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    ์ฅ ๊ฐ„์„ธํฌ์•” ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์ค‘์žฌ์  ์ข…์–‘ํ•™ ์‹คํ—˜์—์„œ ์ƒ‰์ „๋ฌผ์งˆ์˜ ์ด์ƒ์ ์ธ ํฌ๊ธฐ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2022. 8. ์ •์ง„์šฑ์ตœ์ง„์šฐ.Objective To optimize future translational research, the present study aimed to determine the ideal range of sizes for embolic agents in interventional oncology experiments utilizing rat models of hepatocellular carcinoma. Materials and Methods Fifty-four male Spragueโ€“Dawley rats were randomly divided into two groups to evaluate the distribution of microparticles and tumor response rates. After implanting hepatoma cells into the rodent liver, fluorescent microparticles of diverse size ranges were administered via the hepatic artery. In the first group, the distribution of microparticles was evaluated in hepatoma-free rats, and tumor necrosis rates following administration of the pre-determined amounts of microparticles were measured in tumor-bearing rats. Afterwards, the three microparticle sizes associated with the best tumor response rates were chosen for analysis of tumor necrosis rates following complete hepatic artery embolization in the second group. Results The tendency for microparticles to distribute in non-target organs increased as microparticle size decreased below 15 ยตm. Tumor necrosis rates tended to be higher in rats treated with 15โ€“19-ยตm microparticles than in those treated with 18โ€“24.9-ยตm or 25โ€“35-ยตm microparticles. The in-group deviation of tumor necrosis rates was highest for microparticle sizes of 18โ€“24.9 ยตm and 25โ€“35 ยตm, which implies proximal embolization of the hepatic artery for larger microparticle sizes. However, there was no statistically significance among the three groups (p = .095). Conclusion These results suggest that embolic agents ranging in size from 15โ€“19 ยตm should be considered as the first option for achieving tumoricidal effects via transarterial treatments in rat models of HCC.์—ฐ๊ตฌ ๋ชฉ์  ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฅ์˜ ๊ฐ„์„ธํฌ์•” ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ์ƒ‰์ „๋ฌผ์งˆ์˜ ์ด์ƒ์ ์ธ ํฌ๊ธฐ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 54 ๋งˆ๋ฆฌ์˜ ์ˆ˜์ปท Sprague-Dawley ์ฅ๋ฅผ ๋ฌด์ž‘์œ„๋กœ 2๊ฐœ์˜ ๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด ํ•œ ๊ตฐ์€ ์ข…์–‘ ๋ฐ ์žฅ๊ธฐ ๋ณ„ ๋ฏธ์„ธ์ž…์ž์˜ ๋ถ„ํฌ๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ , ํ•œ ๊ตฐ์€ ์ข…์–‘ ๋ฐ˜์‘์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ ๊ตฐ์˜ ์ผ๋ถ€ ์ฅ์™€ ๋‘๋ฒˆ์งธ ๊ตฐ์˜ ๋ชจ๋“  ์ฅ์—์„œ ๊ฐ„์„ธํฌ์•” ์„ธํฌ๋ฅผ ์ฅ์˜ ๊ฐ„์— ์ด์‹ํ•˜์˜€๊ณ , ์ดํ›„ ๋‹ค์–‘ํ•œ ํฌ๊ธฐ์˜ ํ˜•๊ด‘์ž…์ž๋ฅผ ๋ชจ๋“  ์ฅ์˜ ๊ฐ„๋™๋งฅ์„ ํ†ตํ•˜์—ฌ ์ฃผ์ž…ํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ ๊ตฐ์€ ๊ฐ„์„ธํฌ์•”์ด ์—†๋Š” ์ฅ์™€ ๊ฐ„์„ธํฌ์•”์ด ์žˆ๋Š” ์ฅ๋กœ ๋‚˜๋‰˜๋Š”๋ฐ, ๊ฐ„์„ธํฌ์•”์ด ์—†๋Š” ์ฅ์—์„œ๋Š” ๊ฐ ์žฅ๊ธฐ๋ณ„ ๋ฏธ์„ธ์ž…์ž์˜ ๋ถ„ํฌ๋ฅผ ๋ถ„์„ํ•˜์˜€๊ณ , ๊ฐ„์„ธํฌ์•”์ด ์žˆ๋Š” ์ฅ์—์„œ๋Š” ์ข…์–‘ ๋ฐ ๊ฐ ์žฅ๊ธฐ๋ณ„ ๋ฏธ์„ธ์ž…์ž์˜ ๋ถ„ํฌ ๋ฟ๋งŒ์ด ์•„๋‹ˆ๋ผ ์ข…์–‘ ๊ดด์‚ฌ์œจ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ ๊ตฐ์—์„œ๋Š” ์•ž์—์„œ ๊ณ„์‚ฐํ•œ ์ข…์–‘ ๊ดด์‚ฌ์œจ์ด ๋†’์€ 3๊ฐ€์ง€ ํฌ๊ธฐ์˜ ๋ฏธ์„ธ์ž…์ž๋ฅผ ๊ณจ๋ผ ์ฅ์˜ ๊ฐ„๋™๋งฅ์„ ์™„์ „ํžˆ ๋ง‰์€ ํ›„ ์ด์— ๋”ฐ๋ฅธ ์ข…์–‘ ๊ดด์‚ฌ์œจ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฏธ์„ธ์ž…์ž์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์„์ˆ˜๋ก ๋น„ํ‘œ์  ์žฅ๊ธฐ๋กœ ๋ฏธ์„ธ์ž…์ž๊ฐ€ ๋ถ„ํฌ๋˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. Nile Red ์ž…์ž์˜ ํฌ๊ธฐ๊ฐ€ 15 ยตm ์ด์ƒ์ด์—ˆ์„ ๋•Œ๋Š”, ํ•œ ๋งˆ๋ฆฌ์˜ ์ฅ๋งŒ ์ œ์™ธํ•˜๊ณ  ๋‚˜๋จธ์ง€ ์ฅ์—์„œ 3๊ฐœ ์ดํ•˜์˜ ๋ฏธ์„ธ์ž…์ž๊ฐ€ ํ์—์„œ ๊ฒ€์ถœ๋˜์—ˆ๋‹ค. ์ข…์–‘ ๊ดด์‚ฌ์œจ์€ 15โ€“19 ยตm ํฌ๊ธฐ์˜ ๋ฏธ์„ธ์ž…์ž๋ฅผ ์‚ฌ์šฉํ•œ ์ƒ‰์ „์ˆ ๊ตฐ์ด 18โ€“24.9 ยตm ์™€ 25โ€“35 ยตm ์ž…์ž๋ฅผ ์‚ฌ์šฉํ•œ ์ƒ‰์ „์ˆ ๊ตฐ๋ณด๋‹ค ๋” ๋†’์€ ์ข…์–‘ ๊ดด์‚ฌ์œจ์„ ๋ณด์˜€๋‹ค. ํ•œํŽธ 18โ€“24.9 ยตm ์™€ 25โ€“35 ยตm ์ž…์ž๋ฅผ ์‚ฌ์šฉํ•œ ์ƒ‰์ „์ˆ ๊ตฐ์€ ์ข…์–‘ ๊ดด์‚ฌ์œจ์˜ ๊ตฐ๋‚ด ํŽธ์ฐจ๋„ ์ปธ๋Š”๋ฐ, ์ด๋Š” ๋„ˆ๋ฌด ํฐ ์ž…์ž๊ฐ€ ์›์œ„๋ถ€ ์ƒ‰์ „์„ ์œ ๋ฐœํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์œผ๋กœ ์ถ”์ธก๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์„ธ ๊ทธ๋ฃน๊ฐ„ ์ข…์–‘ ๊ดด์‚ฌ์œจ์˜ ์ฐจ์ด๊ฐ€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ˆ˜์ค€์—๋Š” ๋„๋‹ฌํ•˜์ง€ ์•Š์•˜๋‹ค (p = .095).Introduction 1 Materials and Methods 3 Results 11 Discussion 18 Bibliography 22์„

