In this study, graphene was rapidly grown by chemical vapor deposition using a liquid cell for supplying methanol as a carbon source of graphene. To realize the rapid growth, methanol which is carbon-contained organic solvent was used instead of methane gas, a widely used carbon source for graphene growth. The graphene grown with the growth time as a variable was transferred to a SiO2/ Si substrate with an oxide thickness of 300 nm to confirm whether it was grown with full coverage with an optical microscope. The results confirmed a full coverage in 0.5 min of growth. The Raman spectra also confirmed the G-peak position at 1585.0 cm−1 and an intensity ratio of 2D/G at 2.3 or higher. Concerning electrical transport characteristics, at an induced carrier density of 1 × 1012 cm−2, the hole (μh) and electron (μe) mobilities were 1524 cm2 V−1 s−1 and 1528 cm2 V−1 s−1, respectively. Thus, our study confirmed that high-quality, large-area graphene can be grown within 0.5 min.

Large-scale cultivation of Microcystis aeruginosa in different light conditions was conducted for verifying the cell growth in a greenhouse system. Environmental and chemical parameters of the large-scale culture medium were measured for analyzing the interaction between M. aeruginosa and its symbiotic bacteria. During cultivation, a difference in cell growth pattern was observed between control (natural light) and lightlimited groups (reduction of blue, green, and blue/green light, respectively). Comparing the control group, the light reduced groups showed slow and delayed cell growth through the cultivation period. Also, there is differences in the consuming pattern of total nitrogen and total phosphorus which indicated that the possibility of interaction between M. aeruginosa and symbiotic bacteria.

The carbon anode material for lithium-ion battery was prepared by pyrolysis fuel oil and waste polyethylene terephthalate (PET) additive. The pitch was synthesized as a medium material for carbon anode by heat treatment. The waste PET additive improved the softening point and thermal stability of the pitch. La and Lc of the anode material (heat-treated pitch) increased at higher treatment temperature but decreased by waste PET additive. The electric capacity was evaluated based on effects of defective cavity and developed graphite interlayer, respectively. When the La and Lc of the anode material decreased, the electric capacity by cavity increased based on defective graphite structure. Therefore, the addition of waste PET causes the improved capacity by the cavity. The anode material which has a high efficiency (over 95%) and C-rate (95%, 2 C/0.1 C) was obtained by controlling the process of heat treatment and PET addition. The mechanism of lithium-ion insertion was discussed based on effects of defective cavity and developed graphite interlayer.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

After the permanent shutdown of K1 in 2017, decommissioning processes have attracted great attention. According to the current decommissioning roadmap, the dismantling of the activated components of K1 may start in 2026, following the removal of its spent fuel. Since the reactor vessel (RV) and reactor vessel internal (RVI) of K1 contain massive components and are relatively highly activated, their decommissioning process should be conducted carefully in terms of radiological and industrial safety. For achieving maximum efficiency of nuclear waste management processes for K1, we present activation analysis of the segmentation process and waste classification of the RV and RVI components of K1. For RVI, the active fuel regions and some parts of the upper and lower active regions are classified as intermediate-level waste (ILW), while other components are classified as low-level waste (LLW). Due to the RVI’s complex structure and high activation, we suggest various underwater segmentation techniques which are expected to reduce radiation exposure and generate approximately nine ILW and nineteen very low level waste (VLLW)/LLW packages. For RV, the active fuel region and other components are classified as LLW, VLLW, and clearance waste (CW). In this case, we suggest in-situ remote segmentation in air, which is expected to generate approximately forty-two VLLW/LLW packages.