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    Li2RuO3์˜ ์ „์ด๊ธˆ์† ์ดํ•ฉ์ฒด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ๋ฌผ๋ฆฌยท์ฒœ๋ฌธํ•™๋ถ€(๋ฌผ๋ฆฌํ•™์ „๊ณต), 2020. 8. ๋ฐ•์ œ๊ทผ.Transition metal ions in the oxides, which have d orbitals as a valence orbital, has been considered that the only orbital overlap between ligands and the metal is essential to describe the behavior of electrons. Recent studies, however, show that a direct overlap can be formed between the d-orbitals by various factors: for instance, the periodicity of the transition metal, the shape of wave functions in the t2g manifold, and the local network of the metal-ligand polyhedral. In this case, the direct overlap becomes to be much essential to describe electronic behavior. Especially when those conditions are satisfied, the transition metal ions form a cluster, and the electronic wave function has to be described using molecular orbitals. In this thesis, I study on a dimer in Li2RuO3, which is a condensation of two transition metal ions. The 4d Ru oxide Li2RuO3 has a layered honeycomb structure composed by edge-sharing RuO6 octahedra. It exhibits a structural transition at T = 550 K, below which one-third of the Ru-Ru bonds in the honeycomb lattice becomes shorter than others by about 20%. This stable dimerization enhances the direct orbital overlap, so induces the spin-singlet, molecular orbital state. The main question of my thesis is how the dimerization influences the behavior of electrons in the Ru ion. The Ru-Ru dimers form a herringbone pattern; thus, it expected that this system would have anisotropic physical properties reflecting ones of the dimer. The single-crystal sample was required to verify this idea and successfully synthesized. With this crystal, the anisotropies in electrical and magnetic properties were measured. The DFT calculation shows that the opening of the electronic gap requires Coulomb interaction and the correlation between electrons affects the anisotropy of the resistivity. Based on this picture, a dimer model of correlation effect was constructed to simulate the magnetic anisotropy and the calculation with the exact diagonalization method verified its validity. Those results imply that electronic correlation plays a significant role in the dimer. X-ray Spectroscopic study is an excellent way to observe the correlated electrons directly. The experiment is carried out in the I16 beamline of Diamond Light Source. The X-ray absorption spectroscopy result shows that the energy gaps between the t2g and eg level absorption energies depend on the absorption edge; L2 or L3. Furthermore, resonant elastic x-ray scattering (REXS) result on (010) reflection also shows the absorption edge selective behaviors. The simulation with FDMNES, the code with a single electron approach, did not reproduce the experimental results. These results signify that not only the spin-orbit coupling of the 4d electrons is essential but also the direct overlap inducing the correlation exert a strong influence on the electronic structure of the dimer. Orbital radius, according to the periodicity of the transition metal, is one of the critical conditions for forming the cluster. Series of studies on Li2Ru1-xMnxO3 solid solution shows that the replacement with the ion with smaller orbital breaks the herringbone-patterned dimer phase at the Mn substitution rate of 20 %. Within this range, the entropy change during the structural phase transition decreases linearly with increasing the substitution rate, and the local structure around the Ru ion does not that change. Those results back up that the Mn ion does not participate in the dimer, and disrupts the inter-dimer interaction and breaks the herringbone pattern in the end. Also, the local structure measurement with the extended x-ray absorption fine structure (EXAFS) method exhibits the existence of the dimer above the transition temperature more significantly than the pair distribution function analysis with the total scattering.