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    ๋ถˆ๊ฝƒ์ ํ™”์—”์ง„์—์„œ์˜ ๋…ธํ‚น์„ ์–ต์ œํ•˜๊ธฐ ์œ„ํ•œ ๋ฆฌ์„œ์น˜์˜ฅํƒ„๊ฐ€100 ๊ฐ€์†”๋ฆฐ-์—ํƒ„์˜ฌ ํ˜ผํ•ฉ์—ฐ๋ฃŒ ๋‚ด ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰ ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ์†กํ•œํ˜ธ.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฆฌ์„œ์น˜์˜ฅํƒ„๊ฐ€(Research octane number; RON)๊ฐ€ 100์œผ๋กœ ๊ณ ์ •๋œ ์ƒํ™ฉ์—์„œ ๊ฐ€์†”๋ฆฐ-์—ํƒ„์˜ฌ ํ˜ผํ•ฉ ์—ฐ๋ฃŒ ๋‚ด ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์„ ์ตœ์ ํ™”ํ•˜์—ฌ ๋ถˆ๊ฝƒ์ ํ™”(Spark ignition; SI)์—”์ง„์—์„œ์˜ ๋…ธํ‚น์„ ์–ต์ œํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๊ด€ํ•ด ๋…ผํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์—ํƒ„์˜ฌํ‘œ์ค€์—ฐ๋ฃŒ(Ethanol reference fuel; ERF) ๋‚ด ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰ ํ˜ผํ•ฉ์„ ๋ฐ”๊พธ์–ด๊ฐ€๋ฉฐ ์ด 4๊ฐ€์ง€ ํ…Œ์ŠคํŠธ์—ฐ๋ฃŒ๋ฅผ ์„ ์ •ํ•œ ๋‹ค์Œ, ๊ธ‰์†์••์ถ•์žฅ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ํ•ด๋‹น์—ฐ๋ฃŒ์˜ ์ ํ™”์ง€์—ฐ์„ ์ธก์ •ํ•˜์˜€๋‹ค. ์ธก์ •๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ ERF์˜ ๋…ธํ‚นํŠน์„ฑ์ด ์—”์ง„์šด์ „์กฐ๊ฑด์— ๋”ฐ๋ผ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์ž„์˜์˜ ์—”์ง„์šด์ „์กฐ๊ฑด์—์„œ ์ตœ์ ์˜ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์„ ๋„์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฒซ ๋ฒˆ์งธ ๋ชฉํ‘œ๋Š” ์—”์ง„์šด์ „์กฐ๊ฑด์— ๋”ฐ๋ผ ์‹ค๋ฆฐ๋” ๋‚ด ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ์ด ๋ณ€ํ™”ํ•  ์‹œ, ์„œ๋กœ ๋‹ค๋ฅธ ERF์—ฐ๋ฃŒ๋“ค์˜ ๋…ธํ‚น ํŠน์„ฑ์ด ๊ฐ๊ฐ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š”์ง€๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์˜จ๋„, ์••๋ ฅ ๋ฐ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ ํ™”์ง€์—ฐ์„ ์ธก์ •ํ•˜๊ณ , ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ์ด์— ๋Œ€ํ•œ ๊ฒฝํ—˜์‹์„ ๋„์ถœํ•˜์˜€๋‹ค. ๊ทธ ํ›„, 0-D 2๊ตฌ์—ญ SI์—”์ง„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์‚ฌ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์—”์ง„์šด์ „์กฐ๊ฑด ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ์˜ ํŽธ์ฐจ๋ฅผ ๊ณ„์‚ฐํ•œ ๋’ค, ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ์˜ ํŽธ์ฐจ๊ฐ€ ์ ํ™”์ง€์—ฐ์„ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”์‹œํ‚ค๋Š”์ง€ ์ •๋Ÿ‰์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ํ…Œ์ŠคํŠธ์—ฐ๋ฃŒ์˜ ์ ํ™”์ง€์—ฐ์€ RON์ด ๋™์ผํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์˜จ๋„ ๋ฐ ์••๋ ฅ์— ๋Œ€ํ•ด ์ƒ์ดํ•œ ์˜์กด์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒƒ์œผ๋กœ ๋ฐํ˜€์กŒ๋‹ค. ํŠนํžˆ, ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์ด ๋†’์„ ์ˆ˜๋ก ์ ํ™”์ง€์—ฐ์˜ ํ™œ์„ฑํ™” ์—๋„ˆ์ง€๊ฐ€ ๋†’๊ธฐ ๋•Œ๋ฌธ์— ์˜จ๋„ ํŽธ์ฐจ ฮ”T/T์— ๋” ๋ฏผ๊ฐํ•˜๋ฉฐ, ์••๋ ฅ ํŽธ์ฐจ ฮ”P/P์— ๋œ ๋ฏผ๊ฐํ•œ ๊ฒƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ ํ™”์ง€์—ฐ์— ๋Œ€ํ•œ ์˜จ๋„-์••๋ ฅ ํŽธ์ฐจ์˜ ์˜ํ–ฅ์€ ฮ”T/T๊ฐ€ (ฮณ-1)/ฮณ ฮ”P/P์™€ ๋™์ผํ•œ ๊ฒฝ์šฐ์—๋งŒ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰๊ณผ ๋ฌด๊ด€ํ•˜๋‹ค๋Š” ๊ฒƒ์ด ๋ฐํ˜€์กŒ๋‹ค. ๋ณธ ๊ฒฐ๊ณผ๋Š” Kalghatgi์˜ K ๊ฐ’ ์›๋ฆฌ์—์„œ ์˜ˆ์ธก ํ•œ ๊ฒƒ๊ณผ ์ผ์น˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ. ๊ฒฐ๊ณผ์ ์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉํ•œ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ์—”์ง„์šด์ „์กฐ๊ฑด์— ๋”ฐ๋ฅธ ์—ฐ๋ฃŒ ๋ณ„ ๋…ธํ‚น ํŠน์„ฑ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๊ด€์ ์„ ์ œ์‹œํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ƒ๊ธฐ ๊ฒฐ๊ณผ๋Š” ์™ธ๋ถ€ ๋ฐฐ๊ธฐ ๊ฐ€์Šค ์žฌ์ˆœํ™˜(External exhaust gas recirculation; external-EGR)์„ ์‚ฌ์šฉํ•˜๋Š” ์กฐ๊ฑด์—์„œ๋„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ™•์žฅ๋˜์—ˆ๋‹ค. ์•ž์„  ๋ฐฉ๋ฒ•๋ก ๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ, External-EGR์ด ์—”์ง„ ๋‚ด ์ ํ™”์ง€์—ฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํฌ์„ ํšจ๊ณผ ๋ฐ ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ ํšจ๊ณผ๋กœ ๋‚˜๋ˆ„์–ด ๊ฐœ๋ณ„์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, External-EGR์˜ ํฌ์„ํšจ๊ณผ๋Š” ์—ฐ๋ฃŒ ๋‚ด ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์ด 10 %์ผ ๋•Œ(ERF10) ๊ทน๋Œ€ํ™”๋œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ด ์›์ธ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ํšŒ๊ท€ ๋ถ„์„์„ ์ง„ํ–‰ํ•œ ๊ฒฐ๊ณผ, ํ™”์—ผ์ „ํŒŒ ๋™์•ˆ ๋ฏธ์—ฐ์†Œ๊ฐ€์Šค์—์„œ ๋ฐฉ์ถœ๋œ ์—ด๋Ÿ‰๊ณผ ํฌ์„ ํšจ๊ณผ์˜ ํฌ๊ธฐ ์‚ฌ์ด์— ๊ฐ•ํ•œ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ์Œ์„ ๋ฐœ๊ฒฌ ํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ERF10๊ฐ€ ํฌ์„ํšจ๊ณผ์— ๊ฐ€์žฅ ๋ฏผ๊ฐํ•œ ์ด์œ ๋Š” ํ™”์—ผ์ „ํŒŒ ๋™์•ˆ์˜ ์—ด ๋ฐฉ์ถœ๋Ÿ‰์ด ํฌ๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ์ดํ•ด๋œ๋‹ค. ํ•œํŽธ, External-EGR์— ์˜ํ•œ ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ ํšจ๊ณผ๋Š” ์—ฐ๋ฃŒ ๋‚ด ํ•จ๋Ÿ‰์ด ๋งŽ์„์ˆ˜๋ก ๋” ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” External-EGR์— ์˜ํ•œ ์˜จ๋„-์••๋ ฅ ํ”„๋กœํŒŒ์ผ์˜ ํŽธ์ฐจ๊ฐ€ ํ•ญ์ƒ ฮ”T/T>(ฮณ-1)/ฮณ ฮ”P/P์˜ ์กฐ๊ฑด์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, RON์ธก์ •์กฐ๊ฑด์—์„œ Externa-EGR์ด ์‚ฌ์šฉ๋  ์‹œ ์ตœ์ ์˜ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์€ 10 %๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด์— ๋”ํ•˜์—ฌ, ์—”์ง„์†๋„๊ฐ€ ๋ณ€ํ™”ํ•จ์— ๋”ฐ๋ผ ํ™”์—ผ์ „ํŒŒ ๋™์•ˆ์˜ ์—ด ๋ฐฉ์ถœ๋Ÿ‰์ด ์ค„์–ด๋“œ๋Š” ์ƒํ™ฉ์„ ๋ชจ์‚ฌํ•˜์—ฌ, ์ตœ์ ์˜ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์ด ์—”์ง„์†๋„์— ๋”ฐ๋ผ 0 %์—์„œ 10 %๊นŒ์ง€ ๋ณ€ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ๋„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์—”์ง„์šด์ „์กฐ๊ฑด์— ๋”ฐ๋ฅธ ์ตœ์  ์—ํƒ„์˜ฌํ•จ๋Ÿ‰์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋Œ€ํ•œ๋ฏผ๊ตญ ๋‚ด SI์—”์ง„์ฐจ๋Ÿ‰๊ตฐ์˜ ๋…ธํ‚น์–ต์ œ๋ฅผ ์ตœ๋Œ€ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ์ตœ์  ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ, 2018๋…„ ๋Œ€ํ•œ๋ฏผ๊ตญ ์ž๋™์ฐจํŒ๋งคํ†ต๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ, ๊ฐ SI์—”์ง„๋ชจ๋ธ ๋ณ„ ํŒ๋งค๋Ÿ‰์„ ๋„์ถœํ•˜์˜€๋‹ค. ๊ทธ ํ›„, 0-D 2 ๊ตฌ์—ญ SI์—”์ง„ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ๋…ธํ‚น๊ฒฝ๊ณ„์„ (Detonation borderline; DBL) ์กฐ๊ฑด์—์„œ์˜ ๊ฐ SI์—”์ง„๋ชจ๋ธ ๋‚ด ์—ด์—ญํ•™์  ์ƒํƒœ๋ฅผ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, 2018 ๋…„ ๋Œ€ํ•œ๋ฏผ๊ตญ์—์„œ ํŒ๋งค๋œ SI์—”์ง„์ฐจ๋Ÿ‰๊ตฐ์˜ 72 %๊ฐ€ ERF30์ด ์ตœ์ ์ธ ์˜์—ญ์—์„œ ์šดํ–‰๋˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์—”์ง„์†๋„๊ฐ€ ๋นจ๋ผ์งˆ์ˆ˜๋ก DBL์กฐ๊ฑด์—์„œ์˜ ์‹ค๋ฆฐ๋” ๋‚ด ์••๋ ฅ์ด ์ฆ๊ฐ€ํ•˜์—ฌ ERF30์ด ์ตœ์ ์ธ ์˜์—ญ์—์„œ ์šดํ–‰๋˜๋Š” SI์—”์ง„์ฐจ๋Ÿ‰์ด๊ตฐ์ด ๋” ๋งŽ์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ™•์žฅํ•˜์—ฌ, ๋ฏธ๋ž˜์— External-EGR์ด ํ™œ๋ฐœํžˆ ์‚ฌ์šฉ๋˜๋Š” ์ƒํ™ฉ์—์„œ ์ตœ์ ์˜ ์—ํƒ„์˜ฌํ•จ๋Ÿ‰์ด ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”ํ•  ๊ฒƒ์ธ์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, Extern-EGR์ด ๋„์ž…๋จ์— ๋”ฐ๋ผ ์ตœ์ ์˜ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์€ ERF10์— ๊ฐ€๊นŒ์›Œ ์งˆ ๊ฒƒ์ด๋ฉฐ, ์ด๋Š” ERF10์—์„œ External-EGR์˜ ํฌ์„ ํšจ๊ณผ๊ฐ€ ๊ทน๋Œ€ํ™”๋˜๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ๋ฐํ˜€์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—”์ง„์†๋„๊ฐ€ ๋นจ๋ผ์ง์— ๋”ฐ๋ผ ํฌ์„ ํšจ๊ณผ๊ฐ€ ์ค„์–ด๋“ค๊ธฐ ๋•Œ๋ฌธ์—, ๊ณ RPM์˜์—ญ์—์„œ๋Š” External-EGR์— ๊ด€๊ณ„์—†์ด ERF30๊ฐ€ ์—ฌ์ „ํžˆ ์ตœ์ ์ธ ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์–‘ํ•œ ์—”์ง„์šดํ–‰์กฐ๊ฑด์— ๋”ฐ๋ผ ์—ฐ๋ฃŒ ๋ณ„ ์ ํ™”์ง€์—ฐ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜๊ณ  ๋ณธ ๊ฒฐ๊ณผ์™€ ๋…ธํ‚น ํ˜„์ƒ ์‚ฌ์ด์˜ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ์ œ์‹œํ•œ ์ตœ์ดˆ์˜ ์‹คํ—˜์—ฐ๊ตฌ์ด๋‹ค. ๋˜ํ•œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ž„์˜์˜ ์—”์ง„์šดํ–‰์กฐ๊ฑด์—์„œ ์ตœ์ ์˜ ์—ํƒ„์˜ฌํ•จ๋Ÿ‰์„ ๊ฒฐ์ •ํ•˜๋Š” ๋‹ค์ด์–ด๊ทธ๋žจ์„ ์ œ์•ˆํ•˜์—ฌ, ํ–ฅํ›„ ๋Œ€ํ•œ๋ฏผ๊ตญ์— ๊ฐ€์†”๋ฆฐ-์—ํƒ„์˜ฌ ํ˜ผํ•ฉ์—ฐ๋ฃŒ๊ฐ€ ๋„์ž…๋  ์‹œ ์ตœ์ ์—ํƒ„์˜ฌ ํ•จ๋Ÿ‰์„ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ๋„ ์˜์˜๋ฅผ ์ง€๋‹Œ๋‹ค.This dissertation is devoted to optimize the ethanol content in gasoline-ethanol blend fuel with a fixed RON of 100, for maximizing antiknock characteristics in spark-ignition (SI) engines of light duty fleet in South Korea. To this end, the ethanol reference fuels (ERFs), which is a blend of ethanol and primary reference fuel, with varying ethanol content were chosen as surrogate fuels, then their auto-ignition characteristics were measured on rapid compression machine (RCM). The measurement was manipulated to quantitatively analyze the dependency of knocking characteristics of each ERF on engine operating condition from the aspect of chemical kinetics, whose results will eventually derive the optimal ethanol content with varying engine operating condition. The first objective of this study was to analyze the dependency of knocking characteristic of various ERFs on temperatureโ€“pressure profile of engine operating condition. To this end, an empirical correlation of the ignition delay with varying temperature, pressure, and ethanol content was derived based on a regression analysis using ignition delay measurement. Then, the deviation in the temperatureโ€“pressure profile owing to changes in various engine operating parameters was calculated using a 0-D two zone SI engine simulation. Based on these results, it was quantified how the individual effects of temperature deviation and pressure deviation affect the ignition delay of each tested fuel. Consequently, it was found that the ignition delay of the tested fuels exhibits different dependencies on the temperature and pressure despite a fixed RON. In particular, the ignition delay of the fuel with the higher ethanol content is more sensitive to the temperature deviation, ฮ”T/T, owing to its higher activation energy, whereas it is less sensitive to the pressure deviation, ฮ”P/P. Moreover, it was revealed that the effect of temperatureโ€“pressure deviation on the ignition delay is independent of the ethanol content, if and only if ฮ”T/T is equal to (ฮณ-1)/ฮณ*ฮ”P/P, where ฮณ is the specific heat ratio of the end gas. The results of this study were verified to be consistent with those predicted from Kalghatgis K value principle. Consequently, both approaches exhibited the same result on the knocking characteristics of various ERFs under varying engine operating conditions. The above results were expanded to cover the engine operating condition with external-exhaust-gas-recirculation (EGR), where the thermodynamic state of end gas is significantly varied with the rate of dilution. Similar as the analysis on the effect of temperature-pressure effect, the total effect of external-EGR on the ignition delay of the simulated end gas was divided into two effects affecting the auto-ignition behavior: dilution effect and temperatureโ€“pressure profile effect, then each effect was evaluated separately. As a result, the dilution effect by adding external-EGR was maximized when the fuel is ERF10. With a regression analysis, it was found that there is the correlation between the amount of dilution effect and the amount of pre-heat release in the end gas