22 research outputs found

    High-Pressure Phase Behavior of Biodegradable Polymer in Supercritical Solvent Mixtures

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2015. 2. ๊น€ํ™”์šฉ.์ง€๊ตฌ ์˜จ๋‚œํ™”์™€ ๊ฐ™์€ ์—ฌ๋Ÿฌ ํ™˜๊ฒฝ๋ฌธ์ œ๋กœ ์ธํ•ด ์ตœ๊ทผ์˜ ์—ฐ๊ตฌ๋Š” ์นœํ™˜๊ฒฝ์ ์ธ ๋ถ€๋ถ„์— ๋น„์ค‘์„ ๋‘๊ณ  ์ง„ํ–‰ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ํŠนํžˆ, ๊ทธ๋Ÿฐ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋“ค ์ค‘ ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด์‚ฐํ™”ํƒ„์†Œ ๋ฐฐ์ถœ์„ ์ ˆ๊ฐํ•˜๋Š” ๊ฒƒ์€ ํ™˜๊ฒฝ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๋Š” ์ค‘์š”ํ•œ ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ณ ๋ ค๋˜๊ณ  ์žˆ์œผ๋ฉฐ ์‹ค์ œ๋กœ ์˜์•ฝ, ์ž๋™์ฐจ ๋“ฑ ๊ฐ์ข… ๋ถ„์•ผ์—์„œ ํ™œ๋ฐœํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž๋“ค ์ค‘ Poly lactic acid์™€ Polycaprolactone์„ ์ค‘์ ์ ์œผ๋กœ ๋‹ค๋ฃจ๊ณ , ์ด ๊ณ ๋ถ„์ž๋“ค์„ ๋””ํด๋กœ๋กœ๋ฉ”ํƒ„๊ณผ ์ด์‚ฐํ™”ํƒ„์†Œ๋ฅผ ์šฉ๋งค๋กœ ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์˜จ๋„, ์••๋ ฅ, ์กฐ์„ฑ ์กฐ๊ฑด์—์„œ์˜ ๊ณ ์•• ๋‹ค์„ฑ๋ถ„๊ณ„ ์ƒ๊ฑฐ๋™์„ ๊ฐ€๋ณ€ ๋ถ€ํ”ผ ์‹คํ—˜ ์žฅ์น˜(VVVC)๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ์‹ค์ œ ๋‹ค์ˆ˜ ํ™”ํ•™ ๊ณต์ •์—์„œ ์ดˆ์ž„๊ณ„ ์œ ์ฒด ๊ธฐ์ˆ ์„ ์ ‘๋ชฉ์‹œํ‚จ ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž์˜ ์ œ์กฐ ๋ฐ ์ •์ œ๋ฅผ ํ†ตํ•ด ๊ฐ์ข… ์ œํ’ˆ์ด ์ƒ์‚ฐ๋˜๊ณ  ์žˆ์œผ๋‚˜ ์ด ๊ณต์ •๋“ค์˜ ์ตœ์ ํ™”๋œ ์„ค๊ณ„๋ฅผ ์œ„ํ•œ ์—ด์—ญํ•™์  ๋ชจ๋ธ๋ง์˜ ์‹œ๋„๊ฐ€ ๋ถ€์กฑํ–ˆ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด์†Œํ•˜๊ธฐ ์œ„ํ•ด, ๊ธฐ์กด์— ๋ฐœํ‘œ๋œ Peng-Robinson ์ƒํƒœ๋ฐฉ์ •์‹๊ณผ SAFT๊ฐ€ ๊ฒฐํ•ฉ๋œ ํ˜•ํƒœ์ธ Hybrid ์ƒํƒœ๋ฐฉ์ •์‹์„ ์ด์šฉํ•˜์—ฌ ์ƒ๊ฑฐ๋™ ๋ฐ์ดํ„ฐ๋ฅผ ๋ชจ๋ธ๋งํ–ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํšŒํ•ฉํ•œ๋‹ค๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋ถ€์กฑํ•œ ์šฉ๋งค๋“ค์ด ์‹œ์Šคํ…œ์— ํฌํ•จ๋˜์–ด ์žˆ๋Š” ๊ฒฝ์šฐ๋Š” ์šฉ๋งค๊ฐ€ ์ž์ฒด์ ์ธ ํšŒํ•ฉ์„ ์ผ์œผํ‚จ๋‹ค๋Š” ๊ธฐ์กด hybrid ์ƒํƒœ๋ฐฉ์ •์‹์˜ ๊ฐ€์ •๊ณผ ๋Œ€๋น„๋˜๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ ํ•ญ์„ ๊ณ„์‚ฐ์— ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์ด ๋” ํƒ€๋‹นํ•˜๋‹ค๊ณ  ํŒ๋‹จํ–ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๊ธฐ์กด Hybrid ์ƒํƒœ๋ฐฉ์ •์‹์˜ ํšŒํ•ฉํšจ๊ณผ๋ฅผ ์ œ์™ธํ•˜๋Š” ํ˜•ํƒœ๋กœ ๊ณ„์‚ฐ ๋ฐฉ์‹์„ ๋ณ€ํ™”์‹œ์ผฐ๋‹ค. ๋˜ ์šฉ๋งค์˜ ์ˆœ์ˆ˜ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๊ฒฝ์šฐ ๋ฌผ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ์ถ”์‚ฐํ•˜๋Š” ํ˜•ํƒœ๋กœ ์ตœ์ ํ™”๋˜์—ˆ๋Š”๋ฐ ์ด ๊ณผ์ •์ด ๊ณ„์‚ฐ ์‹œ๊ฐ„์„ ์ฆ๊ฐ€์‹œ์ผฐ๊ธฐ ๋•Œ๋ฌธ์— ํ•ด๋‹น ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์„ ์ด ๋ฐฉ์‹์ด ์•„๋‹Œ TC, PC ๋“ฑ ์ž„๊ณ„์ƒ์ˆ˜๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ณ„์‚ฐํ•˜๋Š” ์‹์„ ํ™œ์šฉํ•˜์—ฌ hybrid ์ƒํƒœ๋ฐฉ์ •์‹์— ์ ์šฉ์‹œ์ผฐ๋‹ค. ์•ž์„œ ๋…ผ์˜ํ•œ ๋ฐฉ๋ฒ•๋“ค์„ ์ ์šฉํ•˜์—ฌ ๋ณธ ์—ฐ๊ตฌ์˜ ์‹คํ—˜๋ฐ์ดํ„ฐ๋“ค์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ณ„์‚ฐํ•œ ๊ฒฐ๊ณผ ๊ธฐ์กด ๊ฒฐ๊ณผ์™€ ํฐ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜์œผ๋ฉฐ ๊ณ„์‚ฐ ์‹œ๊ฐ„์ด ๋งŽ์ด ๋‹จ์ถ•๋˜๋Š” ๊ฐœ์„ ํšจ๊ณผ๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค.๊ตญ๋ฌธ ์š”์•ฝ..................................................................โ…ฐ ๋ชฉ์ฐจ.........................................................................โ…ฒ ๊ทธ๋ฆผ ๋ชฉ์ฐจ..................................................................v ํ‘œ ๋ชฉ์ฐจ.....................................................................viii 1. ์„œ๋ก ......................................................................1 1.1 ๊ฐœ๊ด€.....................................................................1 1.1.1 ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž์˜ ์ƒํ‰ํ˜•.....................................1 1.1.2 ์—ฐ๊ตฌ ๋ชฉ์  ๋ฐ ๋ฒ”์œ„................................................3 2. ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž ๋‹ค์„ฑ๋ถ„๊ณ„ ์‹œ์Šคํ…œ ์ƒํ‰ํ˜• ์ธก์ •..............5 2.1 ์‹คํ—˜.....................................................................5 2.1.1 ์‹คํ—˜ ์žฅ์น˜ ๋ฐ ์‹คํ—˜ ๊ณผ์ •.........................................5 2.1.2 ๋‹จ๋Ÿ‰์ฒด + ์ดˆ์ž„๊ณ„ ์ด์‚ฐํ™”ํƒ„์†Œ 2์„ฑ๋ถ„๊ณ„ ์‹œ์Šคํ…œ.............9 2.1.2.1 ์žฌ๋ฃŒ........................................................9 2.1.2.2 ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ.....................................................9 2.1.3 ์ƒ๋ถ„ํ•ด์„ฑ ๊ณ ๋ถ„์ž๊ฐ€ ํฌํ•จ๋œ 3์„ฑ๋ถ„๊ณ„ ์‹œ์Šคํ…œ................27 2.1.3.1 ์žฌ๋ฃŒ................................................................27 2.1.3.2 ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ.....................................................27 3. ๊ณ ๋ถ„์ž โ€“ ์ด์‚ฐํ™”ํƒ„์†Œ ์‹œ์Šคํ…œ์˜ ์ƒํƒœ๋ฐฉ์ •์‹ ๊ณ„์‚ฐ ๋ฐ ๊ฐœ์„ ..57 3.1 ์„ ํ–‰ ์—ฐ๊ตฌ ๊ฒ€ํ† .......................................................57 3.1.1. ๊ณ ๋ถ„์ž ์‹œ์Šคํ…œ์˜ ์ƒํ‰ํ˜• ๊ณ„์‚ฐ........................57 3.2 The Hybrid Equation of State...................................61 3.2.1. The Hybrid Equation of State modification.....78 3.2.2. Modified Hybrid EOS์˜ ๊ณ„์‚ฐ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ..............83 4. ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ...................................................102 5. ์ฐธ๊ณ  ๋ฌธํ—Œ..............................................................105 Abstract...................................................................108Docto