์ „์ด๊ธˆ์† ์‚ฐํ™”๋ฌผ ๋‚ด์˜ ์ „์ด๊ธˆ์† ์ด์˜จ๋“ค์€ d-๊ถค๋„ ์›์ž๊ฐ€ ์ „์ž๋ฅผ ๊ฐ€์ง€๋ฉฐ, ํ•ด๋‹น ํ•จ์ˆ˜์˜ ๊ตญ์†Œ์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ์ธ์ ‘ํ•˜๋Š” ๋ฆฌ๊ฐ„๋“œ์™€์˜ ๊ถค๋„ ๊ฒน์นจ๋งŒ์ด ์ด ์ „์ž์˜ ๊ฑฐ๋™์„ ๊ธฐ์ˆ ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋˜์–ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ์—ฐ๊ตฌ๋Š” ํ•ด๋‹น ์ „์ด๊ธˆ์†์˜ ์ฃผ๊ธฐ์— ๋”ฐ๋ฅธ ๊ถค๋„ ๋ฐ˜๊ฒฝ์˜ ๋ณ€ํ™”, t2g ๋‹ค์–‘์ฒด์˜ ํŠน์„ฑ, ๋ฆฌ๊ฐ„๋“œ์˜ ๊ตญ์ง€์  ๊ตฌ์กฐ์— ์˜ํ•˜์—ฌ ์ด ๊ถค๋„ ํ•จ์ˆ˜๊ฐ€ ํฐ ์ง์ ‘ ๊ฒน์นจ์ด ๊ฐ€๋Šฅํ•˜๊ณ  ์ด๋Ÿฌํ•œ ๊ฒฝ์šฐ ์ด ๊ถค๋„ ๊ฒน์นจ ๋˜ํ•œ ์ „์ž์˜ ๊ฑฐ๋™์„ ๊ธฐ์ˆ ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด๊ณ ํ•˜์˜€๋‹ค. ํŠนํžˆ ์ด๋Ÿฌํ•œ ์กฐ๊ฑด๋“ค์ด ๋งŒ์กฑํ•˜๋Š” ๊ฒฝ์šฐ ์ „์ด๊ธˆ์† ์ด์˜จ๋“ค ๋ช‡๋ช‡์”ฉ ๋ญ‰์น˜ (cluster)๋ฅผ ์ด๋ฃจ์–ด ํ–‰๋™ํ•˜๋ฉฐ ์ด ๋•Œ ๋ญ‰์น˜ ๋‚ด ์ „์ž์˜ ํŒŒ๋™ ํ•จ์ˆ˜๋Š” ๋ถ„์ž ๊ถค๋„ ํ•จ์ˆ˜ (molecular orbital)๋กœ ๊ธฐ์ˆ ๋œ๋‹ค. ๋ณธ๋ฌธ์—์„œ๋Š” ์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ์ผํ™˜์œผ๋กœ ๋ฃจํ…Œ๋Š„ ์‚ฐํ™”๋ฌผ Li2RuO3 ๋‚ด์˜ ์ดํ•ฉ์ฒด (dimer) ๋ญ‰์น˜์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. Li2RuO3๋Š” ๋ฆฌํŠฌ์œผ๋กœ ๋ถ„๋ฆฌ๋˜์–ด์žˆ๋Š” ๋ฃจํ…Œ๋Š„ ๋ฒŒ์ง‘ ๋ชจ์–‘ ๊ฒฉ์ž๊ฐ€ ์ฒฉ์ฒฉ์ด ์Œ“์—ฌ์žˆ๋Š” ๊ตฌ์กฐ๋ฅผ ๊ฐ–๋Š” ๋ฌผ์งˆ์ด๋‹ค. ์ด ์œก๊ฐ๊ฒฉ์ž ๋‚ด์˜ ๋ฃจํ…Œ๋Š„ ์ด์˜จ๋“ค์€ ๋‘ ๊ฐœ์”ฉ ์ง์ง€์–ด ์„œ๋กœ์˜ ๊ฐ„๊ฒฉ์„ ์ค„์—ฌ ์ดํ•ฉ์ฒด๋ฅผ ์ด๋ฃจ๋ฉฐ 550 K์˜ ๊ตฌ์กฐ์ƒ์ „์ด๋ฅผ ํ†ตํ•˜์—ฌ ์ฒญ์–ด๋ผˆ (herringbone) ๋ชจ์–‘์˜ ๊ฒฉ์ž๋ฅผ ๋งŒ๋“ ๋‹ค. ์ด ์ฒญ์–ด๋ผˆ ์œ ํ˜•์˜ ๊ฒฉ์ž๋Š” ๋น„๋“ฑ๋ฐฉ์ ์ธ ๊ธฐํ•˜๋ฅผ ๊ฐ–๊ณ  ์žˆ์–ด, ๊ฒฉ์ž ๋‚ด์˜ ๋ฐฉํ–ฅ์— ๋”ฐ๋ผ ๋ฌผ์„ฑ์˜ ์ฐจ์ด๋ฅผ ๊ฐ€์งˆ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜์—ˆ๊ณ , ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋ฃจํ…Œ๋Š„ ์ดํ•ฉ์ฒด ๋‚ด์˜ ์ „์ž๋“ค์˜ ํ–‰ํƒœ๋ฅผ ํ™•์ธํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด์ „์˜ ์—ฐ๊ตฌ์—์„œ Li2RuO3์˜ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ๋ฌผ์„ฑ์ด ์ธก์ •๋˜์–ด ์™”์œผ๋‚˜, ์ด๋Š” ๋ชจ๋‘ ๋‹ค๊ฒฐ์ • ์‹œ๋ฃŒ๋ฅผ ์ด์šฉํ•œ ๊ฒƒ์ด์—ˆ๊ณ  ๋‹จ๊ฒฐ์ • ์‹œ๋ฃŒ๋Š” ํ•ฉ์„ฑ๋ฒ•์ด ๋ณด๊ณ ๋˜์ง€ ์•Š์•˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฐฉ์„ฑ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ณ ํ’ˆ์งˆ์˜ ๋‹จ๊ฒฐ์ • ์‹œ๋ฃŒ๋ฅผ ํ•ฉ์„ฑํ•˜๊ณ  ํ•ฉ์„ฑ๋œ ์‹œ๋ฃŒ์˜ ์ „๊ธฐ์ , ์ž๊ธฐ์  ๋น„๋“ฑ๋ฐฉ์„ฑ์„ ์ธก์ •ํ•˜๊ณ  ์ด๋ฅผ ๋ฐ€๋„ ๋ฒ”ํ•จ์ˆ˜ ์ด๋ก  ๊ณ„์‚ฐ๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ดํ•ฉ์ฒด ๋‚ด ๋‘ ๋ฃจํ…Œ๋Š„ ์ด์˜จ์˜ ์ „์ž ๊ฐ„์˜ ์ฟจ๋กฑ ๋ฐ˜๋ฐœ๋ ฅ ๊ณ ๋ คํ•ด์•ผ๋งŒ ์ „๊ธฐ์  ๋  ํ‹ˆ์˜ ์กด์žฌ๋ฅผ ๋ชจ์‚ฌํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด์— ์˜ํ•œ ์ „์ž ๊ฐ„ ์ƒ๊ด€ ๊ด€๊ณ„๊ฐ€ ์ „๊ธฐ์  ์ด๋ฐฉ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ž ๊ฐ„ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•œ ๋ชจ๋ธ์„ ์„ธ์›Œ ์™„์ „ ๋Œ€๊ฐํ™” ๊ณ„์‚ฐ์„ ํ†ตํ•˜์—ฌ ์ž๊ธฐ์  ๋น„๋“ฑ๋ฐฉ์„ฑ์„ ์žฌํ˜„ํ•ด๋‚ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋กœ๋ถ€ํ„ฐ, Li2RuO3 ์˜ ์ดํ•ฉ์ฒด์˜ ์ „์ž ๊ตฌ์กฐ์™€ ๊ทธ ๋ฌผ์„ฑ์„ ์„ค๋ช…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ „์ž ๊ฐ„ ์ƒ๊ด€ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•ด์•ผ ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋ณด๋‹ค ์ข€ ๋” Li2RuO3์˜ ์ „์ž๊ตฌ์กฐ์™€ ์Šคํ•€, ์˜ค๋น„ํƒˆ์˜ ๋ฐฐ์—ด ํ˜•ํƒœ์— ๋Œ€ํ•ด ์ง์ ‘์ ์ธ ๊ด€์ธก์„ ํ†ตํ•˜์—ฌ ์ดํ•ฉ์ฒด์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ์ฆ์ง„ํ•˜๊ณ ์ž X์„ ์„ ์ด์šฉํ•œ ์ผ๋ จ์˜ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ฃจํ…Œ๋Š„์€ 4์ฃผ๊ธฐ ์›์†Œ๋กœ์„œ ์•ฝ 2.9 keV์— ํ•ด๋‹นํ•˜๋Š” L-ํก์ˆ˜ ์„ ๋‹จ (absorption edge)์„ ๊ฐ–๋Š”๋ฐ, ์ด ์—๋„ˆ์ง€๋Š” ์ „ํ†ต์ ์œผ๋กœ ์—ฐX์„ ๊ณผ ๊ฒฝX์„ ์œผ๋กœ ๊ตฌ๋ถ„๋˜๋Š” ๋‘ ์˜์—ญ์˜ ์ค‘๊ฐ„์— ์œ„์น˜ํ•˜๋ฉฐ ๊ธฐ์ˆ ์  ์ด์œ ๋กœ ์ œ์–ด๊ฐ€ ํž˜๋“ค๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ•ด๋‹น ์˜์—ญ๋Œ€์˜ ์—๋„ˆ์ง€๋ฅผ ํ™œ์šฉํ•œ ์‹คํ—˜์„ ์ง€์›ํ•˜๋Š” ์˜๊ตญ์˜ Diamond ๋ฐฉ์‚ฌ๊ด‘ ๊ฐ€์†๊ธฐ์˜ I16 ๋น”๋ผ์ธ์—์„œ ํ•ด๋‹น ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. Li2RuO3 ์‹œ๋ฃŒ์— ๋Œ€ํ•˜์—ฌ ๋ฃจํ…Œ๋Š„ L2์™€ L3 ๋‘ ํก์ˆ˜ ์„ ๋‹จ์—์„œ X์„  ํก์ˆ˜ ๋ถ„๊ด‘ํ•™ (x-ray absorption spectroscopy)์„ ์‹ค์‹œํ•œ ๊ฒฐ๊ณผ, Li2RuO3์˜ ํก์ˆ˜ ์ŠคํŽ™ํŠธ๋Ÿผ์ด ํก์ˆ˜ ์„ ๋‹จ์— ๋”ฐ๋ผ ๋‹ค๋ฆ„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ (010) ๋ด‰์šฐ๋ฆฌ์—์„œ ๊ณต๋ช… ํƒ„์„ฑ X์„  ์‚ฐ๋ž€์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ , ๋‘ ํก์ˆ˜ ์„ ๋‹จ์—์„œ (010) ๊ณต๋ช… ๋ด‰์šฐ๋ฆฌ์˜ ๊ฑฐ๋™์ด ๋‹ค๋ฆ„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Š” ๊ณต๋ช… ๋Œ€์ƒ์ธ 2p ์†์ „์ž (core-electron)์˜ ๊ฐ์šด๋™๋Ÿ‰์— ๋”ฐ๋ผ ์ „์ด ๊ทœ์น™์ด ์ ์šฉ ๋จ์„ ์‹œ์‚ฌํ•˜๋ฉฐ, ์ดํ•ฉ์ฒด ๋‚ด์˜ t2g ์ „์ž์˜ ์Šคํ•€-๊ถค๋„ ๊ฒฐํ•ฉ์ด ์œ ํšจํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ ๋‘ ํก์ˆ˜ ์„ ๋‹จ์—์„œ์˜ ๊ณต๋ช… ํƒ„์„ฑ X์„  ์‚ฐ๋ž€ ์‹ ํ˜ธ์˜ ๋ฐฉ์œ„๊ฐ ์˜์กด์„ฑ์„ ํ™•์ธํ•œ ๊ฒฐ๊ณผ ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฉ์œ„๊ฐ ์˜์กด์„ฑ์„ ๊ฐ–๋Š” ๋‘ ์‹ ํ˜ธ๊ฐ€ ๊ฐ„์„ญํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‹คํ—˜์˜ ๊ฒฐ๊ณผ๋“ค์„ ๋‹จ์ผ ์ „์ž ๊ทผ์‚ฌ ๊ณ„์‚ฐ ์ฝ”๋“œ์ธ FDMNES ๊ณ„์‚ฐ๊ณผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ํฌ๊ฒŒ ๋งž์ง€ ์•Š์Œ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด๋Š” ์ดํ•ฉ์ฒดํ™”์— ์˜ํ•œ ์˜ค๋น„ํƒˆ์˜ ์ง์ ‘ ๊ฒน์นจ๊ณผ ์ด์— ๋”ฐ๋ฅธ ์ „์ž๊ฐ„ ์ƒ๊ด€์ด ๋ฌผ์งˆ์˜ ์ „์ž/์Šคํ•€/์˜ค๋น„ํƒˆ ๊ตฌ์กฐ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒฐ๊ณผ์ด๋‹ค. Li2RuO3๋Š” ๊ตฌ์กฐ ์ƒ์ „์ด (550 K)์ด์ƒ์˜ ์˜จ๋„์—์„œ๋Š” X์„  ์‚ฐ๋ž€ ์‹คํ—˜ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ๋ฒŒ์ง‘ ๋ชจ์–‘ ๊ฒฉ์ž ๋‚ด์— ์ดํ•ฉ์ฒด๊ฐ€ ์กด์žฌํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๊ฐ„์ฃผ๋˜์–ด์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ „์‚ฐ๋ž€ (total scattering)๋ฒ•์„ ์ด์šฉํ•œ ์ง๋ถ„ํฌ ํ•จ์ˆ˜ ๋ถ„์„ (pair distribution function analysis) ๊ฒฐ๊ณผ, ์ƒ์ „์ด ์˜จ๋„ ์ด์ƒ์—์„œ๋„ ์ดํ•ฉ์ฒด๊ฐ€ ์—ฌ์ „ํžˆ ๋‚จ์•„ ์žˆ์Œ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ๋ฌธ์—์„œ๋Š” ๊ฒฉ์ž ๋‚ด์˜ ๋ฃจํ…Œ๋Š„์„ ๋ง๊ฐ„์œผ๋กœ ์น˜ํ™˜ํ•œ ๊ณ ์šฉ์ฒด (solid solution)๋ฅผ ํ•ฉ์„ฑํ•˜์—ฌ ์ „์ด ๊ธˆ์† ์˜ค๋น„ํƒˆ ๊ฐ„ ์ง์ ‘ ๊ฒฐํ•ฉ์„ ๊ฐ์†Œ์‹œ์ผœ ์ƒ์ „์ด๊ฐ€ ๋ณ€ํ™”ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ํ™•์ธํ•˜๊ณ , ๊ด‘์—ญ ์—‘์Šค์„  ํก์ˆ˜ ๋ฏธ์„ธ ๊ตฌ์กฐ๋ฒ• (extended x-ray absorption fine structure, EXAFS) ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์น˜ํ™˜๋ฅ ๊ณผ ๊ณ„์˜ ์˜จ๋„์— ๋”ฐ๋ฅธ ๊ตญ์†Œ ๊ตฌ์กฐ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์น˜ํ™˜๋ฅ ์— ๋”ฐ๋ฅธ ๊ตฌ์กฐ์™€ ์ €ํ•ญ, ์žํ™”์œจ์˜ ๋ณ€ํ™”๋กœ๋ถ€ํ„ฐ ์น˜ํ™˜๋ฅ ์ด 20 %๊นŒ์ง€ Li2RuO3์™€ ๊ฐ™์€ ์ดํ•ฉ์ฒด๋ฅผ ํฌํ•จํ•˜๋Š” ์ฒญ์–ด๋ผˆ ๊ฒฉ์ž๋ฅผ ๊ฐ–๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ง๊ฐ„ ์น˜ํ™˜์— ๋”ฐ๋ผ ์ƒ์ „์ด ์‹œ์˜ ์—”ํŠธ๋กœํ”ผ ๋ณ€ํ™”๊ฐ€ ์„ ํ˜•์ ์œผ๋กœ ๊ฐ์†Œํ•˜๊ณ  ๋ฃจํ…Œ๋Š„ ์ด์˜จ ์ฃผ๋ณ€์˜ ๊ตญ์†Œ ๊ตฌ์กฐ๊ฐ€ ๊ฑฐ์˜ ๋ฐ”๋€Œ์ง€ ์•Š๋Š” ๊ฒƒ์„ ๊ด€์ธกํ•˜์˜€๋‹ค. ์ด๋Š” ์ „์ด ๊ธˆ์†์˜ ์ฃผ๊ธฐ์— ๋”ฐ๋ฅธ ์˜ค๋น„ํƒˆ ๋ฐ˜๊ฒฝ์˜ ์ฐจ์ด์— ์˜ํ•ด ๋ฃจํ…Œ๋Š„๊ณผ ๋ง๊ฐ„์ด ์ดํ•ฉ์ฒด๋ฅผ ํ˜•์„ฑํ•˜์ง€ ์•Š๊ณ , ๋ง๊ฐ„ ์น˜ํ™˜์€ ์ดํ•ฉ์ฒด ์ฒญ์–ด ๋ผˆ ๋ชจ์–‘ ๊ฒฉ์ž๋ฅผ ์™€ํ•ด์‹œํ‚ค๋Š” ์—ญํ• ์„ ํ•จ์„ ๋’ท๋ฐ›์นจํ•œ๋‹ค. ๋˜ํ•œ ๋ฃจํ…Œ๋Š„ ์ฃผ๋ณ€ ๊ตญ์†Œ ๊ตฌ์กฐ๊ฐ€ ์ƒ์ „์ด ์ „ํ›„๋กœ ๋ฐ”๋€Œ์ง€ ์•Š์Œ์„ ํ™•์ธํ•˜์˜€๋Š”๋ฐ, ์ด๋Š” ์ดํ•ฉ์ฒด๊ฐ€ ์ƒ์ „์ด์— ์˜ํ•ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์Œ์„ ์ง๋ถ„ํฌ ํ•จ์ˆ˜ ๋ถ„์„ ๊ฒฐ๊ณผ๋ณด๋‹ค ๋ถ„๋ช…ํ•˜๊ฒŒ ๋ณด์—ฌ์ค€๋‹ค.1. Introduction 1 1.1 Transition metal cluster in solid 1 1.1.1 Bonding in solids 1.1.2 Molecular orbital and Cluster formation 1.2 Li2RuO3: The layered honeycomb structural compound with dimerization 8 1.3 Outline of the thesis 14 2. Experimental Techniques 17 2.1 Sample synthesis 17 2.2 Resonant Elastic X-ray Scattering (REXS) 19 2.2.1 Electronic matter-radiation interaction Hamiltonian 2.2.2 Scattering cross-section (Elastic scattering) 3. Anisotropy and transition metal dimer in Li2RuO3 31 3.1 Structural distortion by the dimerization 31 3.1.1 Crystal Structure Analysis 3.1.2 The b/a ratio: a distortion parameter 3.2 Resistivity anisotropy and the dimerization 36 3.2.1 Resistivity anisotropy of Li2RuO3 3.2.2 DFT calculation 3.3 Van Vleck type magnetic Susceptibility and its anisotropy 40 3.3.1 Dimer array approximation and symmetry analysis 3.3.2 Exact diagonalization calculation 3.4 Discussion and Summary 46 4. Resonant Elastic X-ray Scattering on Li2RuO3 49 4.1 X-ray Absorption Spectroscopy on Li2RuO3 51 4.2 Searching superstructure reflection 52 4.3 Tensorial structure factor calculation for Li2RuO3 53 4.4 Characterization of the resonant reflection (010) 55 4.4.1 Polarization and temperature dependency 4.4.2 Azimuthal angle and absorption edge dependent behavior of (010)ฯƒฯ€ 4.5 Discussion and Summary 60 5. An Mn doping study on the valence bond solid phase in Li2RuO3 65 5.1 Valence Bond Liquid phase in Li2RuO3 65 5.2 Structural variation of Li2RuO3 by Mn doping 67 5.3 Physical Properties of Li2Ru1-xMnxO3 69 5.3.1 Electrical properties 5.3.2 Magnetic properties 5.3.3 Thermal properties of the phase transition 5.4 Local Structure variation in Li2RuO3 by Mn doping 73 5.5 Discussion and Summary 74 6. Summary and Outlook 79 6.1 Summary 79 6.2 Outlook 80 Appendix. FDMNES Code for Li2RuO3 81 Publication lists 83 ๊ตญ๋ฌธ ์ดˆ๋ก (Abstract in Korean) 85 ๊ฐ์‚ฌ์˜ ๊ธ€ (Acknowledgement) 88Docto

