the structural materials of disposal facilities. Our study considers an extreme scenario where the SNF canister becomes compromised, allowing underground water to infiltrate and contact the SNF. We aim to improve the corrosion mechanism and mass-balance equation compared with what Shoesmith et al. proved under the same circumstances. To enhance the comprehensibility of the chemical reactions occurring within the breached SNF canister, we have organized these reactions into eight categories: mass diffusion, alpha radiolysis, adsorption, hydrate formation, solidification, decomposition, ionization, and oxidation. After categorization, we define how each species interacts with others and calculate the rate of change in species’ concentrations resulting from these reactions. By summing up the concentration change rates of each species due to these reactions, we redefine the mass-balance equations for each species. These newly categorized equations, which have not been explained in detail previously, offer a detailed description of corrosion reactions. This comprehensive understanding allows us to evaluate the safety implications of a compromised SNF canister and the associated disposal facilities by numerically solving the mass-balance equations.

The sorption/adsorption behavior of radionuclides, usually occurring at the solid-water interface, is considered to be one of the primary reactions that can hinder the migration of radiotoxic elements contained in the spent nuclear fuel. In general, various physicochemical properties such as surface area, cation exchange capacity, type of radionuclides, solid-to-liquid ratio, aqueous concentration, etc. are known to provide a significant influence on the sorption/adsorption characteristics of target radionuclides onto the mineral surfaces. Therefore, the distribution coefficient, Kd, inherently shows a conditiondependent behavior according to those highly complicated chemical reactions at the solid-water interfaces. Even though a comprehensive understanding of the sorption behavior of radionuclides is significantly required for reliable safety assessment modeling, the number of the chemical thermodynamic model that can precisely predict the sorption/adsorption behavior of radionuclides is very limited. The machine-learning based approaches such as random forest, artificial neural networks, etc. provide an alternative way to understand and estimate complicated chemical reactions under arbitrarily given conditions. In this respect, the objective of this study is to predict the sorption characteristics of various radionuclides onto major bentonite minerals, as backfill materials for the HLW repository, in terms of the distribution coefficient by using a machine-learning based computational approach. As a background dataset, the sorption database previously established by the JAEA was employed for random forest machine learning calculation. Moreover, the hyperparameters such as the number of decision trees, the number of variables to divide each node, and random seed numbers were controlled to assess the coefficient of determination, R2, and the final calculation result. The result obtained in this study indicates that the distribution coefficients of various radionuclides onto bentonite minerals can be reliably predicted by using the machine learning model and sorption database.

This study proposes a method of separating uranium (U) and minor actinides from rare earth (RE) elements in the LiCl-KCl salt system. Several RE metals were used to reduce UCl3 and MgCl2 from the eutectic LiCl-KCl salt systems. Five experiments were performed on drawdown U and plutonium (Pu) surrogate elements from RECl3-enriched LiCl-KCl salt systems at 773 K. Via the introduction of RE metals into the salt system, it was observed that the UCl3 concentration can be lowered below 100 ppm. In addition, UCl3 was reduced into a powdery form that easily settled at the bottom and was successfully collected by a salt distillation operation. When the RE metals come into contact with a metallic structure, a galvanic interaction occurs dominantly, seemingly accelerating the U recovery reaction. These results elucidate the development of an effective and simple process that selectively removes actinides from electrorefining salt, thus contributing to the minimization of the influx of actinides into the nuclear fuel waste stream.

딸기 48개 품종메 대하여 반엽병균 2개 균주 를 폿트시험과 하우스 내의 포장시험으로 인공접종에 의하여 품종별, 균주별의 저항성을 검토하고 약제방제효과에 관한 시험을 실시하였다. 1. 딸기 품종간이나 균주간의 감수성의 차이는 높은 유의성을 나타내었다. 2. 균주가 균주에 비하여 대부분의 품종에 대하여 병원성이 높았으며 균주에 따라 품종에 대한 반응에 차가 심하였다. 3. 균주에 대하여 가장 감수성인 품종은 Tioga, Donner, Marshall, Northwest, Red star, Senga sengana, Shasta, Torrey, Hokowase, Daehak No. 1등이고 가장 저항성인 품종은 America, Dabreak, Takanae, Kurumae No. 103, Horida's wander, Benizuru, Hukuba, Himiko등이었다. 4. 균주에 대하여 가장 감수성인 품종은 Marshall. Tioga 등이고 가장 저항성인 품종은 America, Morioka No. 17, Takanae, Kurumae No. 13, Horida's wander, Benizuru, Hukuba등이었다. 5. 딸기 잎의 노유에 따른 균주에 대한 감수성은 노엽일수록 저항성이었다. 6. 공시약제중 가장 효과적인 방제악제는 Captan, Zineb, Difolatan등이었다.