In this study, we examined the effect of NO addition on the ozone-induced soot oxidation activity of $LaMnO_3$ perovskite catalysts. The addition of 10~20% NO ($NO_2$) with respect to the concentration of ozone effectively enhanced the rate of ozone-induced soot oxidation rate over $LaMnO_3$. However, the excessive addition of NO ($NO_2$) was detrimental to ozone-induced soot oxidation activity. It is supposed nitrogen oxides would adsorb on the catalyst and then react with carbon-oxygen species developed on soot surface, but an excessive addition of nitrogen oxide would inhibit the formation of carbon-oxygen species, which is a key intermediate in the reaction, and consequently suppress the oxidation rate of soot.

Carbon nanotubes (CNT) on metal substrates are definitely beneficial because they can maintain robust mechanical stability and high conductivity between CNT and metal interfaces. Here, we report direct growth of CNT on Ni-based superalloy, Inconel 600, using thermal chemical vapor deposition (CVD) with acetylene feedstock in the growth temperature range of $400-725^{\circ}C$. Furthermore, we studied the effect of substrate pretreatment on the growth yield enhancement and growth temperature decrease of CNT on Inconel 600. Activation energy (AE) for CNT growth was estimated from the CNT height change with respect to the growth temperature. The AE values significantly decreased from 205.03 to 24.35 kJ/mol by the pretreatment of thermal oxidation of Inconel substrate at $725^{\circ}C$ under ambient. Higher oxidation temperature tends to have lower activation energy. The results have shown the importance of pretreatment temperature on CNT growth yield and growth temperature decrease.

This work focused on characterization of the starch degradation activity from extremophile strain Deinococcus geothermalis. Glucoamylase gene from D. geothermalis was cloned and overexpressed by pET-21a vector using E. coli BL21 (DE3). In order to characterize starch degrading activity of recombinant glucoamylase, enzyme was purified using HisPur Ni-NTA column. The recombinant glucoamylase from D. geothermalis exhibited the optimum temperature as $45^{\circ}C$ for starch degradation activity. And highly acido-stable starch degrading activity was shown at pH 2. For further optimization of starch degrading activity with metal ion, various metal ions ($AgCl_2$, $HgCl_2$, $MnSO_4{\cdot}4H_2O$, $CoCl_2{\cdot}6H_2O$, $MgSO_4$, $ZnSO_4{\cdot}7H_2O$, $K_2SO_4$, $FeCl_2{\cdot}4H_2O$, NaCl, or $CuSO_4$) were added for enzyme reaction. As results, it was found that $FeCl_2{\cdot}4H_2O$ or $MnSO_4{\cdot}4H_2O$ addition resulted in 17% and 9% improved starch degrading activity, respectively. The recombinant glucoamylase from D. geothermalis might be used for simultaneous saccharification and fermentation (SSF) process at high acidic conditions.

This work experimentally determined the effect of microaerobic condition on anaerobic digestion of thickened waste activated sludge in semi-continuous mesophilic digesters at hydraulic retention time of 20 days. The concentration of hydrogen sulfide was $7{\pm}2ppm$ at the microaerobic condition and $14{\pm}2ppm$ at the anaerobic condition. Removal efficiency of volatile solid was not significantly different between microaerobic ($40{\pm}8%$) and anaerobic ($38{\pm}8%$) conditions. There was no important difference between microaerobic ($1,352{\pm}98ml/d$) and anaerobic ($1,362{\pm}104ml/d$) conditions in the biogas production, either. Therefore, it could be concluded that the application of the microaerobic condition was an efficient method of the hydrogen sulfide removal from the biogas.

Semiconductor logistics systems are facing difficulties in increasing production as production processes become more complicated due to the upgrading of fine processes. Therefore, the purpose of the research is to design predictive models that can predict traffic during the pre-planning stage, identify the risk zones that occur during the production process, and prevent them in advance. As a solution, we build FABs using automode simulation to collect data. Then, the traffic prediction model of the areas of interest is constructed using deep learning techniques (keras - multistory conceptron structure). The design of the predictive model gave an estimate of the traffic in the area of interest with an accuracy of about 87%. The expected effect can be used as an indicator for making decisions by proactively identifying congestion risk areas during the Fab Design or Factory Expansion Planning stage, as the maximum traffic per section is predicted.

This paper addresses effects of nanoscale structures on structural colors of micropatterned transparent substrate by light diffraction. Structural colors is widely investigated because they present colors without any chemical pigments. Typically structural colors is presented by diffraction of light on a micropatterned surface or by multiple interference of light on a surface containing a periodic or quasi-periodic nano-structures. In this paper, each structural colors induced by quasi-periodic nano-structures, periodic micro-structures, and nano/micro dual structures is measured in order to investigate effects of nanoscale and microscale structures on structural colors in the transparent substrate. Using pre-fabricated pattern mold and hot-embossing process, nanoscale and microscale structures are replicated on the transparent PMMA(Poly methyl methacrylate) substrate. Nanoscale and microscale pattern molds are prepared by anodic oxidation process of aluminum sheet and by reactive ion etching process of silicon wafer, respectively. Structural colors are captured by digital camera, and their optical transmittance spectrum are measured by UV/visible spectrometer. From experimental results, we found that nano-structures provide monotonic colors by multiple interference, and micro-structures induce iridescent colors by diffraction of light. Structural colors is permanent and unchangeable, thus it can be used in various application field such as security, color filter and so on.

Hydrogen sulfide ($H_2S$) is mainly produced along with methane and hydrocarbons in many gas fields as well as hydrodesulfurization processes of crude oils containing sulfur compounds and the emission of $H_2S$ has a considerable effect on both environmental problem and human health aspects due to formation of, e.g. acid rain and smog. In recent years, ionic liquids (ILs) have been proposed as the most promising solvents for $CO_2$ and hazardous pollutants capture, such as $H_2S$ and sulfur dioxide ($SO_2$). In this work, we demonstrate the use of the predictive COSMO-SAC model for the prediction of Henry's law constant of $H_2S$ in ILs. Furthermore, the method is used to screen for potential IL candidates for $H_2S$ capture from a set of 2,624 ILs formed from 82 cations and 32 anions. The effects of cation on the Henry's law constant of $H_2S$ such as (i) the variation of the alkyl chain length on cation, (ii) the substituent of methyl group ($-CH_3$) for H in C(2) position and (iii) the change of ring structure for cation family are clearly predicted by COSMO-SAC model.