    ์ด์‚ฐํ™” ํƒ€์ดํƒ€๋Š„ ์ฝ”ํŒ…๋œ ํ—ฌ๋ฆฌ์ฝ”์ด๋“œ์— ์˜ํ•œ ์›ํŽธ๊ด‘ ์˜์กด์  ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ์ƒ์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2022.2. ๋‚จ๊ธฐํƒœ.Since the industrial revolution, various problems from fossil fuels have emerged, and new energy sources and eco-friendly production techniques are needed. In this context, H2O2 is an environmentally friendly chemical oxidizing agent that can be used for water treatment, etc. and a sustainable potential high energy carrier, so it is very important to produce it in an environmentally friendly way. For this purpose, a photocatalyst that uses solar fuel to send a photochemical reaction is promising. In particular plasmonic metal based photocatalysts are being actively studied as they enable the production of socially important sustainable energy sources and breakthroughs in the challenge of intrinsic photocatalysts such as the existing low efficiency and UV-limited photon absorption band. Although there are few in this context, the fascinating concept of chirality has recently been grafted here, and the development of advanced technology that enhances both efficiency and controllability has shown the potential for extension to biochemical applications. However, the formation of a plasmonic photocatalyst structure with strong chiroptical was an obstacle, so further development in this field has hindered. Recently, our group successfully synthesized chiral single nanoparticle making unique 432-point group symmetry and helicoid morphology. Through chirality transfer in nanoscale utilizing organic-inorganic interactions, chiral gold nanoparticles with a g-factor of 0.2 are formed and named Helicoid. Using a sol-gel based titania coating method, uniform TiO2 shell are successfully coated on the synthesized chiral gold nanoparticle and the thickness and uniformity of the TiO2 shell could by easily controlled by adjusting the pH and time, respectively. Synthesized plasmonic metalโ€“semiconductor nanocomposites can be directly applied as photocatalyst, which combine the plasmonic properties of the core and the photoactivity of the shell, thus improving the photocatalytic efficiency. By controlling various photocatalytic conditions, the H2O2 generation of Helicoid@TiO2 photocatalyst achieved a high yield of 0.48mM and revealed several photocatalytic properties. A proposal of the applicability of our photocatalyst for the photo-dynamic therapy was conducted through an investigation of catalytic activity in the Vis-NIR region. Then, by controlling the handedness of the irradiation light, a demonstration of different hot electron injection process according to CPL was performed, and a remarkable CPL-dependent H2O2 generation was discovered for the first time.์‚ฐ์—…ํ˜๋ช… ์ดํ›„ ํ™”์„์—ฐ๋ฃŒ์˜ ๋‹ค์–‘ํ•œ ๋ฌธ์ œ๊ฐ€ ๋Œ€๋‘๋˜๋ฉด์„œ ์ƒˆ๋กœ์šด ์—๋„ˆ์ง€์›๊ณผ ๊ทธ์— ๋Œ€ํ•œ ์นœํ™˜๊ฒฝ์ ์ธ ์ƒ์‚ฐ๊ธฐ์ˆ ์ด ์š”๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ ๊ณผ์‚ฐํ™”์ˆ˜์†Œ๋Š” ์ข…์ด ํ‘œ๋ฐฑ, ํ™”ํ•™ ํ•ฉ์„ฑ, ํ์ˆ˜ ์ฒ˜๋ฆฌ ๋“ฑ์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ์นœํ™˜๊ฒฝ์ ์ธ ํ™”ํ•™์  ์‚ฐํ™”์ œ์ด์ž ์ง€์†๊ฐ€๋Šฅํ•œ ์ž ์žฌ์ ์ธ ๊ณ ์—๋„ˆ์ง€ ์šด๋ฐ˜์ฒด์ด๋‹ค. ๊ธฐ์กด ๊ณผ์‚ฐํ™”์ˆ˜์†Œ๋ฅผ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•๋“ค์€ ๋งŽ์€ ์—๋„ˆ์ง€์™€ ๋น„์šฉ, ์œ„ํ—˜์„ฑ์„ ๋™๋ฐ˜ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ๋Œ€์ฒดํ•  ๊ฒฝ์ œ์ ์ด๊ณ  ์นœํ™˜๊ฒฝ์  ๋Œ€์ฒดํ•ฉ์„ฑ ๋ฐฉ์•ˆ๋“ค์ด ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜์–ด์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘ ํƒœ์–‘ ์—ฐ๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ด‘ํ™”ํ•™ ๋ฐ˜์‘์„ ๋ณด๋‚ด๋Š” ๊ด‘์ด‰๋งค๊ฐ€ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ์นœํ™˜๊ฒฝ์ ์ด๊ธฐ ๋•Œ๋ฌธ์— ํฐ ๊ฒฝ์Ÿ๋ ฅ์ด ์žˆ๋‹ค. ํŠนํžˆ ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๊ธˆ์† ๊ธฐ๋ฐ˜ ๊ด‘์ด‰๋งค๋Š” ์‚ฌํšŒ์ ์œผ๋กœ ์ค‘์š”ํ•œ ์ง€์† ๊ฐ€๋Šฅํ•œ ์—๋„ˆ์ง€์›์˜ ์ƒ์‚ฐ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜๊ณ  ๊ธฐ์กด ๊ด‘์ด‰๋งค์˜ ๊ณ ์œ ์˜ ํ•œ๊ณ„๋ฅผ ๋ŒํŒŒ ๊ฐ€๋Šฅํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํฐ ๊ด€์‹ฌ์„ ๋ฐ›์•„์™”๋‹ค. ๊ทธ๋ฆฌ ๋งŽ์ง€ ์•Š์ง€๋งŒ, ์ตœ๊ทผ ํ‚ค๋ž„์„ฑ ๊ฐœ๋…์„ ํ”Œ๋ผ์Šค๋ชจ๋‹‰ ๊ธ‰์†์— ์ ‘๋ชฉ์‹œ์ผœ ํšจ์œจ์„ฑ๊ณผ ์ œ์–ด ๊ฐ€๋Šฅ์„ฑ์„ ๋ชจ๋‘ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์ „๋žต์ด ์†Œ๊ฐœ๋˜์—ˆ๊ณ  ๋” ๋‚˜๊ฐ€ ์ƒํ™”ํ•™ ์‘์šฉ ๋ถ„์•ผ๋กœ์˜ ํ™•์žฅ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ์ „๋žต์€ ๊ฐ•ํ•œ ํ‚ค๋ž„ ๊ด‘ํ•™ ์„ฑ์งˆ์„ ๊ฐ–๋Š” ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๋‚˜๋…ธ ๊ตฌ์กฐ์ฒด ํ˜•์„ฑ์ด ์–ด๋ ค์›Œ ์ด ์ „๋žต์„ ์‚ฌ์šฉํ•œ ๋ฐœ์ „์ด ๋”๋””๊ฒŒ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์•„์ง๊นŒ์ง€ ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ๊ฐ™์€ ์œ ์˜๋ฏธํ•œ ์—๋„ˆ์ง€์›์„ ์ด๋Ÿฐ ์ „๋žต์„ ํ†ตํ•ด์„œ ์ƒ์„ฑ ๋ฐ ์กฐ์ ˆํ•œ ์˜ˆ์‹œ๋Š” ์—†๋‹ค. ์šฐ๋ฆฌ๋Š” ์นด์ด๋ž„ ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๊ธˆ์†-๋ฐ˜๋„์ฒด ๋‚˜๋…ธ๋ณตํ•ฉ์ฒด ๋””์ž์ธ์„ ํ†ตํ•ด ํŽธ๊ด‘ ์˜์กด์  ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ์ƒ์„ฑ์„ ์ฒ˜์Œ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ํ—ฌ๋ฆฌ์ฝ”์ด๋“œ@์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„ ๊ด‘์ด‰๋งค์˜ ์„ฑ๊ณต์ ์ธ ํ•ฉ์„ฑ์— ์˜ํ•ด ๊ตฌํ˜„ ๊ฐ€๋Šฅํ•˜์˜€๋Š”๋ฐ ์ด๋ฅผ ์œ„ํ•ด 2๊ฐ€์ง€ ๋ฌผ์งˆ์„ ์„ ํƒํ•˜์˜€๋‹ค. ์ฒซ๋ฒˆ์งธ๋Š” ์šฐ๋ฆฌ ๊ทธ๋ฃน์—์„œ ํ•ฉ์„ฑํ•œ ๋…ํŠนํ•œ 432 ์  ๊ทธ๋ฃน ๋Œ€์นญ๊ณผ ๋‚˜์„  ํ˜•ํƒœ๋ฅผ ๋งŒ๋“œ๋Š” ํ‚ค๋ž„ ๋‹จ์ผ ๊ธˆ ๋‚˜๋…ธ ์ž…์ž์ด๋‹ค. ์œ ๊ธฐ-๋ฌด๊ธฐ ์ƒํ˜ธ์ž‘์šฉ์„ ํ™œ์šฉํ•œ ๋‚˜๋…ธ ๊ทœ๋ชจ์˜ ํ‚ค๋ž„์„ฑ ์ „๋‹ฌ์„ ํ†ตํ•ด g-factor๊ฐ€ 0.2์ธ ํ‚ค๋ž„ ๊ธˆ ๋‚˜๋…ธ์ž…์ž๋ฅผ ํ•ฉ์„ฑํ•˜์˜€๊ณ  ์ด๋ฅผ ํ—ฌ๋ฆฌ์ฝ”์ด๋“œ๋ผ๊ณ  ๋ช…๋ช…ํ•˜์˜€๋‹ค. ๋‘๋ฒˆ์งธ๋Š” ๋…์„ฑ์ด ์—†๊ณ  ์ €๋ ดํ•˜๊ณ  ๊ฒฝ์ œ์ ์ด๋ฉฐ ๊ด‘๋ถ€์‹์— ์•ˆ์ •์ ์ด๊ณ  ๊ด‘์ด‰๋งค ํŠน์„ฑ์— ์ข‹์•„ ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ์ƒ์‚ฐ์„ ์œ„ํ•œ ์šฐ์ˆ˜ํ•œ ๊ด‘์ด‰๋งค๋กœ ์•Œ๋ ค์ง„ ์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„์ด๋‹ค. ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๊ธˆ์†์ธ ํ—ฌ๋ฆฌ์ฝ”์ดํŠธ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ, ๊ด‘์ด‰๋งค ํŠน์„ฑ์ด ์ข‹์€ ์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„์„ ๊ป์งˆ๋กœ ํ•˜์—ฌ, ์†”-๊ฒ” ๊ธฐ๋ฐ˜์˜ ํ‹ฐํƒ€๋‹ˆ์•„ ์ฝ”ํŒ… ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๊ท ์ผํ•œ ์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„ ๊ป์งˆ์„ ์ฝ”ํŒ…๋œ ํ—ฌ๋ฆฌ์ฝ”์ด๋“œ@์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„ ๋‚˜๋…ธ ์ž…์ž๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ๊ป์งˆ ํ˜•์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ธ๋ถ„ํ™” ํ•˜์—ฌ, ๊ฐ ์š”์†Œ๋ณ„ ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ ํƒ๊ตฌํ•˜์˜€๊ณ , ์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„ ๊ป์งˆ์˜ ๋‘๊ป˜์™€ ๊ท ์ผ์„ฑ์€ ๊ฐ๊ฐ ์ „๊ตฌ์ฒด ๋†๋„, ์˜จ๋„ ๋“ฑ์„ ์กฐ์ •ํ•˜์—ฌ ๊ท ์ผ๋„์™€ ๋‘๊ป˜๋ฅผ ๋‹จ๊ณ„๋ณ„๋กœ ์กฐ์ ˆํ•˜์˜€๋‹ค. ์ด๋Ÿฐ ๋ฐฉ์‹์œผ๋กœ ์ตœ์ ํ™”๋œ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ pH ๋“ฑ ์—ฌ๋Ÿฌ ๊ด‘์ด‰๋งค์  ์‹คํ—˜์กฐ๊ฑด์„ ์กฐ์ ˆํ•˜์—ฌ 0.48mM ์ด๋ผ๋Š” ๋†’์€ ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ํ™œ์„ฑ์„ ๋‹ฌ์„ฑํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํŒŒ์žฅ๋ณ„ ์ด‰๋งค ํ™œ์„ฑ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ๊ฐ€์‹œ๊ด‘๊ณผ ๊ทผ์ ์™ธ์„  ์˜์—ญ์—์„œ ํ™œ์„ฑ์„ ๊ฐ€์ง์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์–ด ์กฐ์‚ฌ๊ด‘์˜ ์›ํ˜•ํŽธ๊ด‘ ๋ฐฉํ–ฅ์„ ์ œ์–ดํ•˜์—ฌ ์›ํ˜•ํŽธ๊ด‘์— ๋”ฐ๋ฅธ ์„œ๋กœ ๋‹ค๋ฅธ ์—ด์ „์ž์ฃผ์ž…๊ณผ์ •์ด ์ด‰๋งค ํ™œ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๊ณ  ์›ํ˜•ํŽธ๊ด‘ ์˜์กด์  ๊ณผ์‚ฐํ™”์ˆ˜์†Œ ๋ฐœ์ƒ์„ ์ฒ˜์Œ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์šฐ๋ฆฌ ๊ด‘์ด‰๋งค์˜ ํŠน์ง•์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ด‘์—ญํ•™์š”๋ฒ•๋กœ์„œ์˜ ์ ์šฉ์ฒ˜๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœ๋œ ์ž์™ธ์„  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฐ€์‹œ๊ด‘-๊ทผ์ ์™ธ์„  ์˜์—ญ๋Œ€์—์„œ ๊ตฌ๋™ํ•˜๋Š” ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๊ธฐ๋ฐ˜ ๊ด‘์ด‰๋งค ํ”Œ๋ ›ํผ์€, ์ƒˆ๋กœ์šด ๋งž์ถคํ˜• ํŠน์„ฑ์„ ์œ„ํ•œ ์šฉ์•ก ๊ธฐ๋ฐ˜ ๊ธˆ์†-๋ฐ˜๋„์ฒด ๋‚˜๋…ธ ์ด์ข…๊ตฌ์กฐ์ฒด ์ œ์ž‘ ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ ๋กœ ์‚ฌ์šฉ๋  ๊ฒƒ์ด๋ผ ์ƒ๊ฐํ•˜๋ฉฐ, ํ”Œ๋ผ์ฆˆ๋ชจ๋‹‰ ๋‚˜๋…ธ๊ตฌ์กฐ์ฒด์˜ ์ƒํ™”ํ•™ ๋ถ„์•ผ๋กœ์„œ์˜ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์„ ํ™•๋Œ€ํ•ด ์ค„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ์ƒ๊ฐํ•œ๋‹ค.Chapter 1. Introduction 1 1.1 Conventional Method for Hydrogen Peroxide Production 1 1.2 Plasmonic metal in photocatalyst 2 1.3 Objective of the thesis 3 Chapter 2. Experimental Procedure 5 2.1 Chemicals and materials 5 2.2 Methods 5 2.2.1 Synthesis of seed nanoparticles 5 2.2.2 Synthesis of peptide-directed chiral nanoparticles 6 2.2.3 Synthesis of the Helicoid@TiO2 photocatalyst 6 2.2.4 Photocatalytic study for H2O2 generation 7 2.2.5 Light penetration experiment dependent on porcine skin thickness 7 2.2.6 Characterization 8 Chapter 3. Results and Discussion 9 3.1 Synthesis of the Helicoid@TiO2 photocatalyst 9 3.1.1 TiO2 coating principle and mechanism on Helicoid 9 3.1.2 Parameters affecting TiO2 shell uniformity and thickness 12 3.2 Characterization of optimized conditions of the photocatalyted H2O2 generation by Helicoid@TiO2 core-shell photocatalyst 16 3.2.1 Photocatalytic property depending on heat treatment 16 3.2.2 Photocatalytic property dpending on purging and pH 18 3.2.3 Photocatalytic durabiiltiy 20 3.3 Investigation of catalytic activity depending on function of light 22 3.3.1 Investigation of catalytic activity in Vis-NIR region 22 3.3.2 Investigation of catalytic activity depending on CPL 24 3.3.3 Proposal of the applicability of the Helicoid@TiO2 photocatalyst for photo dynamic therapy 27 Chapter 4. Concluding Remarks 30 References 31์„

