• Korean Chemical Engineering Research
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• v.50 no.4
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• pp.684-689
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• 2012

Poly(acrylic acid) (PAA) microspheres is one of the widely-used polymeric materials for the bio-field application and the electric materials. For the synthesis of PAA microspheres, the polymerization technique using surfactants is applied. After the synthesis, the purification and separation processes are required for the removal of surfactant. When general organic solvents were used, many problems, such as huge amount of waste solvent, additional separation processes, and the possibility of residual media, were occurred. Thus, High-pressure Soxhlet extraction using liquid $CO_2$ was developed to solve these problems. In this study, High-pressure Soxhlet extraction of the synthesized PAA microspheres using liquid $CO_2$ was conducted for the removal of Monasil PCA which is used for the dispersion polymerization of acrylic acid in compressed liquid Dimethyl ether (DME). The morphology of the extracted PAA particles was checked by field emission scanning electron microscopy (FE-SEM) and the residual concentration of Monasil PCA was analyzed by inductively coupled plasma - Optical Emission Spectrometer (ICP-OES). For studying the effect of the solvent effect, Soxhlet extraction was conducted using n-hexane, liquid DME, and liquid $CO_2$. In case of n-hexane, some extracted PAA microspheres were produced. However, deformation was also occurred due to the high thermal energy of n-hexane vapor. Liquid DME could not remove Monasil PCA. When using liquid $CO_2$, the extracted PAA microspheres which were free for the residual solvent were produced without deformation. For finding the optimum operating condition, high-pressure Soxhlet extraction was conducted for 8 hours with changing the temperature of reboiler and condenser. When the extractor temperature is $19.6{\pm}0.2^{\circ}C$ and the pressure is $51.5{\pm}0.5$ bar, the best removal efficiency was obtained.

• The Korean Journal of Nuclear Medicine
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• v.33 no.1
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• pp.84-93
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• 1999

Purpose: We investigated the direct labeling method of antibody with $^{99m}Tc$ and $^{188}Re$ and examined the stability and function of these labeled compounds in in vitro and in vivo. Materials and Methods: Disulfide bond of nonspecific human IgG was reduced to -SH group by 2-mercaptoethanol. Stannous ion was used to reduce $^{99m}Tc$ and $^{188}Re$. The stability of $^{99m}Tc$-IgG and $^{188}Re$-IgG was estimated upto 24 hrs. Biodistribution was evaluated in abscess bearing rats at 4 and 24 hr post-injection of $^{99m}Tc$ or $^{188}Re$ labeled IgG. Results: The number of -SH group per reduced IgG molecule was 2.34. The labeling yield of $^{99m}Tc$-IgG and $^{188}Re$-IgG were 90% and 95%, respectively The stability of $^{99m}Tc$-IgG at 1, 4, 6 and 24 hr was 91%, 83%, 78%, 7% and that of $^{188}Re$-IgG at 1, 4, 16 and 24 hr was 94%, 80%, 47%, 42%, respectively. At 4 hr post-injection of $^{99m}Tc$-IgG, high uptake was found on kidney, blood, stomach and abscess ($9.42{\pm}0.68,\;1.43{\pm}0.24,\;0.86{\pm}0.18,\;0.72{\pm}0.10$ %ID/g, respectively). The uptakes at 24 hr were kidney, abscess,.itomach, and blood in descending order. In case of $^{188}Re$-lgG, high uptake at 4 hr post injection appeared on kidney, blood, abscess and stomach ($3.92{\pm}0.62,\;1.32{\pm}0.08,\;0.88{\pm}0.01,\;0.26{\pm}0.06$, respectively). The uptakes at 24 hr were kidney, abscess, blood and stomach in descending order. The abscess to blood uptake ratio of $^{99m}Tc$-IgG was 0.5 at 4 hr and 2.02 at 24 hr and that of $^{188}Re$-IgG was 0.67 and 1.29. Conclusion: $^{99m}Tc$-IgG and $^{188}Re$-IgG canbe labeled efficiently with direct labeling method. However, $^{99m}Tc$-IgG and $^{188}Re$-IgG, labeled with direct method, was unstable. Further study is needed to enhance the stability of the antibody labeling.

• Analytical Science and Technology
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• v.19 no.6
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• pp.535-543
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• 2006

Being used widely in semiconductor and display manufacturing, $NF_3$ is internationally considered as one of the regulated compounds in emission. Numerous companies have been continuously trying to reduce the emissions of $NF_3$ to comply with the global environmental regulation. This work is made to report the destruction and removal efficiency (DRE) of electrically heated scrubbers and the use rate in process chambers installed in three main LCD manufacturing companies in Korea. As the measurement techniques for $NF_3$ emission, mass flow controlled helium gas was continuously supplied into the equipment by which scrubber efficiency is being measured. The partial pressures of $NF_3$ and helium were accurately measured for each sample using a mass spectrometer, as it is emitted from inlet and outlet of the scrubber system. The results show that the DRE value for electrically heated scrubbers installed before 2004 is less than 52 %, while that for the new scrubbers modified based on measurement by scrubber manufacturer has been sigificentely improved upto more than 95 %. In additon, we have confirmed the efficiency depends on such variables as the inlet gas flow rate, water content, heater temperature, and preventative management period. The use rates of $NF_3$ in process chambers were also affected by the process type. The use rate of radio frequency source chambers, built in the $1^{st}$ and $2^{nd}$ generation process lines, was determined to be less than 75 %. In addition, that of remote plasma source chambers for the $3^{rd}$ generation was measured to be aboove 95 %. Therefore, the combined application of improved scrubber and the RPSC process chamber to the semiconductor and display process can reduce $NF_3$ emmision by 99.95 %. It is optimistic that the mission for the reduction of greenhouse gas emission can be realized in these LCD manufacturing companies in Korea.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.