The primary objective of this study is to evaluate a systematic design’s effectivity in remediating actual uranium-contaminated soil. The emphasis was placed on practical and engineering aspects, particularly in assessing the capabilities of a zero liquid discharge system in treating wastewater derived from soil washing. The research method involved a purification procedure for both the uranium-contaminated soil and its accompanying wastewater. Notably, the experimental outcomes demonstrated successful uranium separation from the contaminated soil. The treated soil could be self-disposed of, as its uranium concentration fell below 1.0 Bq·g−1, a level endorsed by the International Atomic Energy Agency for radionuclide clearance. The zero liquid discharge system’s significance lay in its distillation process, which not only facilitated the reuse of water from the separated filtrate but also allowed for the self-disposal of high-purity Na2SO4 within the residues of the distilled filtrate. Through a comparative economic analysis involving direct disposal and the application of a remediation process for uranium-contaminated soil, the comprehensive zero liquid discharge system emerged as a practical and viable choice. The successful demonstration of the design and practicality of the proposed zero liquid discharge system for treating wastewater originating from real uranium-contaminated soil is poised to have a lasting impact.
Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.
Nuclear power plants, like other national critical infrastructures, could be under the threat of terrorism or other malicious action. Thus, a nuclear power plant has a robust security system that includes security guards, sensors, barriers, access control systems, lights, and alarm stations with security procedures. However, an effective security system is hard to design because a chain is only as strong as its weakest link, and there could be a vulnerable hole even in the robust security system. Thus, an effective security system requires the evaluation of all possible scenarios. Evaluation software for security system effectiveness assists in systematically assessing all the possible attack scenarios. Many countries developed security effectiveness evaluation software. The first software was developed by the U.S. Sandia National Laboratories in the 1980s. Now there are several commercially available software packages with a function to simulate limited-scope combat between security guards and attacking enemies. However, academic communication is comparatively weak because it may contain sensitive information on the vulnerability of nuclear power plants. We developed original software called Tools for Evaluating Security Systems (TESS) to identify the most vulnerable path to the designated target and model the security systems of all South Korean nuclear power plants. We also used commercial security effectiveness evaluation software, AVERT, to model the same nuclear power plants. TESS was developed to verify the results of commercial security effectiveness evaluation software for the purpose of regulatory use. For the feasibility test, we compared the results of two software with those of force-on-force (FoF) exercises in nuclear power plants. According to the relevant Act, every nuclear power plant site should perform the FoF exercises every year. KINAC was in charge of evaluating the FoF exercise and used several of its results for the study. In the results, even in some differences in detail, the two software and FoF exercises showed qualitative similarity. Conclusively, evaluation software is a useful tool to design and/or assess the security systems of nuclear power plants. We modeled the security systems of all South Korean nuclear power plants, and compared the developed software, a commercial software and FoF exercises. The results showed qualitative similarity. We provided the results of evaluation to nuclear operators for the better security of nuclear power plants.
Lysophosphatidic acid (LPA) is a bioactive lipid messenger involved in the pathogenesis of chronic inflammation and various diseases. Recent studies have shown an association between periodontitis and neuroinflammatory diseases such as Alzheimer’s disease, stroke, and multiple sclerosis. However, the mechanistic relationship between periodontitis and neuroinflammatory diseases remains unclear. The current study found that lysophosphatidic acid receptors 1 (LPAR1) and 6 (LPAR6) exhibited increased expression in primary microglia and astrocytes. The primary astrocytes were then treated using medium conditioned to mimic periodontitis through addition of Porphyromonas gingivalis lipopolysaccharides, and an increased nitric oxide (NO) production was observed. Application of conditioned medium from human periodontal ligament stem cells with or without LPAR1 knockdown showed a decrease in the production of NO and expression of inducible nitric oxide synthase and interleukin 1 beta. These findings may contribute to our understanding of the mechanistic link between periodontitis and neuroinflammatory diseases.