    ํ•œ๊ตญ - ํ™์ฝฉ์˜ ์‡ผํ•‘๊ด€๊ด‘ ๊ฒฝ์Ÿ๋ ฅ ๋น„๊ต๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› : ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œ์ง€์—ญํ•™์ „๊ณต), 2016. 8. ๊น€ํ˜„์ฒ .Abstract Comparative Research on Competiveness of Shopping Tourism Industry - South Korea and Hong Kong Kim Sung-Ho Department of International Area Studies Graduate School of International Studies Seoul National University The success of the tourism industry is determined by its competitiveness. A destination is competitive when it can attract and satisfy travelers. And, that competitiveness is determined by factors that influence the performance of people or organizations involved in providing the tourism products. As we know, tourisms purpose can be divided into many kinds, such as medical tourism, cultural tourism, historical tourism and so forth. Among them, Shopping has risen as one of most significant component of the tourism industry. Even in recent years, shopping has turned out to be a determining factor for destination selection and an important part of the travel experience. Hence, shopping tourism destinations around the world have a great opportunity to exploit this new market by developing unique and attractive shopping tourism experiences. In recent years, with growing living standards and high demand for foreign brands, the amount of outbound Chinese tourists has increased dramatically, and among the variety of destinations, Korea has turned out to be one of prime destinations for Chinese because it is geographically close to mainland China and famous for Han-Ryu culture as well. This trend is a considerable opportunity for Korean economic development, so the Korean government has made several promotions such as Korean Black Friday and tax refunds to attract more potential Chinese tourists. But recently we are facing two challenges: one is increasing dissatisfaction with Korea, the other one is the rise of other competing destinations such as Hong Kong. According to a report by Korean Tourism organization, Chinese dissatisfaction rate has risen to 6.1% in 2014 from 2.2% in 2011. This has brought about unwillingness to revisit Korea and researchers say if this problem is not figured out, there will be big challenge with sustainable development of the shopping tourism industry. With this background, this research explores the competitiveness of Korean shopping tourism industry by using Importance Performance Analysis (IPA). To provide implications for policy makers, a comparative analysis between Korea and Hong Kong has been undertaken. Factors pertaining to the destinations competitiveness were used to build an instrument that was used to make a survey for visitors from mainland China. Respondents were asked to rate the factors for importance and performance. The results were analyzed and discussed with the IPA grid. This research offers a quantitative analysis that can provide information for policy and managerial decisions in the shopping tourism industry. Keyword: shopping tourism destination, Korea, Hong Kong, China, competitiveness, Importance-Performance Analysis (IPA) Student Number: 2014-24305I. Introduction 1 II. Research Question 5 III. Literature review 6 1. Tourism Destination Competitiveness 6 2. Shopping tourism 9 3. Shopping tourism satisfaction 12 IV. Methodology 14 1. Importance Performance Analysis (IPA) 14 2. Survey Instrument 18 3. Data Analysis 19 V. Demographic Structure 23 VI. IPA analysis 25 1. Korean Case 25 2. Hong Kong Case 35 VII. Discussion - Comparative approach 42 VIII. Conclusion 51 IX. Bibliography 54 ๊ตญ๋ฌธ ์ดˆ๋ก 59Maste

    Protein kinase casein kinase 2-mediated upregulation of N-cadherin confers anoikis resistance on esophageal carcinoma cells.