    ๋ผํ”Œ๋ผ์Šค ์˜์—ญ์—์„œ์˜ ํŒŒ๋™๊ฒฝ๋กœ ๋ถ„์„๊ณผ ์ด์— ๋”ฐ๋ฅธ ํŒŒํ˜•์—ญ์‚ฐ์˜ ์ „๋žต

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2017. 8. ์‹ ์ฐฝ์ˆ˜.๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์žฅํŒŒ์žฅ ํƒ„์„ฑํŒŒ ์†๋„ ๋ชจ๋ธ ์ถ”์ • ๊ธฐ์ˆ ๋กœ์„œ, ์ฃผํŒŒ์ˆ˜ ์˜์—ญ ๋ฐ ์‹œ๊ฐ„ ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ๊ณผ ๊ฐ™์€ ๊ณ ํ•ด์ƒ๋„ ์†๋„ ๋ชจ๋ธ ์ถ”์ • ๊ธฐ์ˆ ์— ์ดˆ๊ธฐ ์†๋„ ๋ชจ๋ธ์„ ์ œ๊ณตํ•˜๋Š” ์šฉ๋„๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ์ฃผํŒŒ์ˆ˜ ์˜์—ญ ๋ฐ ์‹œ๊ฐ„ ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์€ ์ดˆ๊ธฐ ์†๋„ ๋ชจ๋ธ์— ๋Œ€๋‹จํžˆ ๋ฏผ๊ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์˜ ์ •ํ™•์„ฑ์€ ์ „์ฒด ์†๋„ ๋ชจ๋ธ ์ถ”์ • ๊ณผ์ •์— ์žˆ์–ด์„œ ๋Œ€๋‹จํžˆ ์ค‘์š”ํ•œ ์š”์†Œ์ด๋‹ค. ๋˜ํ•œ ๋ผํ”Œ๋ผ์Šค ํŒŒํ˜•์—ญ์‚ฐ์—์„œ ์‚ฌ์šฉ๋˜๋Š” ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒ๋™์žฅ์€ ์–ป๋Š” ๊ณผ์ •์—์„œ ๋งŽ์€ ๋น„์šฉ์„ ์š”๊ตฌํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ๊ธฐ์ˆ ์˜ ๊ฒฝ์ œ์„ฑ ์ธก๋ฉด์—์„œ ๋ผํ”Œ๋ผ์Šค ํŒŒํ˜•์—ญ์‚ฐ์˜ ์ˆ˜๋ ด์†๋„ ๋ฐ ํšจ์œจ์„ฑ์€ ์—ญ์‚ฐ ๊ณผ์ •์˜ ์„ฑํŒจ๋ฅผ ๊ฐ€๋ฅด๋Š” ์ค‘์š”ํ•œ ์š”์†Œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๋ผํ”Œ๋ผ์Šค ํŒŒํ˜•์—ญ์‚ฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋“ค์—์„œ๋Š” ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ์ž๋ฃŒ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ํŒŒ๋™๊ฒฝ๋กœ(wavepath)์— ๋Œ€ํ•œ ๊ณ ์ฐฐ์ด ๋ถˆ์ถฉ๋ถ„ํ•œ ๊ด€๊ณ„๋กœ ๋ชจ๋ธ ํ•ด์ƒ๋„ ๋ฐ ์ˆ˜๋ ด์†๋„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์ด ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ ๋ฐํžˆ์ง€ ๋ชปํ•˜์˜€๋˜ ๋ผํ”Œ๋ผ์Šค ์˜์—ญ์˜ ํŒŒ๋™๊ฒฝ๋กœ์˜ ์„ฑ์งˆ์„ ๊ทœ๋ช…ํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ ์ œ๋Œ€๋กœ ์ˆ˜ํ–‰ํ•˜์ง€ ๋ชปํ•˜์˜€๋˜ ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์— ๋Œ€ํ•œ ์ˆ˜๋ ด์†๋„ ๋ฐ ๋ชจ๋ธ ํ•ด์ƒ๋„, ๊ทธ๋ฆฌ๊ณ  ํšจ์œจ์„ฑ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋จผ์ € ๊ณต๊ฐ„ ์˜์—ญ์— ๋Œ€ํ•œ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜๋ผ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ์‡  ์ƒ์ˆ˜(attenuation constant)๋ผ๋Š” ๊ฐœ๋…์„ ๋„์ž…ํ•จ์œผ๋กœ์จ ๋ผํ”Œ๋ผ์Šค ์˜์—ญ์˜ ํŒŒ๋™๊ฒฝ๋กœ๊ฐ€ ๊ทผ์‚ฌ์ ์œผ๋กœ ๊ฐ์‡  ์ƒ์ˆ˜ ๋ฒกํ„ฐ์™€ ๊ณต๊ฐ„ ๋ฒกํ„ฐ์˜ ๊ณฑ์„ ์ง€์ˆ˜๋กœ ํ•˜๋Š” ์‹ค ์ง€์ˆ˜ํ•จ์ˆ˜ ๊ธฐ์ €์ž„์„ ์ฆ๋ช…ํ•œ๋‹ค. ์ด์— ๋”ํ•˜์—ฌ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ผํ”Œ๋ผ์Šค ์˜์—ญ์˜ ํŒŒ๋™ ๊ฒฝ๋กœ๊ฐ€ ํฐ ์กฐ๊ฑด์ˆ˜๋ฅผ ๊ฐ€์ง€๋Š” ์‹ค ์ง€์ˆ˜ํ•จ์ˆ˜์ธ ๊ฒƒ์„ ํ†ตํ•ด, ๋น ๋ฅธ ์ˆ˜๋ ด์†๋„๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์— ๊ฐ€์šฐ์Šค๋‰ดํ„ด๋ฒ•์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ํ•ฉ๋ฆฌ์ ์ž„์„ ๋ฐํžŒ๋‹ค. BP ๋ฒค์น˜๋งˆํฌ ๋ชจ๋ธ์˜ ์ˆ˜์น˜ ์˜ˆ์ œ๋Š” ์ด๋Ÿฌํ•œ ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ ๊ฐ€์šฐ์Šค๋‰ดํ„ด๋ฒ•์ด ๊ฐ€์ง€๋Š” ํšจ์šฉ์„ฑ์„ ์ฆ๋ช…ํ•ด์ค€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ์‡  ์ƒ์ˆ˜ ๋ฒกํ„ฐ๊ฐ€ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜์™€ ํŒŒ์˜ ์ž…์‚ฌ ๊ฐ๋„์— ๋Œ€ํ•œ ํ•จ์ˆ˜์ž„์„ ์ฆ๋ช…ํ•จ์œผ๋กœ์จ, ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์„ ํ†ตํ•ด ๋†’์€ ํ•ด์ƒ๋„์˜ ๋ชจ๋ธ์„ ์–ป๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋„“์€ ๋ฒ”์œ„์˜ ์ž…์‚ฌ ๊ฐ๋„๊ฐ€ ํ•„์ˆ˜์ ์ž„์„ ๋ฐํžŒ๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋ธ ํ•ด์ƒ๋„์™€ ์ž…์‚ฌ ๊ฐ๋„ ๋ฒ”์œ„์™€์˜ ๊ด€๊ณ„๋Š” ์˜คํ”„์…‹-์‹ฌ๋„๋น„(offset-depth ratio)๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ๋ชจ๋ธ ํ•ด์ƒ๋„๊ฐ€ ๋‚ฎ์•„์ง€๋Š” ์ด์œ ๋ฅผ ์„ค๋ช…ํ•ด์ฃผ๋ฉฐ, ํƒ์‚ฌํ™˜๊ฒฝ์— ๋”ฐ๋ฅธ ์ˆ˜ํ‰ ๋ฐ ์ˆ˜์ง ํ•ด์ƒ๋„์˜ ๋ณ€ํ™” ์—ญ์‹œ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๋งˆ์ง€๋ง‰ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ผํ”Œ๋ผ์Šค ์˜์—ญ ํŒŒํ˜•์—ญ์‚ฐ์˜ ํšจ์œจ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ํšจ์œจ์ ์ธ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜ ์„ ํƒ ์ „๋žต์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์„ ํƒ๋œ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜๋Š” ๊ฐ์‡ ์ƒ์ˆ˜์˜ ๋ฒ”์œ„์˜ ์—ฐ์†์„ฑ์„ ์œ ์ง€์‹œํ‚ด์œผ๋กœ์จ ๋ชจ๋ธ ํ•ด์ƒ๋„๋ฅผ ๋ณด์žฅํ•˜๋ฉฐ, ๊ฐ์‡ ์ƒ์ˆ˜์˜ ๋ถˆํ•„์š”ํ•œ ์ค‘๋ณต์„ ์ตœ์†Œํ™”ํ•จ์œผ๋กœ์จ ํšจ์œจ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. ์ˆ˜์น˜ ์˜ˆ์ œ๋กœ๋ถ€ํ„ฐ ์ œ์•ˆ๋œ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜ ์„ ํƒ ์ „๋žต์ด ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์“ฐ์—ฌ์™”๋˜ ๋“ฑ๊ฐ„๊ฒฉ์œผ๋กœ ๋ผํ”Œ๋ผ์Šค ์ƒ์ˆ˜ ์„ ํƒํ•˜๋Š” ์ „๋žต์— ๋น„ํ•ด ํšจ์œจ์„ฑ ๋ฐ ์ •ํ™•์„ฑ์˜ ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ ์›”๋“ฑํ•œ ๊ฒฐ๊ณผ๋ฅผ ์‚ฐ์ถœํ•ด ๋‚ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค.Laplace-domain waveform inversion (WI) is a technique for estimating long wavelength velocity models. The velocity model, estimated by Laplace-domain WI, is used as an initial