during a flame propagation; therefore, it is understood that ERF10 is the most sensitive to dilution effect due to its pre-heat release characteristic. On the other hand, ERF with lower ethanol content was more sensitive to temperatureโ€“pressure effect by external-EGR on the ignition delay. It is found out that the deviation of temperatureโ€“pressure profile by external-EGR always satisfies the condition of ฮ”T/T>(ฮณ-1)/ฮณ*ฮ”P/P; therefore, the temperatureโ€“pressure effect of the fuel with lower ethanol content is higher than the other fuels, due to its lower sensitivity of ignition delay on temperature. Consequently, ERF10 has the highest external-EGR sensitivity in anti-knock behavior at RON test condition, and it is further discussed that the optimum ethanol content for external-EGR strategy could vary from ERF0 to ERF10 according to engine speed. Based on the understanding of the effect of engine operating condition on the knocking characteristics of various ERFs, the ethanol content was optimized for maximizing knock suppression of light duty fleet in South Korea. In this regard, the sales volumes of each SI engine models was achieved from the statistics for model year 2018 in South Korea, which can be found from the database of KAMA. Then, the thermodynamic state of each SI engine design at detonation borderline (DBL) condition with varying engine speed were calculated using 0-D two-zone SI engine model. As a result, it was found that 72 % of SI engines sold for model year 2018 operates on the thermodynamic state where ERF30 is optimal for knock suppression. Moreover, as engine speed get faster, in-cylinder pressure at DBL condition increases, and more SI engines becomes operated on the thermodynamic state where ERF30 is optimal. The analysis was expanded to how the optimal ethanol content changes on the scenario that external-EGR is widely used in the future. Consequently, it was found that the introduction of external-EGR will makes the optimal ethanol content to be ERF10, which is the most sensitive to dilution effect of external-EGR. However, as the engine speed gets faster, the priority of ERF10 on dilution effect was surpassed by the priority of ERF30 on pressure effect, thus ERF30 still be optimal at the faster engine speed regardless of external-EGR. This is the first experimental study to analyze the effect of engine operating condition on the ignition delay of various ERFs and develop quantitative correlations between ignition delay and knocking phenomena, reflecting operating conditions of modern engines. Moreover, this study suggested the useful diagram determining the optimal ethanol content at an arbitrary engine operating condition, which can be manipulated for government to determine the optimal ethanol content in gasoline-ethanol blend fuel suitable for the countries.Chapter 1. Introduction 1 1.1 Research background 1 1.1.1 Regulations on carbon dioxide emissions and petroleum consumption from light duty vehicles 1 1.1.2 Advance in engine design for high thermal efficiency 5 1.1.3 Knocking 8 1.1.4 Ethanol as an antiknock additive 11 1.1.5 Optimal ethanol content for a fixed RON 13 1.2 Literature review 15 1.2.1 New knock metrics 15 1.2.2 Optimization of fuel composition 17 1.3 Research objective and implication 18 1.4 Summary 19 Chapter 2. Methodology 21 2.1 Fuel matrix 21 2.2 0-D two-zone SI engine model 22 2.2.1 Determination of representative engine operating condition 22 2.2.2 Governing equation for the model 23 2.3 Rapid compression machine experiment 30 2.3.1 Measurement procedure and data processing 30 2.3.2 Active manipulation of compression process for simulating the flame propagation process in SI engine 38 Chapter 3. Effect of temperaturepressure profile of the end gas on optimal ethanol content 43 3.1 Engine simulation for varying engine operating conditions 44 3.2 Regression analysis on the ignition delay data 48 3.3 Quantitative analysis on the effect of engine operating condition on knocking characteristics 56 3.4 Uncertainty Quantification of E_T, E_P, and E_TP 57 3.5 The effect of engine operating condition on the knocking characteristics 58 3.6 Dependency of optimal ethanol content on the engine design 62 3.7 Analogy with K value principle 67 3.8 Summary 71 Chapter 4. Effect of external exhaust gas recirculation on optimal ethanol content 74 4.1 Engine simulation for the engine operating condition with external-EGR 76 4.2 Regression analysis on the ignition delay data 78 4.3 Quantitative analysis on the effect of engine operating condition on knocking characteristics 84 4.4 The effect of external-EGR on the knocking characteristics 85 4.4.1 Dilution effect 85 4.4.2 Temperature and pressure profile effect 88 4.4.3 The total effect of external-EGR 90 4.5 Dependency of the effect of external-EGR on engine speed 91 4.6 Summary 94 Chapter 5. Ethanol content optimization for light duty fleet in South Korea 95 5.1 Optimal ethanol content diagram 96 5.2 Thermodynamic state of SI engines in South Korea 98 5.3 Optimal ethanol content maximizing knock suppression in South Korea 103 5.4 Summary 106 Chapter 6. Conclusion 107 6.1 Knocking analysis based on the ignition delay 107 6.2 Optimal ethanol content for South Korea 110 6.3 Future work 111 Nomenclature 113 Abbreviation 118 Reference 121 ๊ตญ๋ฌธ์ดˆ๋ก 134Docto