    ์ˆ˜์ง์  ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•œ ๋„์‹œ ์ˆ˜๋ชฉ์˜ ์ฆ์‚ฐ๋Ÿ‰ ์‚ฐ์ • ๋‹ค์ธต ๋ชจ๋ธ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2020. 8. ์ด๋™๊ทผ.As the urban heat island has been intensified, the cooling effect of urban trees is becoming important. Tree can reduce the radiant heat reaching the surface of the urban area by blocking or reflecting the radiant heat. In addition, the surface temperature of the tree is lower than that of the impervious surface such as asphalt and concrete, resulting in lower longwave radiation. Transpiration of tree also have cooling effect by releasing water into the atmosphere through the stomata of leaves, which reduces urban sensible heat by increasing latent heat. However, most previous studies which have conducted to calculate the transpiration rate have not focused on urban trees or oversimplified plant physiological process. I propose a multi-layer model for transpiration of urban tree accounting for plant physiological process considering the vertical structure of trees and buildings. It is expanded from urban canopy model to simulate photosynthetically active radiation and leaf surface temperature accurately. To evaluate how building and tree conditions affect transpiration, I simulate transpiration by scenarios varying conditions of building height, tree location and vertical leaf area variation of trees. Simulations are conducted on four LAD distribution of trees; (1) Constant Density (C.D), (2) High Density, few layers (H.D), (3) High Density in Middle layers (M.H.D), (4) High Density in lower layers (L.H.D). LAI and tree height is same in all cases. The scenarios include three types of surrounding building (12m, 24m, and 36m) and two types of tree location (South and North). One of the day that was a clear day, did not have rain back and forth, had high air temperature, low relative humidity is selected (1 August 2018) in Seoul (126.9658, 37.57142) to simulated, so that transpiration can occur highly. The result show transpirative-efficient LAD distribution differs depending on tree structure and surrounding building height. The north tree surrounded by low building is most efficient for transpiration. The difference in tree transpiration during a day is up to 24.1%(south), 13.2%(north) depending on the building height. In scenario where building height are high(3H) and low(1H), the variations in tree transpiration during a day is up to 8.3% (3H) and 7.4%(1H) according to LAD distribution. This model can be a useful tool for providing guideline on the plantation of thermo-efficient trees depending on the structure or environment of the city. And if radiant heat reduction effects are analyzed together in future studies, it will be able to get more accurate insight into the cooling effects of trees.๋„์‹œ ์—ด์„ฌ ํ˜„์ƒ์ด ์‹ฌํ•ด์ง์— ๋”ฐ๋ผ ๋„์‹œ ์ˆ˜๋ชฉ์˜ ๋ƒ‰๊ฐ ํšจ๊ณผ๊ฐ€ ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ๋‹ค. ์ˆ˜๋ชฉ์€ ๋ณต์‚ฌ์—ด์„ ์ฐจ๋‹จํ•˜๊ฑฐ๋‚˜ ๋ฐ˜์‚ฌ์‹œ์ผœ ๋„์‹œ ํ‘œ๋ฉด์— ๋„๋‹ฌํ•˜๋Š” ๋ณต์‚ฌ์—ด์„ ์ €๊ฐ์‹œํ‚ฌ ์ˆ˜ ์žˆ๊ณ , ์ˆ˜๋ชฉ์˜ ํ‘œ๋ฉด์˜จ๋„๋Š” ์•„์ŠคํŒ”ํŠธ๋‚˜ ์ฝ˜ํฌ๋ฆฌํŠธ ๋“ฑ์˜ ๋ถˆํˆฌ์ˆ˜ ํ‘œ๋ฉด๋ณด๋‹ค ๋‚ฎ์•„ ๋ฐฉ์ถœํ•˜๋Š” ์žฅํŒŒ ๋ณต์‚ฌ์—ด์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์ˆ˜๋ชฉ์˜ ์ฆ์‚ฐ ์ž‘์šฉ์€ ๋ฟŒ๋ฆฌ๋ฅผ ํ†ตํ•ด ํก์ˆ˜ํ•œ ๋ฌผ์„ ์žŽ์˜ ๊ธฐ๊ณต์„ ํ†ตํ•ด ๋Œ€๊ธฐ๋กœ ๋ฐฉ์ถœํ•จ์œผ๋กœ์จ ์ž ์—ด์„ ์ฆ๊ฐ€์‹œ์ผœ ํ˜„์—ด์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ฆ์‚ฐ๋Ÿ‰์„ ๊ณ„์‚ฐํ•˜๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋“ค์€ ๋„์‹œ ์ˆ˜๋ชฉ์— ์ง‘์ค‘ํ•˜์ง€ ์•Š๊ฑฐ๋‚˜, ์ˆ˜๋ชฉ์˜ ์ƒ๋ฆฌํ•™์ ์ธ ๊ณผ์ •์„ ์ง€๋‚˜์น˜๊ฒŒ ๋‹จ์ˆœํ™”ํ•œ๋‹ค. ๋‚˜๋Š” ์ˆ˜๋ชฉ๊ณผ ๊ฑด๋ฌผ์˜ ์ˆ˜์ง์  ๊ตฌ์กฐ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ๋„์‹œ ์ˆ˜๋ชฉ์˜ ์ฆ์‚ฐ๋Ÿ‰ ์‚ฐ์ • ๋‹ค์ธต ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๊ฒƒ์€ ๊ด‘ํ•ฉ์„ฑ ํ™œ์„ฑ ๋ฐฉ์‚ฌ์„ ๊ณผ ์žŽ์˜ ํ‘œ๋ฉด ์˜จ๋„๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ๋ชจ์˜ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋„์‹œ ์บ๋…ธํ”ผ ๋ชจ๋ธ์—์„œ ํ™•์žฅ๋˜์—ˆ๋‹ค. ๊ฑด๋ฌผ๊ณผ ์ˆ˜๋ชฉ ํ™˜๊ฒฝ์ด ์ฆ์‚ฐ์— ์ฃผ๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ฑด๋ฌผ ๋†’์ด, ์ˆ˜๋ชฉ์˜ ์œ„์น˜, ๊ทธ๋ฆฌ๊ณ  ์ˆ˜๋ชฉ์˜ ์ˆ˜์ง์  ์žŽ ๋ฉด์  ๋ถ„ํฌ์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๋Š” ์‹œ๋‚˜๋ฆฌ์˜ค๋“ค๋กœ ์ฆ์‚ฐ๋Ÿ‰์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์€ ๋„ค ๊ฐ€์ง€ ์žŽ ๋ฉด์  ๋ฐ€๋„(LAD) ๋ถ„ํฌ๋ฅผ ๊ฐ€์ง„ ์ˆ˜๋ชฉ์„ ์‹ค์‹œํ•˜์˜€๋‹ค; (1) ์ผ์ •ํ•œ ๋ฐ€๋„(C.D), (2) ๋†’์€ ๋ฐ€๋„์™€ ์ ์€ ์ธต (H.D), (3) ์ค‘์ธต๋ถ€์—์„œ์˜ ๋†’์€ ๋ฐ€๋„ (M.H.D), (4) ํ•˜์ธต๋ถ€์—์„œ ๋†’์€ ๋ฐ€๋„ (L.H.D). ์žŽ ๋ฉด์  ์ง€์ˆ˜(LAI)์™€ ์ˆ˜๋ชฉ์˜ ๋†’์ด๋Š” ๋ชจ๋“  ๊ฒฝ์šฐ์—์„œ ๋™์ผํ•˜์˜€๋‹ค. ์‹œ๋‚˜๋ฆฌ์˜ค๋Š” ์„ธ ๊ฐ€์ง€ ๊ฑด๋ฌผ ๋†’์ด(12m, 24m, ๊ทธ๋ฆฌ๊ณ  36m)์™€ ๋‘ ๊ฐ€์ง€ ์ˆ˜๋ชฉ ์œ„์น˜(๋‚จ์ชฝ, ๋ถ์ชฝ)์„ ํฌํ•จํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์œ„ํ•ด ์„œ์šธ์—์„œ ์ „ํ›„ ์‹œ๊ฐ„์— ๋น„๊ฐ€ ์˜ค์ง€ ์•Š์•˜๊ณ  ๋†’์€ ๊ธฐ์˜จ, ๋‚ฎ์€ ์Šต๋„๋ฅผ ๊ฐ€์ง„ ๋ง‘์€ ๋‚ (2018๋…„ 8์›” 1์ผ)์„ ์„ ์ •ํ•˜์—ฌ ์ฆ์‚ฐ ์ž‘์šฉ์ด ํฌ๊ฒŒ ์ผ์–ด๋‚˜๊ฒŒ ํ•˜์˜€๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์ˆ˜๋ชฉ ๊ตฌ์กฐ์™€ ์ฃผ๋ณ€ ๊ฑด๋ฌผ ๋†’์ด์— ๋”ฐ๋ผ ์ฆ์‚ฐ-ํšจ์œจ์ ์ธ LAD ๋ถ„ํฌ๊ฐ€ ๋‹ค๋ฅด๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‚ฎ์€ ๊ฑด๋ฌผ๋กœ ๋‘˜๋Ÿฌ์‹ธ์ธ ๋ถ์ชฝ ์ˆ˜๋ชฉ์€ ์ฆ์‚ฐ์— ๊ฐ€์žฅ ํšจ์œจ์ ์ด์—ˆ๋‹ค. ํ•˜๋ฃจ ๋™์•ˆ ์ˆ˜๋ชฉ์˜ ์ฆ์‚ฐ๋Ÿ‰์˜ ์ฐจ์ด๋Š” ๊ฑด๋ฌผ ๋†’์ด์— ๋”ฐ๋ผ ์ตœ๋Œ€ 24.1%(๋‚จ์ชฝ), 13.2%(๋ถ์ชฝ)๊นŒ์ง€ ์ฐจ์ด๊ฐ€ ๋‚ฌ๋‹ค. ๊ฑด๋ฌผ ๋†’์ด๊ฐ€ ๋†’๊ณ (3H), ๋‚ฎ์€(1H) ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ๋Š” LAD ๋ถ„ํฌ์— ๋”ฐ๋ผ ํ•˜๋ฃจ ์ค‘ ์ˆ˜๋ชฉ์˜ ์ฆ์‚ฐ๋Ÿ‰์˜ ํŽธ์ฐจ๊ฐ€ ์ตœ๋Œ€ 8.3%(3H), 7.4(1H)์˜€๋‹ค. ์ด ๋ชจ๋ธ์€ ๋„์‹œ์˜ ๊ตฌ์กฐ๋‚˜ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ์—ด ํšจ์œจ์ด ๋†’์€ ์ˆ˜๋ชฉ ์‹์žฌ์— ๊ด€ํ•œ ๊ฐ€์ด๋“œ๋ผ์ธ์„ ์ œ๊ณตํ•˜๋Š” ๋ฐ ์œ ์šฉํ•œ ๋„๊ตฌ๊ฐ€ ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ–ฅํ›„ ์—ฐ๊ตฌ์—์„œ ๋ณต์‚ฌ์—ด ์ €๊ฐ ํšจ๊ณผ์™€ ํ•จ๊ป˜ ๋ถ„์„ํ•œ๋‹ค๋ฉด ๋„์‹œ ์ˆ˜๋ชฉ์˜ ๋ƒ‰๊ฐ ํšจ๊ณผ์— ๋Œ€ํ•œ ๋ณด๋‹ค ์ •ํ™•ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์–ป์„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Chapter 1. Introduction ๏ผ‘ Chapter 2. Method ๏ผ” 2.1 Research flow ๏ผ” 2.2 Model description ๏ผ• 2.2.1 Input data ๏ผ• 2.2.2 Model processing ๏ผ– 2.3 Scenario simulation ๏ผ‘๏ผ• 2.3.1. Tree location ๏ผ‘๏ผ• 2.3.2. Building height ๏ผ‘๏ผ– 2.3.3. LAD distribution ๏ผ‘๏ผ– Chapter 3. Results and Discussion ๏ผ‘๏ผ™ 3.1. Parameter ๏ผ‘๏ผ™ 3.1.1. PAR & leaf surface temperature ๏ผ‘๏ผ™ 3.1.2. Resistances ๏ผ’๏ผ 3.2. Transpiration ๏ผ’๏ผ‘ 3.2.1. Temporal variation ๏ผ’๏ผ‘ 3.2.2. Scenarios simulation ๏ผ’๏ผ’ 3.3. Model limitations and future development ๏ผ’๏ผ• Chapter 4. Conclusion ๏ผ’๏ผ— Bibliography ๏ผ’๏ผ˜ Appendix ๏ผ”๏ผ‘Maste

