Electrochemical oxidation and reduction reactions are fundamental in various conversion and energy storage devices. Functional materials derived from MOFs have been considered promising as electrical catalysts for ORR, HER, and OER, which can be used in Zinc-air batteries and water electrolysis. Herein, we designed a novel approach to fabricating the ultrafine Co9S8 embedded nitrogen-doped hollow carbon nanocages ( Co9S8@N-HC). The method involved a process of sulfidation of cobalt-based metal–organic frameworks (ZIF67) and then coating them with polypyrrole (PPy). PPy has notable properties such as high electrical conductivity and abundant nitrogen content, rendering it highly promising for catalytic applications. The Co9S8@ N-HC catalyst was successfully synthesized via the carbonization of CoSx@ PPy. Remarkably, the Co9S8@ N-HC catalyst demonstrated exceptional electrocatalytic activity, requiring only low overpotentials of 285 mV and 201 mV at 10 mA cm‒ 2 for OER and HER, respectively, and exhibited high activity for ORR, with an onset potential ( Eonset) of 0.923 V and half-wave potential ( E1/2) of 0.879 V in alkaline media. The electrocatalytic efficiency displayed by Co9S8@ N-HC opens a new line of research on the synergistic effect of MOF-PPy materials on energy storage and conversion.

Natural uranium-contaminated soil in Korea Atomic Energy Research Institute (KAERI) was generated by decommissioning of the natural uranium conversion facility in 2010. Some of the contaminated soil was expected to be clearance level, however the disposal cost burden is increasing because it is not classified in advance. In this study, pre-classification method is presented according to the ratio of naturally occurring radioactive material (NORM) and contaminated uranium in the soil. To verify the validity of the method, the verification of the uranium radioactivity concentration estimation method through γ-ray analysis results corrected by self-absorption using MCNP6.2, and the validity of the pre-classification method according to the net peak area ratio were evaluated. Estimating concentration for 238U and 235U with γ-ray analysis using HPGe (GC3018) and MCNP6.2 was verified by 􀟙-spectrometry. The analysis results of different methods were within the deviation range. Clearance screening factors (CSFs) were derived through MCNP6.2, and net peak area ratio were calculated at 295.21 keV, 351.92 keV(214Pb), 609.31 keV, 1120.28 keV, 1764.49 keV(214Bi) of to the 92.59 keV. CSFs for contaminated soil and natural soil were compared with U/Pb ratio. CSFs and radioactivity concentrations were measured, and the deviation from the 60 minute measurement results was compared in natural soil. Pre-classification is possible using by CSFs measured for more than 5 minutes to the average concentration of 214Pb or 214Bi in contaminated soil. In this study, the pre-classification method of clearance determination in contaminated soil was evaluated, and it was relatively accurate in a shorter measurement time than the method using the concentrations. This method is expected to be used as a simple pre-classification method through additional research.

Radioactive waste can be classified according to the concentration level for radionuclides, and the disposal method is different through the level. Gamma analysis is inevitably performed to determine the concentration of radioactive waste, and when a large amount of radioactive waste is generated, such as decommissioning nuclear facilities, it takes a lot of time to analyze samples. The performance of a lot of analysis can cause human errors and workload. In general, gamma analysis is performed using by HPGe detector. Recently, for convenience of analysis, commercial automatic sample changers applicable to the HPGe detectors were developed. The automatic sample changers generate individual analysis reports for each sample. In this study, gamma analysis procedure was improved using the application of the automatic sample changer and the automated data parsing using by Python. The application of automatic sample changers and data parsing technique can solve the problems. The human errors were reduced to 0% compared to the previous method by improving the gamma analysis procedure, and working time were also dramatically reduced. This automation of analysis procedure will contribute to reducing the burden of analysis work and reducing human errors through various improvements.