    ๊ฐ„์œ ๋ฆฌ ์Œ์˜ ๊ฒฐ์ ˆ์—์„œ ์ˆ˜์ˆ  ์ „ ์กฐ์ง๊ฒ€์‚ฌ๊ฐ€ ํ•„์š”ํ•œ๊ฐ€์— ๋Œ€ํ•œ ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜ํ•™๊ณผ ๋‚ด๊ณผํ•™, 2016. 2. ์ด์ถ˜ํƒ.Introduction: Percutaneous needle aspiration or biopsy (PCNA or PCNB) is an established diagnostic technique that has a high diagnostic yield. However, its role in the diagnosis of nodular ground-glass opacities (nGGOs) is controversial, and the necessity of preoperative histologic confirmation by PCNA or PCNB in nGGOs has not been well addressed. Methods: We here evaluated the rates of malignancy and surgery-related complications, and the cost benefits of resecting nGGOs without prior tissue diagnosis when those nGGOs were highly suspected for malignancy based on their size, radiologic characteristics, and clinical courses. Patients who underwent surgical resection of nGGOs without preoperative tissue diagnosis from January 2009 to October 2013 were retrospectively analyzed. Results: Among 356 nGGOs of 324 patients, 330 (92.7%) nGGOs were resected without prior histologic confirmation. The rate of malignancy was 95.2% (314/330). In the multivariate analysis, larger size was found to be an independent predictor of malignancy (odds ratio, 1.08695% confidence interval, 1.001-1.178, p =0.047). A total of 324 (98.2%) nGGOs were resected by video-assisted thoracoscopic surgery (VATS), and the rate of surgery-related complications was 6.7% (22/330). All 16 nGGOs diagnosed as benign nodules were resected by VATS, and only one patient experienced postoperative complications (prolonged air leak). Direct surgical resection without tissue diagnosis significantly reduced the total costs, hospital stay, and waiting time to surgery. Conclusions: With careful selection of nGGOs that are highly suspicious for malignancy, surgical resection of nGGOs without tissue diagnosis is recommended as it reduces costs and hospital stay.Introduction 1 Material and Methods 5 Study population and study design 5 Radiologic evaluation 5 Total costs, days of hospitalization, and waiting time 7 Statistical analysis 8 Results 9 Patient characteristics 9 Radiologic characteristics 12 Surgical procedure and complications related to surgery 14 Pathologic diagnosis 14 Advantages on costs, hospital stay, and waiting time 16 Discussion 18 Conclusions 23 References 24 Abstract in Korean 31Maste