velocity model for techniques such as frequency-domain and time-domain waveform inversion. These techniques are then used to develop high resolution velocity models used in subsurface imaging. Since frequency-domain and time-domain waveform inversion are sensitive to the initial velocity model, model resolution of Laplace-domain WI is an important factor in the overall velocity-estimation process. In addition, since the cost for obtaining the wavefield of the Laplace domain is large, it is necessary to improve the convergence rate and efficiency of Laplace-domain WI. Previous Laplace-domain WI studies have shown difficulty in analyzing model resolution and convergence rate due to insufficient understanding of the wavepath and its role in representing the relationship between the model parameters and seismic data. This study investigates the characteristics of the wavepath in the Laplace domain which have not been clarified in previous research. Through this study, we implement convergence rate, model resolution, and efficiency analysis for Laplace-domain inversion. By introducing the attenuation constant, which can be considered a Laplace constant in the spatial domain, we prove that the wavepath of the Laplace domain is a real exponential basis with the product of the attenuation constant vector and the position vector as an exponent. We also prove that the attenuation constant vector is a function of both the Laplace constant and the incident angle. From the numerical example, it can be confirmed that the attenuation constant depends on both the Laplace constant and the incident angle. In addition, this study shows that it is reasonable to apply the Gauss-Newton method to Laplace-domain WI for fast convergence. The wavepath of the Laplace domain is a real exponential function, which has a large condition number. The numerical example of the BP benchmark model demonstrates the effectiveness of the Gauss-Newton method in this Laplace-domain WI algorithm. We also prove that a wide range of incident angles is essential to obtain a high resolution model through Laplace-domain inversion. The relationship between the model resolution and the incident angle range explains why the model resolution decreases as the offset-depth ratio increases. Also, horizontal and vertical resolution changes, depending on the exploration environment, can be predicted. Finally, we propose an efficient Laplace constant