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    Previously, we reported that high PKCK2 activity could protect cancer cells from death receptor-mediated apoptosis through phosphorylation of procaspase-2. Because anoikis is another form of apoptosis, we asked whether PKCK2 could similarly confer resistance to anoikis on cancer cells. Human esophageal squamous cancer cell lines with high PKCK2 activity (HCE4 and HCE7) were anoikis-resistant, whereas cell lines with low PKCK2 activity (TE2 and TE3) were anoikis-sensitive. Because the cells showed different sensitivity to anoikis, we compared the expression of cell adhesion molecules between anoikis-sensitive TE2 and anoikis-resistant HCE4 cells using cDNA microarray. We found that E-cadherin is expressed only in TE2 cells; whereas N-cadherin is expressed instead of E-cadherin in HCE4 cells. To examine whether PKCK2 activity could determine the type of cadherin expressed, we first increased intracellular PKCK2 activity in TE2 cells by overexpressing the PKCK2ฮฑ catalytic subunit using lentivirus and found that high PKCK2 activity could switch cadherin expression from type E to N and confer anoikis resistance. Conversely, a decrease in PKCK2 activity in HCE4 cells by knockdown of PKCK2ฮฑ catalytic subunit using shRNA induced N- to E-cadherin switching and the anoikis-resistant cells became sensitive. In addition, N-cadherin expression correlated with PKB/Akt activation and increased invasiveness. We conclude that high intracellular PKCK2 activity confers anoikis resistance on esophageal cancer cells by inducing E- to N-cadherin switching.ope

    GAS ๊ณต์ •์„ ์ด์šฉํ•œ ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2013. 8. ์ด์ข…๋ฏผ.๊ณ ์ฒด ์ž…์ž๋Š” ๊ทธ ์ž…์ž์˜ ํฌ๊ธฐ์™€ ํ˜•ํƒœ ๋“ฑ์— ๋”ฐ๋ผ ๊ทธ ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋Ÿฌํ•œ ์„ฑ์งˆ๋“ค์„ ์›ํ•˜๋Š” ํ˜•ํƒœ๋กœ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•๋“ค์ด ๋งŽ์ด ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ๊ธฐ์กด์—๋Š” ์ œ๋ถ„, ์œ ๊ธฐ ์šฉ๋งค์„ ํ†ตํ•œ ์žฌ๊ฒฐ์ • ๋“ฑ์˜ ๋ฐฉ๋ฒ•์ด ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์—ˆ์œผ๋‚˜ ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ์žฌ๊ฒฐ์ •ํ™”ํ•  ๋Œ€์ƒ ๋ฌผ์งˆ์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ์žฌ๊ฒฐ์ •ํ™”์˜ ์„ฑ๋Šฅ ์ฐจ์ด๋‚˜ ์•ˆ์ „์„ฑ ๋ฌธ์ œ๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. Gas Anti-Solvent (GAS) ๊ณต์ •์€ ๊ทผ๋ž˜ ๋“ค์–ด ์ƒˆ๋กœ์ด ๊ฐœ๋ฐœ๋œ ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์œผ๋กœ, ์ดˆ์ž„๊ณ„์ƒ์˜ ์œ ์ฒด๋ฅผ ์ด์šฉํ•˜์—ฌ ์šฉ์•ก ๋‚ด์— ์šฉํ•ด๋œ ๊ณ ์ฒด ๋ฌผ์งˆ์„ ์žฌ๊ฒฐ์ •ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ์ด ๊ณต์ •์€ ์‹คํ—˜์‹ค ๊ทœ๋ชจ์˜ ์‹คํ—˜ ์—ฐ๊ตฌ๋Š” ๋‹ค์–‘ํ•˜๊ฒŒ ์ด๋ฃจ์–ด ์กŒ์ง€๋งŒ, ์‹ค์ œ ํ”Œ๋žœํŠธ์˜ ์šด์ „์„ ์œ„ํ•œ ์žฌ๊ฒฐ์ •ํ™” ๊ธฐ๊ธฐ์˜ ๋ชจ๋ธ๋ง ๋ฐ ์—ฐ๊ณ„๋œ ์ „์ฒด ๊ณต์ •์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ฏธ์ง„ํ•œ ์ƒํƒœ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๊ณต์ •์˜ ์ƒ์—…์  ๊ทœ๋ชจ ์šด์ „์„ ์œ„ํ•œ ๊ณต์ •์˜ ์žฌ๊ฒฐ์ •ํ™”๊ธฐ, ๋ถ„๋ฆฌ ๊ณต์ • ๋“ฑ ์ „์ฒด ๊ณผ์ •์ด ํ†ตํ•ฉ ์—ฐ๊ณ„๋œ ๊ณต์ • ๋ชจ๋ธ์„ ์„ค๊ณ„ํ•˜๊ณ  ๊ฒฝ์ œ์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์ตœ์ ์˜ ๊ณต์ • ๊ตฌ์กฐ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. Custom Modeler๋ฅผ ์ด์šฉํ•˜์—ฌ GAS ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ์žฌ๊ฒฐ์ •ํ™”๊ธฐ๋ฅผ ๋ชจ์‚ฌํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•˜์—ฌ HMX ๊ฒฐ์ •์˜ ์ž…์ž ํฌ๊ธฐ ๋ถ„ํฌ ๊ฒฐ๊ณผ๋ฅผ ๊ตฌํ•˜์˜€๊ณ , ์ด์™€ ์—ฐ๊ณ„๋˜๋Š” HMX ๊ฒฐ์ •์˜ ๋ถ„๋ฆฌ, ์ด์‚ฐํ™”ํƒ„์†Œ ๋ฐ ์•„์„ธํ†ค์˜ ๋ถ„๋ฆฌ ๋ฐ ์žฌํ™œ์šฉ ๊ณต์ •๋ฅผ ๋ชจ์‚ฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ์—ฌ๋Ÿฌ ๊ณต์ • ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๊ฒฝ์ œ์„ฑ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ตœ์ ์˜ ๊ณต์ • ๊ตฌ์กฐ๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์„ ์—ฐ๊ณ„ํ•˜์—ฌ ํ†ตํ•ฉ๋œ ๊ณต์ • ๋ชจ๋ธ๋กœ ์ œ์ž‘ํ•˜๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹ค์ œ ์ƒ์—…์  ๊ทœ๋ชจ์˜ ์šด์ „์„ ์œ„ํ•œ ๊ณต์ • ์„ค๊ณ„์˜ ๊ธฐ์ดˆ ๋ฐ์ดํ„ฐ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์ดํ›„ ๋ณธ ์—ฐ๊ตฌ์™€ ํ•จ๊ป˜ ๋ถˆ์—ฐ์†์  ๋ฐฐ์น˜ ๊ณต์ •์ธ GAS ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ์ œ์–ด ๊ตฌ์กฐ ์—ฐ๊ตฌ๊ฐ€ ์ถ”๊ฐ€์ ์œผ๋กœ ์ง„ํ–‰๋œ๋‹ค๋ฉด ์ œํ’ˆ์˜ ํ’ˆ์งˆ ์•ˆ์ •ํ™” ๋ฐ ์ƒ์‚ฐ์„ฑ ํ–ฅ์ƒ์„ ํ†ตํ•˜์—ฌ ์ƒˆ๋กญ๊ฒŒ ๊ฐœ๋ฐœ๋œ GAS ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ๊ฒฝ์Ÿ๋ ฅ ํ™•๋ณด์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.CONTENTS I. ์„œ๋ก  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 ์—ฐ๊ตฌ ๋ชฉ์  . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ์›๋ฆฌ . . . . . . . . . . . . . . . . . . . . 3 1.4 GAS ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ • . . . . . . . . . . . . . . . . . . . . . 