    ์›ํ•ต๋ฏธ์ƒ๋ฌผ ๋ถ„๋ฅ˜์ฒด๊ณ„์— ๊ธฐ๋ฐ˜ํ•œ 16S rRNA ์œ ์ „์ž ๋ฐ ์œ ์ „์ฒด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ช…๊ณผํ•™๋ถ€, 2017. 8. ์ฒœ์ข…์‹.In prokaryotic taxonomy, the 16S ribosomal RNA (rRNA) gene sequence-based approach has served as an alternative standard method to DNA-DNA hybridization (DDH), for which the 97% 16S rRNA gene sequence similarity was considered to be equivalent to the 70% DDH value for species demarcation. While the 16S rRNA-based method is unable to perfectly classify and identify bacterial and archaeal species using 16S rRNA gene, it is currently the most general tool to evaluate the taxonomic position of a prokaryotic strain at the same genus or species levels. Therefore, the 16S rRNA-based approach is still important in the classification of prokaryotes and the use of a database with taxonomically well-curated sequences such as EzTaxon-e is essential for accurate species identification. There has been a recent evolution of DNA sequencing technologies, called next-generation sequencing (NGS), which has been facilitating Culture-independent microbial community analysis using 16S ribosomal RNA gene as well as the use of genome sequencing data for more informative and precise classification and identification of Bacteria and Archaea. Because the current species definition is based on the comparison of genome sequences between type and other strains in a given species, building a genome database with accurate taxonomic information is a premium need to enhance our efforts in exploring prokaryotic diversity and discovering new species as well as for routine identifications. In this study, an integrated database, called EzBioCloud, was constructed to hold the taxonomic hierarchy of Bacteria and Archaea that are represented by quality-controlled 16S rRNA gene and genome sequences. The various bioinformatics pipelines, tools, and algorithms which were applied during the construction of the database were also developed to optimally utilize the database contents. For a more efficient 16S rRNA-based analysis, the pairwise sequence alignment algorithm was improved and a high-performance microbial community analysis pipeline was newly developed in order to better facilitate the analysis of massive NGS data and to produce better results than conventional methods. For whole genome based analyses, quality assessment methods for genome assembly and a genome annotation pipeline were developed and evaluated. The full-length 16S rRNA extraction method and efficient average nucleotide identity (ANI) calculation algorithm were utilized in the identification of public prokaryotic genomes. In order to construct the integrated genome database, whole genome assemblies in the NCBI Assembly Database were first screened to determine low-quality genomes and then subsequently subjected to a composite identification bioinformatics pipeline that employed gene-based searches followed by the calculation of average nucleotide identity. The resulting database consisted of 61,700 species/phylotypes including 13,132 with validly published names, and 62,362 whole genome assemblies that were taxonomically identified at the genus, species and subspecies level. Genomic properties, such as genome size and GC content, and the occurrence in human microbiome data were calculated for each genus or higher taxa. This comprehensive database of taxonomy, 16S rRNA gene, and genome sequences, with its accompaniment of bioinformatics tools, should accelerate genome-based classification and identification of Bacteria and Archaea. The database and related search tools are available at http://www.ezbiocloud.net/.CHAPTER 1 General introduction 1 1.1. Taxonomy of prokaryotes 2 1.1.1. Principle of prokaryotic taxonomy 2 1.1.2. Prokaryotic species concept 4 1.2. Next generation sequencing (NGS) 8 1.2.1. 454 Pyrosequencing 8 1.2.2. Illumina-Solexa sequencing 10 1.2.3. Pacific Bioscience SMRT sequencing 11 1.3. Use of 16S rRNA gene in microbiology 13 1.4. Prokaryotic genomics 17 1.5. Objectives of this study 21 CHAPTER 2 Development of bioinformatics pipelines and tools for EzBioCloud database 23 2.1. Introduction 24 2.1.1. 16S rRNA based prokaryote identification algorithm 25 2.1.2. Microbial community analysis 27 2.1.3. 16S rRNA sequence in genome with short-read sequencing data 31 2.1.4. Public genome data of prokaryotes 31 2.1.5. Quality of genome assembly 32 2.1.6. Average nucleotide identity 33 2.2. Materials and method 36 2.2.1. Improvement of 16S rRNA sequence based identification algorithm 36 2.2.2. Development of microbial taxonomic profiling (MTP) pipeline 38 2.2.3. Method for extracting full-length 16S rRNA genes from short-read sequencing data 42 2.2.4. Pipeline for prokaryotic whole genome analysis 44 2.2.5. Methods for the quality assessment of genome 48 2.2.6. Efficient calculation method for average nucleotide identity 52 2.3. Results 54 2.3.1. Advanced microbial taxonomic profiling (MTP) pipeline 54 2.3.2. Comparison of full length 16S rRNA extraction methods 62 2.3.3. Annotation of public genomes 66 2.3.4. Quality of bacterial genomes 68 2.3.5. Evaluation of algorithms for average nucleotide identity 75 2.4. Discussion 81 CHAPTER 3 Development of EzBioCloud: A taxonomically united database of 16S rRNA and whole genome assemblies 84 3.1. Introduction 85 3.2. Methods 87 3.2.1. Data collection 87 3.2.2. Identification of genome sequences 90 3.2.3. Calculation of genomics features for each taxon 93 3.2.4. Bacterial community analysis of human microbiome 93 3.2.5. Operating system and software development 95 3.3. Results 96 3.3.1. Comparison of databases 96 3.3.2. Hierarchical taxonomic backbone 99 3.3.3. Identification of genome projects 103 3.3.4. Genome-derived information 107 3.4. Discussion 108 CHAPTER 4 General conclusions 111 REFERENCES 115 ๊ตญ๋ฌธ์ดˆ๋ก 130Docto

    ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ ์ œ์กฐ์—…์ฒด์— ํŠนํ™”๋œ ๊ตญ์ œ์šด์†ก๊ฒฝ๋กœ ์—ฐ๊ตฌ

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    Abstract Wind energy began to receive attention as a new alternative fuel since 20 years ago and is growing as a booming global business model. Power efficiency and reliability has greatly been improved during the past 15 years to perform the role as an alternative resource that replaces existing energy sources. Many suppliers and technological advancements are being introduced to provide quick installation of wind power generator around the world. Global wind power generation in the world has been continuously increasing for the past 10 years, accounting for over 30% of cumulative rate compared to total power generation. Global demand for wind power generation is gradually expanding due to restriction on carbon emission and environmental problems caused by increased greenhouse effect. Total wind power equipment capacity in 2008 was 120 GW (giga watt). The United States has largest market share in the world, followed by Germany, Spain and China that are progressively expanding their wind power equipment. Wind energy is receiving the spotlight with solar energy to replace fossil energy until 2020, as a means to reduce carbon dioxide emission and prevent further expansion of greenhouse effects. About 20 assembly parts are attached on the head of wind power equipment. Tower supporting this head is installed at a height of 80 ~ 150 meters from the ground. Size of tower components (container, flange, and frame) is being increased to a width of 2.3 ~ 5.5 meters. Size of such components can be further enlarged with future development of wind power generation technology and increase in energy use. The top two manufacturers of wind power equipment components in the world are located in Korea, as well as small and medium companies being established since 2009. Interest on transportation of products to major consumers such as Europe and the United States is demanding changes in existing perspective on distribution. The number one wind power equipment manufacturer in the world is company A (located in Jisa-dong, Gangseo-gu, Busan). This company is a free forging company that produces over 600,000 tons of wind power flange larger than 4.5 meters and exceeds annual sales of 300 billion won. From heating of original material until manufacture of finished products through forging and rough machining processes, materials must be transported 6.5 times on average. However, current traffic laws limit transportation by trucks up to a width of 2.3 meters unless a special permit on limited vehicles is obtained. Such limited vehicles can only be driven after midnight (00:00 ~ 06:00), which inevitably results in illegal drives. In fact, the company paid a fine of about 10 million won in 2010 on over-width vehicles. Considering the fact that there are 31 small and large corporations in Korea conducting similar businesses, size of total fines paid by these companies is probably beyond imagination. Furthermore during marine transportation for exports, FR container (flatrack container) is designated as an over-width cargo. There are serious problems in loading, as well as transportation to docks. The foremost task of wind power companies is to find an international transportation route for stable and economic transportation of large sized wind power components to wind power complexes in the United States, Germany, Spain, and China that are responsible for 65% of global wind power generation market. Since about 7 ~ 9% of sales is appropriated as distribution expense, potential value of this study is expected to be greater than 100 billion won. While existing studies were limited to specific transportation methods for large sized products, this study attempts to focus on an international transportation route for general application. The purpose is not only to create profit for specific companies but also to secure stable competitiveness of wind power generator component manufacturers in Korea, in the midst of growing wind power market around the world. Accordingly in this study, current transportation routes are classified into three types including access-priority route, economics-priority route, and convenience- priority route depending on distribution characteristics of wind power equipment in order to suggest transportation methods other than ships. Reflection of distribution characteristics of wind power equipment on wind power capacity to be expanded until 2030 will contribute to development of individual manufacturers and to create national benefit through transportation route applicable to other large sized products such as large plant industry and shipbuilding industry.โ… . ์„œ ๋ก  1 1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ ๋™ํ–ฅ 3 3. ์—ฐ๊ตฌ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 7 โ…ก. ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ์˜ ๋ฌผ๋ฅ˜์  ํŠน์„ฑ 11 1. ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ์˜ ๊ธฐ๋Šฅ 11 2. ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ์˜ ๋ฌผ๋ฅ˜์  ํŠน์„ฑ 15 3. ๊ตญ์ œ ๊ด€๋ จ๋ฒ•์˜ ํŠน์ง• 25 4. ํ’๋ ฅ๋ฐœ์ „ ๋ถ€ํ’ˆ์˜ ์šด์†ก์ œ์•ฝ 29 โ…ข. ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ์˜ ๊ตญ์ œ์šด์†ก๊ฒฝ๋กœ ํ˜„ํ™ฉ ๋ฐ ๋ฌธ์ œ์  32 1. ๊ตญ์ œ ์šด์†ก๊ฒฝ๋กœ ํ˜„ํ™ฉ 32 2. ๊ตญ์ œ ์šด์†ก๊ฒฝ๋กœ์ƒ ๋ฌธ์ œ์  49 โ…ฃ. ํ’๋ ฅ๋ฐœ์ „๋ถ€ํ’ˆ์— ํŠนํ™”๋œ ๊ตญ์ œ์šด์†ก๊ฒฝ๋กœ ์ œ์‹œ 63 1. ๋ฏธ๊ตญ์ง€์—ญ - SAVANNAH:์ ‘๊ทผ์„ฑ ์šฐ์„  ๊ฒฝ๋กœ 64 2. ์บ๋‚˜๋‹ค์ง€์—ญ -WINDSOR:๊ฒฝ์ œ์„ฑ ์šฐ์„  ๊ฒฝ๋กœ 69 3. ์œ ๋Ÿฝ์ง€์—ญ - GIJON:ํŽธ์˜์„ฑ ์šฐ์„  ๊ฒฝ๋กœ 76 4. ๊ตญ์ œ์šด์†ก๊ฒฝ๋กœ์— ๋Œ€ํ•œ ์ถ”๊ฐ€์ œ์•ˆ 80 โ…ค. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 84 1. ๊ฒฐ๋ก  84 2. ์ œ์–ธ 87 ใ€Š์ฐธ๊ณ  ๋ฌธํ—Œใ€‹ 8