selection strategy to improve the efficiency of Laplace domain inversion. The Laplace constants selected through the proposed method improve efficiency by maintaining continuity of the range of attenuation constants and by minimizing unnecessary repetition of attenuation constants. From the numerical example, it can be seen that the proposed Laplace constant selection strategy yields superior results in terms of both efficiency and accuracy, compared with the strategy of choosing Laplace constants at fixed intervals. This applies for both the simple-model and complex-model case, such as the SEG/EAGE salt dome model.Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation and research objective 6 1.3 Outline 8 Chapter 2 Wavepath in the Laplace domain 10 2.1 Wave equation in the Laplace domain 11 2.2 Logarithmic objective function for Laplace-domain waveform inversion (WI) 13 2.3 Laplace-domain Greens functions for a homogeneous acoustic unbounded medium 17 2.4 Rytov wavepath in the Laplace domain 20 2.5 Vertical components of wavepath in the Laplace domain considering the geometrical spreading effect 24 2.6 Numerical examples 26 Chapter 3 Truncated Gauss-Newton method for Laplace-domain WI 30 3.1 Gauss-Newton method and ill-conditioned problems 31 3.2 Ill-conditioning of the Laplace-domain WI algorithm 35 3.3 Truncated Gauss-Newton method 38 3.4 Stopping criterion 40 3.5 Numerical examples 42 Chapter 4 Resolution analysis for Laplace-domain WI 50 4.1 Relationship between the number of attenuation constants and model resolution 51 4.2 Relationship between the condition number of wavepath and model resolution 53 4.3 Range of the attenuation constants and condition number of data kernel matrix in the Laplace domain 57 4.4 Numerical examples 59 Chapter 5 An efficient strategy for Laplace constant selection 71 5.1 Continuity and redundancy of attenuation constants 72 5.2 An efficient strategy for Laplace constant selection 75 5.3 A modified Laplace constant selection strategy considering the geometrical spreading 78 5.4 Effectiveness of the Laplace constant selection strategy in a 2D or 3D heterogeneous medium 81 5.5 Numerical examples 84 Chapter 6 Discussions & Conclusions 98 Appendix A. Rytov wavepath considering the geometrical spreading 101 Appendix B. Truncated Gauss-Newton method 106 References 110 ์ดˆ ๋ก 119Docto

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์–ธ๋ก ์ •๋ณดํ•™๊ณผ,2008. 8.Maste