4 1.5 ๊ณต์ • ๋Œ€์ƒ ๋ฌผ์งˆ . . . . . . . . . . . . . . . . . . . . . . . . 7 II. ์ „์ฒด ๊ณต์ •์˜ ๊ตฌ์„ฑ . . . . . . . . . . . . . . . . . . . . . . . . 9 III. GAS ์žฌ๊ฒฐ์ •ํ™” ๊ณต์ •์˜ ๋Œ€ํ˜•ํ™”์— ๊ณ ๋ คํ•ด์•ผํ•  ์š”์†Œ . . . . . . 11 IV. GAS ์žฌ๊ฒฐ์ •ํ™”๊ธฐ์˜ ๋ชจ๋ธ๋ง . . . . . . . . . . . . . . . . . . . 12 4.1 ์žฌ๊ฒฐ์ •ํ™”๊ธฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ „๋žต . . . . . . . . . . . . . . . . 12 4.2 GAS๊ณต์ •์˜ ์ˆ˜ํ•™์ ๋ชจ๋ธ . . . . . . . . . . . . . . . . . . 13 4.2.1 ์ด์‚ฐํ™”ํƒ„์†Œ, ์•„์„ธํ†ค, HMX ๊ฐ„์˜ Peng-Robinson ์ƒํƒœ๋ฐฉ์ •์‹์˜ ์ด์„ฑ๋ถ„๊ณ„ ์ƒํ˜ธ์ž‘์šฉ์— ๊ด€ํ•œ ๊ณ„์ˆ˜ . 13 4.2.2 ๊ฒฐ์ • ํ˜•ํƒœ์— ๋”ฐ๋ฅธ HMX ๊ฒฐ์ •์˜ ํ•ต ์ƒ์„ฑ ์†๋„ ๋ฐ ์„ฑ์žฅ์†๋„ ๋ชจ๋ธ๊ณ„์ˆ˜ . . . . . . . . . . . . . . . . 14 4.3 Customizing simulator๋ฅผ ์ด์šฉํ•œ ์ž…์ž ํฌ๊ธฐ ๋ถ„ํฌ์˜ ๋ชจ์‚ฌ ๊ตฌํ˜„ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.4 PSD ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์˜ ์—ฐ๊ณ„ . . . . . . . . . . . . . . . . 22 4.4.1 ์ž‘์„ฑ๋œ ์žฌ๊ฒฐ์ •ํ™”๊ธฐ ๋ชจ๋ธ์˜ ๋ฐ์ดํ„ฐ ์ถœ๋ ฅ ํฌํŠธ(output port)๋ฅผ ์ •์˜ . . . . . . . . . . . . . . . . . . . 22 4.4.2 100๊ฐœ์˜ ์ž…์ž ํฌ๊ธฐ ๋ถ„ํฌ ๋ฐ์ดํ„ฐ๋ฅผ ( 2 x 100 )์˜ integer set์œผ๋กœ๋ณ€ํ™˜ . . . . . . . . . . . . . . . . . 23 4.4.3 Interger set์„ ๊ณต์ • ๋ชจ์‚ฌ๊ธฐ์˜ ํ˜ผํ•ฉ ์ŠคํŠธ๋ฆผ์˜ ์ž…์ž ํฌ๊ธฐ ๋ถ„ํฌ ์ˆ˜์น˜๋กœ ๋ณ€ํ™˜ . . . . . . . . . . . . . . . 23 4.5 ๊ณต์ • ๋ชจ์‚ฌ ํ”„๋กœ๊ทธ๋žจ๊ณผ ์ž‘์„ฑ๋œ custom model๊ณผ์˜ ์—ฐ๊ณ„ . . 24 4.6 ์žฌ๊ฒฐ์ •ํ™”๊ธฐ ๋‚ด๋ถ€์˜ ๊ณต์ • ์ƒํƒœ ๊ณ„์‚ฐ . . . . . . . . . . . . 26 V. ์žฌ๊ฒฐ์ •ํ™”๊ธฐ ์ดํ›„ ์—ฐ๊ณ„ ๊ณต์ • ๊ตฌ์„ฑ . . . . . . . . . . . . . . . . 28 5.1 ์žฌ๊ฒฐ์ •ํ™”๋œ HMX ๊ฒฐ์ •์˜ ํšŒ์ˆ˜ . . . . . . . . . . . . . . . 28 5.2 ์ด์‚ฐํ™”ํƒ„์†Œ(Anti-solvent)์™€ ์•„์„ธํ†ค(solvent)์˜ ๋ถ„๋ฆฌ๋ฐ ์žฌํ™œ์šฉ์„ ์œ„ํ•œ ๋‹ค์–‘ํ•œ์‹œ๋‚˜๋ฆฌ์˜ค ๊ตฌ์„ฑ . . . . . . . . . . . . 29 5.2.1 ๋ถ„๋ฆฌ ๋ฐ ์žฌํ™œ์šฉ ๊ณต์ •์˜ ๊ณต์ • ๋ชจ์‚ฌ๊ธฐ ๋ฐ ์—ด์—ญํ•™ ๋ชจ๋ธ . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.2.2 ์ด์‚ฐํ™”ํƒ„์†Œ ์žฌ์••์ถ• ๊ณต์ •์˜ ์ตœ์  ์šด์ „์กฐ๊ฑด ๊ตฌ์„ฑ ๋ฐฉ๋ฒ• . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.2.3 ์‹œ๋‚˜๋ฆฌ์˜ค #1 - ์ˆœ์ˆ˜ํ•œ ์ด์‚ฐํ™”ํƒ„์†Œ์™€ ์ˆœ์ˆ˜ํ•œ ์•„์„ธํ†ค ๋‘ ๊ฐ€์ง€ ๋ฌผ์งˆ์„ ๋ชจ๋‘ ๋ถ„๋ฆฌํ•˜์—ฌ ์žฌํ™œ์šฉ . . . . . 32 5.2.4 ์‹œ๋‚˜๋ฆฌ์˜ค #2 - ์ˆœ์ˆ˜ํ•œ ์ด์‚ฐํ™”ํƒ„์†Œ๋ฅผ ๋ถ„๋ฆฌํ•˜์—ฌ ์žฌํ™œ์šฉํ•˜๊ณ  ์•„์„ธํ†ค+์ž”๋ฅ˜ ์ด์‚ฐํ™”ํƒ„์†Œ๋Š” ํ๊ธฐ . . . . 34 5.2.5 ์‹œ๋‚˜๋ฆฌ์˜ค #3 - ์ˆœ์ˆ˜ํ•œ ์•„์„ธํ†ค์„ ๋ถ„๋ฆฌํ•˜์—ฌ ์žฌํ™œ์šฉํ•˜๊ณ  ์ด์‚ฐํ™”ํƒ„์†Œ+์ž”๋ฅ˜ ์•„์„ธํ†ค์€ ํ๊ธฐ . . . . . . 36 5.3 ์ตœ์ ์˜ ๊ณต์ • ๊ตฌ์„ฑ์„ ์œ„ํ•œ ๊ฐ ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๊ฒฝ์ œ์„ฑ ํ‰๊ฐ€ . . 37 VI. ๊ณต์ •์˜ ํ†ตํ•ฉ์ธํ„ฐํŽ˜์ด์Šค ๋ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ . . . . . . . . . 41 VII. ๊ฒฐ๋ก  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 ์ฐพ์•„๋ณด๊ธฐ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Maste

    Comparison of toluene diisocyanate concentrations collected with different sampling methods by work process type

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    ์‚ฐ์—…๋ณด๊ฑดํ•™๊ณผ/์„์‚ฌ์—ฐ๊ตฌ๋ชฉ์ : ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ ๊ณต์ •๋ณ„ TDIs ํฌ์ง‘๋†๋„๋ฅผ ๋น„๊ตํ•˜์—ฌ ๊ฐœ์ธ ๋…ธ์ถœํ‰๊ฐ€์— ์ ํ•ฉํ•œ ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์ด๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ•: ์ธ์ฒœ์ง€์—ญ๋‚ด TDIs ์ทจ๊ธ‰ ์‚ฌ์—…์žฅ 2๊ณณ์„ ์„ ์ •ํ•˜์˜€์œผ๋ฉฐ, ์‹œ๋ฃŒ์ฑ„์ทจ ์œ„์น˜๋Š” TDIs์˜ ๋ฐœ์ƒ ํ˜•ํƒœ๊ฐ€ ๋‹ค๋ฅธ ์Šคํ”„๋ ˆ์ด ๋„์žฅ๊ณต์ •, ๊ฑด์กฐ๊ณต์ •, ์—ฐ๋งˆ๊ณต์ •, ๋ฐœํฌ๊ณต์ • ์ด๋‹ค. ๊ณต๊ธฐ์ค‘ TDIs์˜ ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์€ ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”(OSHA#42), ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”, ์ž„ํ•€์ €(NIOSH#5522)๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋™์ผ ์žฅ์†Œ์—์„œ ๋™์‹œ์— ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‹œ๋ฃŒ์ฑ„์ทจ ์‹œ๊ฐ„์€ ์˜ค์ „, ์˜คํ›„ ๊ฐ๊ฐ 3์‹œ๊ฐ„ ์ด์ƒ์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ: ๊ณต์ •๋ณ„ ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ TDIs ํฌ์ง‘๋†๋„๋ฅผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ 2,4-TDI์˜ ๊ฒฝ์šฐ ์Šคํ”„๋ ˆ์ด ๋„์žฅ๊ณต์ •๊ณผ ๋ฐœํฌ๊ณต์ •์—์„œ, 2-6-TDI์˜ ๊ฒฝ์šฐ ๋ชจ๋“  ๊ณต์ •์—์„œ ์ž„ํ•€์ €, ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”, ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋” ์ˆœ์œผ๋กœ ํฌ์ง‘๋†๋„๊ฐ€ ๋†’์•˜๋‹ค.์Šคํ”„๋ ˆ์ด ๋„์žฅ๊ณต์ •์˜ ๊ฒฝ์šฐ ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,4 ๋ฐ 2,6-TDI์˜ ๋†๋„๋Š” 0.341, 0.989ใŽ/ใŽฅ๋กœ ์ž„ํ•€์ €๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„ 0.896, 1.878ใŽ/ใŽฅ์˜ 38%์™€ 53%์ด์–ด ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„(0.225, 0.681ใŽ/ใŽฅ) 25%์™€ 36% ๋ณด๋‹ค ๋†’์•˜๋‹ค.