    Modeling charge transport in organic hosts: BCP and NBPhen

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2020. 2. ๊น€์žฅ์ฃผ.์ „์ž ์ด๋™์ธต ๋ฌผ์งˆ๋กœ ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๋Š” BCP์™€ NBPhen ๋ถ„์ž๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ์ „ํ•˜ mobility ๊ฐ’์„ ๊ฐ€์ง„๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ์‹คํ—˜์ ์œผ๋กœ ์ธก์ •๋œ NBPhen์˜ ์ „์ž mobility ๊ฐ’์€ BCP์˜ ๊ฒฝ์šฐ๋ณด๋‹ค 10๋ฐฐ ๊ฐ€๋Ÿ‰ ๋†’๊ฒŒ ์ธก์ •๋˜๋ฉฐ, ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•œ ๊ฒฐ๊ณผ์—์„œ๋„ 9๋ฐฐ ๊ฐ€๋Ÿ‰ ๋†’์€ ๊ฐ’์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์™€ ๊ฐ™์€ mobility์™€ ๋ถ„์ž ๊ตฌ์กฐ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๊ทœ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ microscopic simulation ๋ฐฉ๋ฒ•๋ก ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•๋ก ์—์„œ ์œ ๊ธฐ ๋ฐ•๋ง‰์„ ๋ชจ์‚ฌํ•œ ๋ฌด์ •ํ˜• ์‹œ์Šคํ…œ์„ ๋งŒ๋“ค๊ธฐ ์œ„ํ•˜์—ฌ ๋ถ„์ž ๋™์—ญํ•™ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ•์ด ์‚ฌ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ๊ฐ ๋ถ„์ž์˜ ์ „์ž ํŠน์„ฑ ์ค‘ transfer integral, site energy, reorganization energy๋ฅผ ๊ตฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ฐ€๋„ ๋ฒ”ํ•จ์ˆ˜ ์ด๋ก ๊ณผ Polarizable force field ๋ฐฉ๋ฒ•๋ก ์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ƒ์ž ์•ˆ์—์„œ ์ „ํ•˜์˜ ๋™์—ญํ•™ ํŠน์„ฑ์„ ๋ชจ์‚ฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ Kinetic Monte Carlo ๋ฐฉ๋ฒ•๋ก ์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. BCP์™€ NBPhen ์‚ฌ์ด์˜ ์ „ํ•˜ mobility ์ฐจ์ด์˜ ์›์ธ์€ ํฌ๊ฒŒ 3๊ฐ€์ง€ ํ•ญ๋ชฉ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ ์ด๋Š” energetic disorder, transfer integral ๊ทธ๋ฆฌ๊ณ  reorganization energy๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. Energetic disorder์˜ ๊ด€์ ์—์„œ๋Š”, ์ƒ๋Œ€์ ์œผ๋กœ ๋” ํฐ ๋ถ„์ž ์Œ๊ทน์ž ๋ถ„ํฌ ํŠน์„ฑ์„ ๊ฐ–๋Š” NBPhen์˜ ๊ฒฝ์šฐ energetic disorder ๊ฐ’์ด ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์ „ํ•˜ mobility ํŠน์„ฑ์— ์Œ์˜ ํšจ๊ณผ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค. ํ•˜์ง€๋งŒ transfer integral๊ณผ reorganization energy์˜ ๊ด€์ ์—์„œ๋Š” transfer integral์˜ ํ‰๊ท  ๊ฐ’์ด ํฌ๊ณ  reorganization energy ๊ฐ’์ด ์ž‘์€ NBPhen์ด ์ „ํ•˜ mobility ๊ฐ’์— ์žˆ์–ด์„œ ์–‘์˜ ํšจ๊ณผ๋ฅผ ๊ฐ–๋Š”๋ฐ, ์ด๋Š” energetic disorder๋กœ๋ถ€ํ„ฐ ์•ผ๊ธฐ๋˜๋Š” ์Œ์˜ ํšจ๊ณผ๋ฅผ ๋›ฐ์–ด ๋„˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฒฐ๊ณผ์ ์œผ๋กœ NBPhen์˜ mobility๊ฐ€ ํฌ๊ฒŒ ์ธก์ •๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„ ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด Phenanthroline ์ค‘์‹ฌ๋ถ€๋ฅผ ๊ณตํ†ต์œผ๋กœ ๊ฐ€์ง€๋ฉฐ ๋ฉ”ํ‹ธ๊ธฐ์™€ ๋‚˜ํ”„ํƒˆ๋ Œ๊ธฐ์—์„œ ์น˜ํ™˜๊ธฐ์˜ ์ฐจ์ด์ ์„ ๊ฐ–๋Š” BCP์™€ NBPhen ๋ถ„์ž์˜ mobility ์ฐจ์ด์— ๋Œ€ํ•ด์„œ๋งŒ ๋ถ„์„์„ ํ•˜์˜€์œผ๋‚˜, ์ด์— ๋” ๋‚˜์•„๊ฐ€ ๋‹ค์–‘ํ•œ ์ข…๋ฅ˜์˜ ์น˜ํ™˜๊ธฐ์— ๋”ฐ๋ฅธ mobility ์ฐจ์ด๋ฅผ ํ†ตํ•ด ์ผ๋ฐ˜ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๋ฉฐ, ๋†’์€ mobility ํŠน์„ฑ์„ ๊ฐ–๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‚ฎ์€ energetic disorder์™€ reorganization energy ๊ฐ’์„ ๊ฐ€์ง€๋ฉฐ ๋†’์€ transfer integral ํ‰๊ท  ๊ฐ’์„ ๊ฐ€์ง€๋„๋ก ๋ถ„์ž๋ฅผ ์„ค๊ณ„ํ•ด์•ผ ํ•  ๊ฒƒ์ด๋ฉฐ, ์ด๋Š” ์น˜ํ™˜๊ธฐ์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ๋ถ„์ž์˜ ์ „์ž ๊ตฌ์กฐ ์ฐจ์ด ๋ถ„์„๊ณผ ๋ฌด์ •ํ˜• ์‹œ์Šคํ…œ ๋‚ด์—์„œ์˜ ์ง‘ํ•ฉ์ ์ธ ํŠน์„ฑ (energetic disorder, transfer integral) ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ด๋ฃจ์–ด ์งˆ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.The charge carrier mobility of BCP and NBPhen which are commonly used as an electron transport material, are different from each other. Specifically, experimentally measured electron mobility of NBPhen is nearly 10 times larger than that of BCP and 9 times larger in charge carrier transport simulation. In order to unveil the relation between mobility and molecular structure, microscopic simulation method was used. In this methodology, amorphous system was constructed by using Molecular Dynamics (MD) simulation and electronic properties of molecule such as transfer integral, site energy of each molecular site and reorganization energy were calculated by using DFT method and Polarizable force field. Finally, charge dynamics was explicitly simulated by using Kinetic Monte Carlo method in the simulation box. The reason of difference in mobility can be categorized into three terms: energetic disorder, transfer integral and reorganization energy term. In the respective of energetic disorder, larger molecular dipole distribution of NBPhen which results in high energetic disorder has negative effect on charge carrier mobility. However, in the respective of transfer integral and reorganization energy, higher average value of transfer integral and smaller value of reorganization energy of NBPhen have positive effect on charge carrier mobility, which exceed the negative effect from energetic disorder. This result is originated from the difference in molecular structure between BCP and NBPhen. To sum up, from this research, we only analyzed the difference between BCP and NBPhen molecule that have Phenanthroline core in common and difference in substituents. However, by analyzing the mobility difference with different substituent groups, the analysis of this research can be generalized. Furthermore, in order to obtain high mobility value, the molecule should be designed to have low energetic disorder and reorganization value, and high average value of transfer integral which can be achieved by analyzing the difference in electronic properties of molecules with different substituent and collective property of amorphous system.Chapter 1 Introduction 6 1.1 OLEDs 6 1.2 Charge transfer theory 8 1.3 Brief history of charge transport simulation 11 1.4 Motivation 17 1.5 Contents of the thesis 18 Chapter 2 Microscopic charge transport simulation 19 2.1 Workflow of Microscopic charge transport simulation 19 2.2 Material morphology 22 2.3 Reorganization energy 33 2.4 Site energy 36 2.5 Transfer integrals 40 2.6 Charge transfer rate equation 41 2.7 Solving the master equationโ€ฏ; KMC method 46 2.8 Finite-size scaling of charge carrier mobility 51 Chapter 3 Application: Understanding mobility difference between BCP and NBPhen molecule 54 3.1 Introduction 54 3.2 Simulation results and analysis 54 3.3 Summary 70 Chapter 4 Summary and Conclusion 72 References 73 Curriculum Vitae 77 ์š”์•ฝ (๊ตญ๋ฌธ ์ดˆ๋ก) 79Maste