    ๊ณ ์ž์žฅ MRI์—์„œ CEST์™€ T1rho ์˜์ƒ ๊ตฌํ˜„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๋ฐฉ์‚ฌ์„ ์‘์šฉ์ƒ๋ช…๊ณผํ•™์ „๊ณต, 2014. 8. ๊น€ํ˜„์ง„.์„œ๋ก : ๊ด€์ ˆ์˜ ์žฅ์• ๋ฅผ ๊ฐ€์ง€๋Š” ๊ด€์ ˆ์—ผ์€ ๊ณต๊ณต ๋ณด๊ฑด์—์„œ ํฐ ์–ด๋ ค์›€์œผ๋กœ ์—ฌ๊ฒจ์ง„๋‹ค. ์ด๋Ÿฐ ๊ด€์ ˆ์—ผ์˜ ์ดˆ๊ธฐ ์ฆ์„ธ๋กœ์„œ ๊ด€์ ˆ์•ˆ์˜ glycosaminoglycan (GAG) ์–‘์ด ๊ฐ์†Œ๋˜๋Š” ๊ฒƒ์ด ๋ณด๊ณ ๋˜์—ˆ๋‹ค. ์ด๋Ÿฐ GAG์™€ ๊ฐ™์€ ๊ณ ๋ถ„์ž๋Š” TE๊ฐ€ ๋งค์šฐ ์งง๊ณ  ์ผ๋ฐ˜์ ์ธ MR ์˜์ƒ ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ๋ฐœ๊ฒฌํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์ƒ์ฒด ๋‚ด์—์„œ GAG์™€ ๊ฐ™์€ ๊ณ ๋ถ„์ž ๋Ÿ‰์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, T1rho ๋งต๊ณผ gagCEST๋Š” ์ง์ ‘ ๋˜๋Š” ๊ฐ„์ ‘์ ์ธ ๊ด€์ ˆ ์„ฑ๋ถ„ ์ธก์ • MR ์˜์ƒ ๊ธฐ๋ฒ•์ด๋‹ค. ๋ฐฉ๋ฒ•: gagCEST์™€ T1rho ํŽ„์Šค ์‹œํ€€์Šค ์‹คํ—˜์€ ํŒฌํ…€๊ณผ ๋™๋ฌผ ์ƒ์ฒด ์‹คํ—˜์œผ๋กœ ์‹ค์‹œํ–ˆ๋‹ค. gagCEST์˜ ๊ฒฝ์šฐ ๋‹ค์–‘ํ•œ ์ฃผํŒŒ์ˆ˜์˜ ํฌํ™” ํŽ„์Šค๋กœ CEST z-spectrum๊ณผ MTRasym์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. T1rho์˜ ๊ฒฝ์šฐ๋Š” ์ผ๋ฐ˜์ ์ธ FSE ํŽ„์Šค ์‹œํ€€์Šค ์•ž ๋ถ€๋ถ„์— ์Šคํ•€ ๊ณ ์ • ํŽ„์Šค๋ฅผ ์—ฐ๊ฒฐํ•˜์˜€๋‹ค. ์ด ์Šคํ•€ ๊ณ ์ • ๋ถ€๋ถ„์˜ ์‹œ๊ฐ„(TSL : spin lock time)์„ 10, 30, 50, 70, 100, 120 ms์œผ๋กœ ๋ฐ”๊ฟ”์„œ ๋ผ์ง€ ๊ด€์ ˆ, ์ƒ์ฒด ๋žซ ๋ฌด๋ฆŽ ๊ด€์ ˆ ์˜์ƒ์„ ์–ป์—ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ์–ป์€ ์˜์ƒ์œผ๋กœ T1rho ๋งต ์˜์ƒ์„ ๊ตฌํ–ˆ๋‹ค. ๊ฒฐ๊ณผ: gagCEST์˜ ๊ฒฝ์šฐ ๋ผ์ง€ ๊ด€์ ˆ ํŒฌํ…€๊ณผ ์ƒ์ฒด ๋žซ ๋ฌด๋ฆŽ ๊ด€์ ˆ ๋ชจ๋‘ MTRasym = 1.0 ppm์ผ ๊ฒฝ์šฐ ๊ฐ€์žฅ ๋†’์€ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. gagCEST์˜ ์ •๋Ÿ‰ํ™”๋Š” ์ด CEST ์ŠคํŽ™ํŠธ๋Ÿผ์ด 1.0 ppm์ผ ๋•Œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  T1rho ์˜์ƒ์˜ ๊ฒฝ์šฐ ์ผ๋ฐ˜์ ์ธ T1 ๊ฐ•์กฐ์˜์ƒ, T2 ๊ฐ•์กฐ์˜์ƒ๋ณด๋‹ค ๊ด€์ ˆ ๋ถ€์œ„์˜ ์‹ ํ˜ธ ๋Œ€ ์žก์Œ๋น„ (SNR : signal to noise ratio)๊ฐ€ ๋” ๋†’๊ณ , T1rho ๋งต ์˜์ƒ์„ ํ†ตํ•ด์„œ ๊ณ ๋ถ„์ž์˜ ์ •๋Ÿ‰ํ™”๋ฅผ ๋ถ„๋ช…ํžˆ ๋ณด์—ฌ์ค€๋‹ค. ๊ฒฐ๋ก : gagCEST์™€ T1rho ํŽ„์Šค ์‹œํ€€์Šค๋Š” ์ผ๋ฐ˜์ ์€ MRI ์˜์ƒ ๊ธฐ๋ฒ•์œผ๋กœ๋Š” ๋ถ„์„ํ•˜๊ธฐ ์–ด๋ ค์šด ์ƒ์ฒด ๋‚ด ๊ณ ๋ถ„์ž๋ฅผ ์˜์ƒํ™” ํ•˜๊ธฐ ์œ„ํ•ด ๋งŒ๋“ค์–ด์ง„ ๋ฐฉ๋ฒ•์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด์„œ, ๋ผ์ง€ ๊ด€์ ˆ ํŒฌํ…€๊ณผ ๋žซ์˜ ์ƒ์ฒด ๋‚ด ๋ฌด๋ฆŽ ๊ด€์ ˆ์„ CEST์™€ T1rho ์˜์ƒ ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•˜์—ฌ, ๊ณ ์ž์žฅ (9.4T) MRI์—์„œ gagCEST์™€ T1rho ๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์˜๋ฏธ ์žˆ๋Š” ๊ด€์ ˆ ์˜์ƒ์„ ์–ป์—ˆ๋‹ค. ์ถ”ํ›„ ์•Œ์ธ ํ•˜์ด๋จธ๋ณ‘, ์•”๊ณผ ๊ฐ™์€ ๋‹ค๋ฅธ ๊ณ ๋ถ„์ž ์˜์—ญ ์งˆ๋ณ‘์—๋„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ–ˆ๋‹ค.Introduction: Rheumatoid arthritis (RA) is a public health problem that involves the disorder of articular cartilage. It has been reported that the loss of glycosaminoglycan (GAG) in cartilage is a signal of early RA onset. The macromolecules such as GAGs have much shorter TE and are not able to be identified using conventional MR Imaging techniques. In order to monitor GAGs concentration change in vivo, novel quantitative techniques such as T1rho mapping, gagCEST provide direct and indirect assessments of cartilage composition. Methods: With the purpose of setting up the CEST sequences, phantom and in-vivo scans were performed. The MR signals under saturation pulse at different frequencies were fit into a smooth CEST z-spectrum and MTR asymmetry curve were calculated. A pulse sequence consisting of T1rho-prepped, FSE image acquisition. Multiple TSL(spin lock time10, 30, 50, 70, 100, 120 ms) were used to construct a T1rho relaxation map in the both phantom(a swine patella ex-vivo) and male wistar rat in the knee joint in-vivo. Results: High MTRasym (1.0 ppm) values reflect the GAGs contents in both swine patella phantom and cartilage in rat knee in-vivo. The quantitation of gagCEST MRI is based on the asymmetry in the CEST spectrum curve around 1.0 ppm and its reference frequency -1.0 ppm. The experiments attempt to explain how molecular properties of model tissue systems influence proton relaxation in the rotating frame and how they may be employed to generate useful contrast in images. The experiments will emphasize quantitative measurements of spin-lattice relaxation in the rotating frame. Conclusions: CEST MRI and T1rho image as a molecular imaging technique was developed to detect the in-vivo MMs protons, which cannot be assessed by the traditional MRI methods. In this study, CEST and T1rho MRI was performed in swine patella phantoms and rats in-vivo to set up the MR imaging procedures and test its performance in finding macromolecules. Based on these preliminary results, we can get meaningful 9.4T gagCEST and T1rho cartilage images. And the ability of other MMs zones potential remains such as Alzheimers disease, and tumors.Abstract i Contents iv List of tables and figures v List of Abbreviations viii Introduction 1 Material and Methods 4 Results 17 Discussion 27 Conclusion 31 References 32 Abstract in Korean 35Maste

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