์—ฐ๋งˆ๊ณต์ •์˜ ๊ฒฝ์šฐ ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,4 ๋ฐ 2,6-TDI์˜ ๋†๋„๋Š” 0.214, 0.397ใŽ/ใŽฅ๋กœ ์ž„ํ•€์ €๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„ 0.213, 0.565ใŽ/ใŽฅ์˜ 101%์™€ 70%์ด์–ด ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„(0.180, 0.330 ใŽ/ใŽฅ) 85%์™€ 58% ๋ณด๋‹ค ๋†’์•˜๋‹ค.๋ฐœํฌ๊ณต์ •์˜ ๊ฒฝ์šฐ ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,4 ๋ฐ 2,6-TDI์˜ ๋†๋„๋Š” 0.266, 0.678ใŽ/ใŽฅ๋กœ ์ž„ํ•€์ €๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„ 0.381, 0.981ใŽ/ใŽฅ์˜ 70%์™€ 69%์ด์–ด ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ TDIs์˜ ๋†๋„(0.247, 0.514 ใŽ/ใŽฅ) 65%์™€ 52% ๋ณด๋‹ค ๋†’์•˜๋‹ค.๊ฑด์กฐ๊ณต์ •์˜ ๊ฒฝ์šฐ 2,6-TDI๋งŒ ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,6-TDI์˜ ๋†๋„๊ฐ€ 0.282ใŽ/ใŽฅ๋กœ ์ž„ํŽ€์ €๋กœ ํฌ์ง‘ํ•œ 2,6-TDI์˜ ๋†๋„ 0.353ใŽ/ใŽฅ์˜ 80%๋กœ ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,6-TDI์˜ ๋†๋„(0.203ใŽ/ใŽฅ) 58% ๋ณด๋‹ค ๋†’์•˜๋‹ค. 2,4-TDI์˜ ๊ฒฝ์šฐ ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ 2,4-TDI์˜ ๋†๋„๊ฐ€ 0.110ใŽ/ใŽฅ๋กœ ์ž„ํ•€์ €์™€ ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋” ๋†๋„(0.094ใŽ/ใŽฅ) ๋ณด๋‹ค ๋” ๋†’์•˜๋‹ค. ๊ฒฐ๋ก : ์„ธ ๊ฐ€์ง€ ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ ๊ณต์ •์—์„œ TDIs ํฌ์ง‘๋†๋„๋ฅผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ์ž„ํ•€์ €์˜ ํฌ์ง‘๋†๋„๊ฐ€ ๊ฐ€์žฅ ๋†’์•˜์œผ๋‚˜, NIOSH์—์„œ๋Š” DMSO์˜ ๋…ธ์ถœ ์œ„ํ—˜์ด ์žˆ์–ด ์ž„ํ•€์ € ์ฑ„์ทจ๋ฐฉ๋ฒ•์„ ์ง€์—ญ์‹œ๋ฃŒ์ฑ„์ทจ๋กœ ์ œํ•œ์„ ๋‘๊ณ  ์žˆ์–ด ํ˜„ํ–‰ ์ž‘์—…ํ™˜๊ฒฝ์ธก์ •์‹œ ๊ฐœ์ธ๋…ธ์ถœํ‰๊ฐ€์— ์ ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๊ฑด์กฐ๊ณต์ •์„ ์ œ์™ธํ•œ ๋ชจ๋“  ๊ณต์ •์—์„œ TDIs๋Š” ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๋กœ ํฌ์ง‘ํ•œ TDIs ๋†๋„๊ฐ€ ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋” ๋ณด๋‹ค ๋†’์•˜๋‹ค. ์ด ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ ์Šคํ”„๋ ˆ์ด ๋„์žฅ๊ณต์ •, ์—ฐ๋งˆ๊ณต์ •, ๋ฐœํฌ๊ณต์ •์˜ TDIs ๊ฐœ์ธ์‹œ๋ฃŒ ์ฑ„์ทจ์‹œ ๊ฐœ๋ฐฉํ˜• ์นด์„ธํŠธ ํ™€๋” ๋ณด๋‹ค ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋”๊ฐ€ ์ ์ ˆํ•œ ์‹œ๋ฃŒ์ฑ„์ทจ๋ฐฉ๋ฒ•์ด๋‹ค. ํŠนํžˆ, ํƒ€ ๊ณต์ •์— ๋น„ํ•ด ํฌ์ง‘๋†๋„๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ์Šคํ”„๋ ˆ์ด ๋„์žฅ๊ณต์ •์˜ ๊ฐœ์ธ๋…ธ์ถœํ‰๊ฐ€๋Š” ๋ณ€ํ˜• 2๋‹จ ์นด์„ธํŠธ ํ™€๋” ์‚ฌ์šฉ์ด ๋ฐ”๋žŒ์งํ•˜๋‹ค.ope

    The Influence of Roughing Mill Conditions on the Strength and Toughness of Plate

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    Maste

    FCC๊ณต์ •์˜ ๋ชจ๋ธ๋ง, ์‹œ๋ฎฌ๋ ˆ์ด์…˜, ์‹œ์Šคํ…œ ๊ตฌ์กฐ๋ถ„์„ ๋ฐ ๊ณต๊ธ‰๋ฌผ ์ •์˜

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2018. 8. ์ด์ข…๋ฏผ.This thesis presents a mathematical approach on modeling the fluid catalytic cracking(FCC) process and its application including systematic analysis and feed characterization. Fluid catalytic cracking (FCC) is one of the most important re_x000C_nery processes. It is used for cracking high molecular weight hydrocarbon feedstocks to smaller, valuable molecules. The existing FCC plant in the re_x000C_nery consists of a reaction unit which is followed by the fractionation unit that separates the reactor e_x000F_uent into the final products. The reaction unit is composed of the riser and the regenerator therefore are modeled separately and interconnected. Meanwhile, The process disturbance or faults have a serious impact on process operation, product quality, safety, productivity and process economy if undetected. However, measuring all state variables of a complex FCC process is usually impossible or impractical. What is more realistic is to estimate the state variables based on a _x000C_nite set of measurements. Furthermore, we nave less accessibility on physical properties of the feed in the FCC process. Since direct measurement on the operating plant is not realistic because of both cost and time, alternative methods that provides complete description of FCC process feeds from measured process data is highly demanded. At _x000C_rst, reaction kinetics were developed to describe the reactor effluents and thermodynamic phenomena in the reactor. Empirical correlations that describe the reaction kinetics with model parameters were built. Also, an approach to apply the yield function for the kinetic model of the riser was made. Lastly, hydrodynamics, mass balance and energy balance equations of the riser reactor and the regenerator were considered to complete the modeling. Steady-state simulation results and dynamic responses to the change of process variables were simulated by the process model and compared to the plant data. The results showed good agreement with the measured data from the plant. After the modeling, a systematic analysis was performed to identify the structural observability