    Etopside์— ์˜ํ•œ HeLa ์„ธํฌ์‚ฌ๋ฉธ ๊ธฐ๊ฐ„ ์ค‘ acetyltransferase์ธ ARD1์˜ cytoplasmic translocation ์œ ๋„ํšจ๊ณผ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    Thesis(master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์•ฝํ•™๊ณผ ์˜์•ฝ์ƒ๋ช…๊ณผํ•™์ „๊ณต,2005.Maste

    ์ƒ์ฒด ๊ฐ„์ด์‹์—์„œ ๊ฒฝ๋„์˜ ์ง€๋ฐฉ๊ฐ„์ด ์žˆ๋Š” ์ด์‹ํŽธ์˜ ๊ฐ„์žฌ์ƒ ๋Šฅ๋ ฅ

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    Thesis(master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์™ธ๊ณผํ•™์ „๊ณต,2005.Maste

    Genome-wide association study of non-tuberculous mycobacterial pulmonary disease

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021.8. ์ž„์žฌ์ค€.Background The prevalence and incidence of non-tuberculous mycobacterial pulmonary disease (NTM-PD) are increasing in different parts of the world including South Korea. Despite ubiquitous exposure to NTM, a subset of people develop NTM-PD. Moreover, the presence of ethnic disparity, familial clustering, and the distinct phenotype of NTM-PD indicate the presence of genetic predispositions to this disease. However, the genetic factors associated with susceptibility to NTM-PD have been unclear. We aimed to find genetic variants in individuals with NTM-PD by a genome-wide association study (GWAS). Methods We performed a GWAS with Korean patients with NTM-PD and controls from the Healthy Twin Study, Korea cohort. Candidate single-nucleotide polymorphisms (SNPs) from this discovery cohort were then validated in another Korean replication cohort. The Genotype-Tissue Expression (GTEx) data set was utilized to find expression quantitative trait loci (eQTL). In addition, we conducted the PrediXcan analysis to predict the candidate gene expression levels using our imputed genotype data and the GTEx data set. We performed a Mendelian randomization (MR) analysis to evaluate the causal effect related to the expression level of a candidate gene on the risk of development of NTM-PD. Moreover, we performed a transcriptome profiling analysis with publicly accessible data sets to identify differentially expressed genes (DEGs) in NTM-infected macrophages. Results Through the GWAS with 403 patients with NTM-PD and 306 controls, we found rs849177 on chromosome 7p13 as the candidate SNP associated with susceptibility to NTM-PD (odds ratio, 2.34; 95% confidence interval [CI], 1.71 ~ 3.21; p=1.36ร—10โˆ’7). Its association was replicated in the independent cohort of 184 individuals with NTM-PD and 1,680 controls. When the Fisherโ€™s method was applied, the combined p-value for both cohorts was 4.92ร—10โˆ’8. The eQTL analysis revealed that a risk allele (C) at rs849177 was associated with decreased expression levels of STK17A, a proapoptotic gene. According to the PrediXcan analysis, STK17A was the most significant DEG between individuals with NTM-PD and controls. A causal effect of STK17A on the development of NTM-PD was found in the MR analysis (ฮฒ, -4.627; 95% CI, -8.768 ~ -0.486; p=0.029). According to the transcriptome profiling analysis, the expression levels of STK17A increased significantly in macrophages infected with NTM. Conclusions We found rs849177 on chromosome 7p13 as a susceptibility marker for NTM-PD in a Korean population. This genetic variant might be related to susceptibility to NTM-PD by modifying the expression level of a proapoptotic gene.์„œ๋ก : ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์€ ํ† ์–‘๊ณผ ๊ฐ•๋ฌผ ๋“ฑ์˜ ์ž์—ฐํ™˜๊ฒฝ์— ๋„๋ฆฌ ๋ถ„ํฌํ•˜๊ณ  ์žˆ์œผ๋ฏ€๋กœ ๊ฑฐ์˜ ๋ชจ๋“  ์‚ฌ๋žŒ๋“ค์ด ์ข…์ข… ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์„ ํก์ž…ํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์ด๋“ค ์ค‘ ์ผ๋ถ€์—์„œ๋งŒ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ์ ์€ ์ด ์งˆํ™˜์— ๋Œ€ํ•œ ์œ ์ „์  ์†Œ์ธ์ด ์กด์žฌํ•œ๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ตœ๊ทผ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž๋“ค์˜ ์œ ์ „์  ๊ฒฐํ•จ ์กด์žฌ ์—ฌ๋ถ€๋ฅผ ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋ฐฉ๋ฒ•: ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž์™€ ์Œ๋‘ฅ์ด ์ฝ”ํ˜ธํŠธ์— ์ฐธ์—ฌํ•œ ๊ฑด๊ฐ•์ธ์„ ๋Œ€์กฐ๊ตฐ์œผ๋กœ ์‚ผ์•„ ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ–ˆ๋‹ค. ์Œ๋‘ฅ์ด๋Š” ๋ฌด์ž‘์œ„๋กœ ๋ฐฐ์ •๋œ 1๋ช…๋งŒ ๋Œ€์กฐ๊ตฐ์œผ๋กœ ์ฐธ์—ฌํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์—ฐ๊ตฌ์—์„œ ํ›„๋ณด๋กœ ์„ ์ •๋œ ์œ ์ „์  ๋ณ€์ด์— ๋Œ€ํ•ด์„œ๋Š” ๋…๋ฆฝ์ ์ธ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž-๋Œ€์กฐ๊ตฐ ์ฝ”ํ˜ธํŠธ์—์„œ ์—ฐ๊ด€์„ฑ์ด ์žฌํ˜„๋˜๋Š”์ง€ ํ™•์ธํ–ˆ๋‹ค. Genotypeโ€“Tissue Expression (GTEx) ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ์ด์šฉํ•ด์„œ ๋ฐœํ˜„ ์–‘์  ํ˜•์งˆ ์œ ์ „์ž์ž๋ฆฌ(expression quantitative trait loci, eQTL)๋ฅผ ์‹๋ณ„ํ•˜๊ณ , ๋ฉ˜๋ธ ๋ฌด์ž‘์œ„๋ฐฐ์ •(Mendelian randomization) ๋ถ„์„์„ ์‹œํ–‰ํ–ˆ๋‹ค. ๊ฒฐ๊ณผ: 403๋ช…์˜ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž์™€ 306๋ช…์˜ ๋Œ€์กฐ๊ตฐ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด์„œ 7๋ฒˆ ์—ผ์ƒ‰์ฒด ๋‹จ์™„(7p13)์— ์œ„์น˜ํ•œ ์œ ์ „์  ๋ณ€์ด์ธ rs849177์ด ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์— ๋Œ€ํ•œ ๊ฐ์ˆ˜์„ฑ๊ณผ ๊ด€๋ จ๋œ ์œ ์ „์ž ๋ณ€์ด๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค (OR, 2.34; 95% CI, 1.71 to 3.21; p=1.36ร—10โˆ’7). ์ด ์—ฐ๊ด€์„ฑ์€ ๋…๋ฆฝ์ ์ธ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž-๋Œ€์กฐ๊ตฐ ์ฝ”ํ˜ธํŠธ์—์„œ ์žฌํ˜„๋˜์—ˆ๊ณ , ๋‘ ์ฝ”ํ˜ธํŠธ๋ฅผ ํ•จ๊ป˜ ๋ถ„์„ํ–ˆ์„ ๋•Œ์˜ ์œ ์˜ํ™•๋ฅ ์€ 4.92ร—10โˆ’8์˜€๋‹ค. eQTL ๋ถ„์„์„ ํ†ตํ•ด rs849177์˜ ์œ„ํ—˜ ๋Œ€๋ฆฝ์œ ์ „์ž(risk allele)๊ฐ€ ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ์œ ๋ฐœํ•˜๋Š” ์œ ์ „์ž์ธ STK17A์˜ ๋ฐœํ˜„ ๊ฐ์†Œ์™€ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ๋ฉ˜๋ธ ๋ฌด์ž‘์œ„๋ฐฐ์ • ๋ถ„์„์„ ํ†ตํ•ด STK17A๊ฐ€ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜ ๋ฐœ์ƒ์— ์ธ๊ณผ๊ด€๊ณ„๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค (ฮฒ, -4.627; 95% CI, -8.768 ~ -0.486; p=0.029). ๊ฒฐ๋ก : 7๋ฒˆ ์—ผ์ƒ‰์ฒด ๋‹จ์™„(7p13)์— ์œ„์น˜ํ•œ ์œ ์ „์  ๋ณ€์ด๊ฐ€ ํ•œ๊ตญ์ธ์˜ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์— ๋Œ€ํ•œ ๊ฐ์ˆ˜์„ฑ๊ณผ ์—ฐ๊ด€๋˜์–ด ์žˆ์—ˆ๋‹ค. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์ž๋Š” ์ด ์œ ์ „์  ๋ณ€์ด์˜ ์œ„ํ—˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€๋ฐ, ์ด ์œ„ํ—˜ ๋Œ€๋ฆฝ์œ ์ „์ž๋Š” ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ์œ ๋ฐœํ•˜๋Š” ์œ ์ „์ž์ธ STK17A์˜ ๋ฐœํ˜„ ๊ฐ์†Œ์™€ ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์— ๊ฐ์—ผ๋œ ๋Œ€์‹์„ธํฌ์˜ ์„ธํฌ์ž๋ฉธ์‚ฌ๊ฐ€ ์ •์ƒ์ ์œผ๋กœ ์ง„ํ–‰๋˜์ง€ ์•Š์•„ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์— ๋Œ€ํ•œ ๊ฐ์ˆ˜์„ฑ์ด ์ฆ๊ฐ€ํ•  ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค.I. ์„œ ๋ก  1 1. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์˜ ์„ธ๊ณ„์ ์ธ ์ฆ๊ฐ€ ์ถ”์„ธ 1 2. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜ ์น˜๋ฃŒ์˜ ์–ด๋ ค์›€ 6 3. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์œผ๋กœ ์ธํ•œ ์‚ฌ๋ง๋ฅ  ๋ฐ ์‚ฌํšŒโˆ™๊ฒฝ์ œ์  ๋น„์šฉ์˜ ์ฆ๊ฐ€ 6 4. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜์— ๋Œ€ํ•œ ์œ ์ „์  ์†Œ์ธ 8 5. ์ตœ๊ทผ์˜ ์—‘์†œ ์—ผ๊ธฐ์„œ์—ด ๋ถ„์„ ์—ฐ๊ตฌ์™€ ์ œํ•œ์  10 6. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 14 II. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 15 1. ๋ฐœ๊ตด ์ฝ”ํ˜ธํŠธ(discovery cohort) 15 2. ์žฌํ˜„ ์ฝ”ํ˜ธํŠธ(replication cohort) 16 3. ๋ฐœ๊ตด ์ฝ”ํ˜ธํŠธ์—์„œ ์ˆ˜ํ–‰๋œ ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์—ฐ๊ตฌ 17 4. ์žฌํ˜„ ์ฝ”ํ˜ธํŠธ๋ฅผ ํ†ตํ•œ ๊ฒ€์ฆ(validation) 21 5. ๋‹ค์ฐจ์› ์ฒ™๋„๋ฒ•(multidimensional scaling, MDS) 23 6. ์ง€์—ญ ํ”Œ๋กฏ(regional plot) 24 7. ์ „์žฅ์œ ์ „์ฒด ๋ณตํ•ฉ ํ˜•์งˆ ๋ถ„์„(genome-wide complex trait analysis) 24 8. ํ† ํด๋กœ์ง€ ์—ฐ๊ด€ ๋„๋ฉ”์ธ ๋ถ„์„(topologically associating domain analysis) 25 9. ๋ฐœํ˜„ ์–‘์  ํ˜•์งˆ ์œ ์ „์ž์ž๋ฆฌ(expression quantitative trait loci, eQTL)์™€ PrediXcan ๋ถ„์„ 25 10. 2-ํ‘œ๋ณธ ๋ฉ˜๋ธ ๋ฌด์ž‘์œ„๋ฐฐ์ •(two-sample Mendelian randomization) ๋ถ„์„ 26 11. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์— ๊ฐ์—ผ๋œ ๋Œ€์‹์„ธํฌ(macrophage)์˜ ์ „์‚ฌ์ฒด(transcriptome) ํ”„๋กœํŒŒ์ผ๋ง ๋ถ„์„ 29 12. Gene Ontology enrichment ๋ถ„์„ 30 13. ๊ธฐ์กด์— ์•Œ๋ ค์ง„ ์œ ์ „์ž์— ๋Œ€ํ•œ ํ”ํ•œ ๋ณ€์ด(common variant) ๋ถ„์„๊ณผ ์œ ์ „์ž ์„ธํŠธ(gene set) ๋ถ„์„ 30 III. ๊ฒฐ ๊ณผ 32 1. ์ „์žฅ์œ ์ „์ฒด์—ฐ๊ด€๋ถ„์„์œผ๋กœ ์–ป์€ ์ž ์žฌ์  ํ›„๋ณด SNP 32 2. ์ž ์žฌ์  ํ›„๋ณด SNP์˜ ๊ฒ€์ฆ 45 ๏ผ“. 3์ฐจ์› ๊ตฌ์กฐ ๋ถ„์„ 48 ๏ผ”. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜๊ณผ ์—ฐ๊ด€๋œ ํ›„๋ณด ์œ ์ „์ž 49 ๏ผ•. ํ›„๋ณด ์œ ์ „์ž๊ฐ€ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜ ๋ฐœ์ƒ์— ๋ฏธ์น˜๋Š” ์ธ๊ณผ์  ์˜ํ–ฅ(causal effect) 56 ๏ผ–. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์— ๊ฐ์—ผ๋œ ๋Œ€์‹์„ธํฌ์—์„œ ํ›„๋ณด ์œ ์ „์ž์˜ ์ฐจ๋ณ„์  ๋ฐœํ˜„(differential expression) 58 ๏ผ—. ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท ์— ๊ฐ์—ผ๋œ ๋Œ€์‹์„ธํฌ์—์„œ ์„ธํฌ์ž๋ฉธ์‚ฌ(apoptosis) ๊ฒฝ๋กœ์˜ ๊ฐ•ํ™”(enrichment) 60 ๏ผ˜. ๊ธฐ์กด์— ์•Œ๋ ค์ง„ ๋น„๊ฒฐํ•ตํ•ญ์‚ฐ๊ท  ํ์งˆํ™˜ ๊ด€๋ จ ์œ ์ „์ž์— ๋Œ€ํ•œ ์—ฐ๊ด€ ์—ฐ๊ตฌ 64 IV. ๊ณ  ์ฐฐ 69 V. ๊ฒฐ ๋ก  79 ์ฐธ๊ณ ๋ฌธํ—Œ 80 Abstract 96๋ฐ•