of the system using the model and process design data. The reactor and regenerator unit in this system were divided into six nodes based on their functions and modeling relationships were built based on nodes and edges of the directed graph. Output-set assignment algorithm was demonstrated on the occurrence matrix. It was found that only a part of the system was fully observable and the states in the regenerator was not observable with current measurement sets. Optimal locations for additional measurement were suggested by completing the whole output-set assignment algorithm of the system. Finally, to estimate unmeasured properties of feed mixture, a correlation method relating properties of mixture were investigated. Various correlation methods between complex petroleum properties were found from literature and interconnected to find the distribution function. The correlation model was validated by comparing the reaction results from model with another results from the chemical process simulator. The comparison showed slightly disagreed expectation result for LPG and LCO. It is assumed that uncertainties about catalyst in the reactor and process model in the simulator have caused this di_x000B_erence. Considering that point, we conclude that the correlation model exhibits an acceptable agreement with the results of Aspen HYSYS V8.4. The proposed approach provided insights into the FCC process and was found to be a suitable technique for process design, operation and even more applications such as optimization.Table of Contents v List of Figures vii 1 Introduction 1 1.1 Fluid catalytic cracking process . . . . . . . . . . . . . . . . . . . . . 1 1.2 Structural Analysis of Systems . . . . . . . . . . . . . . . . . . . . . . 3 1.3 FCC feed characterization with plant data . . . . . . . . . . . . . . . 4 1.4 The scope of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Modeling and simulation of a uid catalytic cracking (FCC) process 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Modeling of the Riser reactor . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Modeling of the feed inlet zone . . . . . . . . . . . . . . . . . 8 2.2.2 Reaction zone . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Regenerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 The dense bed phase . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.2 The dilute phase . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 Steady-state simulation results . . . . . . . . . . . . . . . . . . . . . . 25 2.6 Dynamic response analysis . . . . . . . . . . . . . . . . . . . . . . . . 26 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3 Structural observability analysis of FCC plant system 41 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2 Graph-theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.1 Concept of graph theory . . . . . . . . . . . . . . . . . . . . . 42 3.2.2 Modeling of FCC plant systems through graph and directed graph 43 3.3 Structural analysis of modeling releationships . . . . . . . . . . . . . 44 3.3.1 Structuring the modeling relationships of a system . . . . . . . 44 3.3.2 Attempt to solve the entire modeling relationships simultaneously 45 3.3.3 Finding an output-set assignment . . . . . . . . . . . . . . . . 45 3.3.4 Completing the assignment - Finding optimal place for addi- tional measurements . . . . . . . . . . . . . . . . . . . . . . . 46 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4 FCC feed characterization with plant data 55 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2 Experimental data on basic properties of petroleum fractions . . . . . 56 4.2.1 Boiling point and distillation curves . . . . . . . . . . . . . . . 56 4.3 Conversion of various distillation data . . . . . . . . . . . . . . . . . . 58 4.3.1 Riazi-Daubert method . . . . . . . . . . . . . . . . . . . . . . 59 4.3.2 Daubert's method . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.4 Conversion of various process data to distillation curve . . . . . . . . 61 4.5 Validation of the results . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.6 Conversion of the measured process data into model constants . . . . 70 4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5 Concluding remarks 75 Appendix A Nomenclature 77 Bibliography 83Docto

    ํ•œ ๋‹จ๊ณ„ ์ˆ˜์„ฑ๊ฐ€์Šค์ „ํ™˜๋ฐ˜์‘์šฉ ๋‹ˆ์ผˆ ๋ฒŒํฌ์ด‰๋งค์™€ CeO2์™€ SiO2์— ์˜ํ•œ ๊ทธ์˜ ์ด‰๋งคํŠน์„ฑ ๋ณ€ํ™”

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    Thesis(doctors) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€,2010.2.Docto
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