    Effect of V addition on the ferrite transformation in low carbon steels

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2002.Docto

    ์ƒ์ฒด๊ฐ„์ด์‹ ํ›„ ์ง€๋ฐฉ๊ฐ„ ์ด์‹ํŽธ์—์„œ์˜ ๊ฐ„์ „๊ตฌ์„ธํฌ์˜ ์ฆ์‹

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์™ธ๊ณผํ•™ ์ „๊ณต,2007.Docto

    ํŽ˜๋ผ์ดํŠธ-ํผ์–ผ๋ผ์ดํŠธ๊ฐ•์˜ ๋ƒ‰๊ฐ์†๋„์— ๋”ฐ๋ฅธ ๋ฏธ์„ธ์กฐ์ง๊ณผ ๊ธฐ๊ณ„์  ์„ฑ์งˆ ์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธˆ์†๊ณตํ•™๊ณผ,1997.Maste

    Long-working(0.3m) distance microscope

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    MasterThe need for a high magnification microscope with a long-working distance has emerged in order to observe the laser focus position for TMDC sample with a 30~40 ฮผm. To solve this problem, a long-working (0.3m) distance microscope should be manufactured to measure TMDC samples in a vacuum chamber. Some conditions and restrictions were placed for simple implementation. (1) Operates at a single wavelength of 532 to reduce chromatic aberration (2) Realize a magnification of 10 times or more (3) Limit the distance from the first lens to the detector to 40 or less for actual production (4) Use of commercial lenses as much as possible for simple production (5) Minimization of monochromatic aberration through simulation Simulations were conducted through the above conditions and constraints. As a result of the experiment based on the simulation, it succeeded in obtaining an image with a magnification of up to 25 times and a resolution of 8 ฮผm.ํฌํ•ญ๊ณต๋Œ€ ์•„ํ† ์ดˆ ๊ณผํ•™ ์—ฐ๊ตฌ์‹ค ๋‚ด ์•„ํ† ์ดˆ ๊ด‘ ์‹œ์„ค์˜ ๊ฒฝ์šฐ ๋ฐ˜๊ฒฝ์ด 25 ์ˆ˜์ค€์˜ ์ง„๊ณต ์ฑ”๋ฒ„ ์•ˆ์— ์ˆ˜์‹ญ ์ˆ˜์ค€์˜ TMDC ์ƒ˜ํ”Œ์„ ํ™œ์šฉํ•˜๋Š” ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ์ง„๊ณต ๋ฐ–์—์„œ 30~40 ์ˆ˜์ค€์˜ TMDC ์ƒ˜ํ”Œ์— ๋Œ€ํ•œ ๋ ˆ์ด์ €์˜ ์ง‘์†์œ„์น˜๋ฅผ ๊ด€์ธกํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ธด Working distance์˜ ๊ณ ๋ฐฐ์œจ ํ˜„๋ฏธ๊ฒฝ์˜ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜์—ˆ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด Long-working(0.3m) distanace microscope์„ ์ œ์ž‘์„ ํ•˜์—ฌ ์ง„๊ณต ์ฑ”๋ฒ„ ์•ˆ์˜ TMDC์ƒ˜ํ”Œ์„ ์ธก์ •ํ•ด์•ผ ํ•œ๋‹ค. Microscope๋ฅผ ์œ„์˜ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜๋ฉฐ, ๊ฐ„๋‹จํ•˜๊ฒŒ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ช‡ ๊ฐ€์ง€ ์กฐ๊ฑด๊ณผ ์ œ์•ฝ์„ ๋‘์—ˆ๋‹ค. (1) ์ƒ‰์ˆ˜์ฐจ๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด 532 ์˜ ๋‹จ์ผ ํŒŒ์žฅ์—์„œ ์ž‘๋™ (2) 10๋ฐฐ ์ด์ƒ์˜ ๋ฐฐ์œจ์„ ๊ตฌํ˜„ (3) ์‹ค์ œ ์ œ์ž‘์„ ์œ„ํ•ด ์ฒซ๋ฒˆ์งธ ๋ Œ์ฆˆ๋กœ๋ถ€ํ„ฐ detector๊นŒ์ง€์˜ ๊ฑฐ๋ฆฌ๋ฅผ 40 ์ดํ•˜ ์ œ์•ฝ (4) ๊ฐ„๋‹จํ•œ ์ œ์ž‘์„ ์œ„ํ•ด ์ตœ๋Œ€ํ•œ์˜ ์ƒ์šฉ๋ Œ์ฆˆ ์‚ฌ์šฉ (5) ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ๋‹จ์ƒ‰์ˆ˜์ฐจ์˜ ์ตœ์†Œํ™” ์œ„์˜ ์กฐ๊ฑด๊ณผ ์ œ์•ฝ์„ ํ†ตํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ง„ํ–‰ํ•˜์˜€๊ณ , ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค.์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์‹คํ—˜ ๊ฒฐ๊ณผ ์ตœ๋Œ€ 25๋ฐฐ์˜ ๋ฐฐ์œจ๊ณผ 8ฮผm ๋ถ„ํ•ด๋Šฅ์˜ ์ด๋ฏธ์ง€๋ฅผ ์–ป๋Š” ๋ฐ ์„ฑ๊ณตํ–ˆ๋‹ค
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