    Magnetic nanoparticles control neural stem cell behavior in normal and ischemic brain

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    ์˜๊ณผ๋Œ€ํ•™/๋ฐ•์‚ฌOver the past few decades, the establishment of neural stem cells as a long-lasting source of neurons and glial cells had led to the development of novel therapeutic approaches for a variety of neurodegenerative disorders such as brain stroke. Neural stem cells graft promoted brain protection, regeneration and functional recovery. Nonetheless, the therapeutic benefits of neural stem cells had been limited due to their poor in vivo control of migration, engraftment and differentiation into target tissue. Recently, nanotechnologies are emerging platforms that could be useful in measuring, understanding and manipulating stem cells. Advanced nanoparticles, carbone nanotubes, and polyplexes have been widely used for stem cell labeling, tracking, differentiation and transplantation. Here we demonstrated magnetotactic human neural stem cells that can be directed to the desired target lesion via non-invasive, remote magnetic guidance. In the presence of an external magnetic field, the advanced magnetic nanoparticle allowed neural stem cells to possess strong attraction forces, which was sufficient for migration, and highly sensitive MRI contrast that enabled long-term tracking of neural stem cells. We found the enhanced migration and engraftment and promoted neuronal differentiation of non-invasively injected magnetotactic neural stem cells in animal stroke model which resulted in improved neurological function and pathology.ope

    Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

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    Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

    Real-Time Discrimination between Proliferation and Neuronal and Astroglial Differentiation of Human Neural Stem Cells

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    Neural stem cells (NSCs) are characterized by a capacity for self-renewal, differentiation into multiple neural lineages, all of which are considered to be promising components for neural regeneration. However, for cell-replacement therapies, it is essential to monitor the process of in vitro NSC differentiation and identify differentiated cell phenotypes. We report a real-time and label-free method that uses a capacitance sensor array to monitor the differentiation of human fetal brain-derived NSCs (hNSCs) and to identify the fates of differentiated cells. When hNSCs were placed under proliferation or differentiation conditions in five media, proliferating and differentiating hNSCs exhibited different frequency and time dependences of capacitance, indicating that the proliferation and differentiation status of hNSCs may be discriminated in real-time using our capacitance sensor. In addition, comparison between real-time capacitance and time-lapse optical images revealed that neuronal and astroglial differentiation of hNSCs may be identified in real-time without cell labeling.ope

    Human neural stem cells alleviate Alzheimer-like pathology in a mouse model

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    BACKGROUND: Alzheimer's disease (AD) is an inexorable neurodegenerative disease that commonly occurs in the elderly. The cognitive impairment caused by AD is associated with abnormal accumulation of amyloid-ฮฒ (Aฮฒ) and hyperphosphorylated tau, which are accompanied by inflammation. Neural stem cells (NSCs) are self-renewing, multipotential cells that differentiate into distinct neural cells. When transplanted into a diseased brain, NSCs repair and replace injured tissues after migration toward and engraftment within lesions. We investigated the therapeutic effects in an AD mouse model of human NSCs (hNSCs) that derived from an aborted human fetal telencephalon at 13 weeks of gestation. Cells were transplanted into the cerebral lateral ventricles of neuron-specific enolase promoter-controlled APPsw-expressing (NSE/APPsw) transgenic mice at 13 months of age. RESULTS: Implanted cells extensively migrated and engrafted, and some differentiated into neuronal and glial cells, although most hNSCs remained immature. The hNSC transplantation improved spatial memory in these mice, which also showed decreased tau phosphorylation and Aฮฒ42 levels and attenuated microgliosis and astrogliosis. The hNSC transplantation reduced tau phosphorylation via Trk-dependent Akt/GSK3ฮฒ signaling, down-regulated Aฮฒ production through an Akt/GSK3ฮฒ signaling-mediated decrease in BACE1, and decreased expression of inflammatory mediators through deactivation of microglia that was mediated by cell-to-cell contact, secretion of anti-inflammatory factors generated from hNSCs, or both. The hNSC transplantation also facilitated synaptic plasticity and anti-apoptotic function via trophic supplies. Furthermore, the safety and feasibility of hNSC transplantation are supported. CONCLUSIONS: These findings demonstrate the hNSC transplantation modulates diverse AD pathologies and rescue impaired memory via multiple mechanisms in an AD model. Thus, our data provide tangible preclinical evidence that human NSC transplantation could be a safe and versatile approach for treating AD patients.ope

    Human fetal brain-derived neural stem/progenitor cells grafted into the adult epileptic brain restrain seizures in rat models of temporal lobe epilepsy

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    Cell transplantation has been suggested as an alternative therapy for temporal lobe epilepsy (TLE) because this can suppress spontaneous recurrent seizures in animal models. To evaluate the therapeutic potential of human neural stem/progenitor cells (huNSPCs) for treating TLE, we transplanted huNSPCs, derived from an aborted fetal telencephalon at 13 weeks of gestation and expanded in culture as neurospheres over a long time period, into the epileptic hippocampus of fully kindled and pilocarpine-treated adult rats exhibiting TLE. In vitro, huNSPCs not only produced all three central nervous system neural cell types, but also differentiated into ganglionic eminences-derived ฮณ-aminobutyric acid (GABA)-ergic interneurons and released GABA in response to the depolarization induced by a high K+ medium. NSPC grafting reduced behavioral seizure duration, afterdischarge duration on electroencephalograms, and seizure stage in the kindling model, as well as the frequency and the duration of spontaneous recurrent motor seizures in pilocarpine-induced animals. However, NSPC grafting neither improved spatial learning or memory function in pilocarpine-treated animals. Following transplantation, grafted cells showed extensive migration around the injection site, robust engraftment, and long-term survival, along with differentiation into ฮฒ-tubulin III+ neurons (~34%), APC-CC1+ oligodendrocytes (~28%), and GFAP+ astrocytes (~8%). Furthermore, among donor-derived cells, ~24% produced GABA. Additionally, to explain the effect of seizure suppression after NSPC grafting, we examined the anticonvulsant glial cell-derived neurotrophic factor (GDNF) levels in host hippocampal astrocytes and mossy fiber sprouting into the supragranular layer of the dentate gyrus in the epileptic brain. Grafted cells restored the expression of GDNF in host astrocytes but did not reverse the mossy fiber sprouting, eliminating the latter as potential mechanism. These results suggest that human fetal brain-derived NSPCs possess some therapeutic effect for TLE treatments although further studies to both increase the yield of NSPC grafts-derived functionally integrated GABAergic neurons and improve cognitive deficits are still needed.ope

    Therapeutic Application of Neural Stem Cells for Neonatal Hypoxic-Ischemic Brain Injury

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    Neural stem cells (NSCs) are characterized by a capacity for self-renewal, differentiation into multiple neural cell lineages, and migration toward damaged sites in the central nervous system (CNS). NSCs expanded in culture could be implanted into the brain where they integrate into host neural circuitry and stably express foreign genes. It hence appears that transplantation of NSCs has been proposed as a promising therapeutic strategy in neurological disorders. During hypoxic-ischemic (HI) brain injury, factors are transiently elaborated to which NSCs respond by migrating to degenerating regions and differentiating towards replacement of dying neural cells. In addition, NSCs serve as vehicles for gene delivery and appear capable of simultaneous neural cell replacement and gene therapy (e.g. with factors that might enhance neuronal differentiation, neurites outgrowth, proper connectivity, neuroprotection, and/or immunomodulatory substances). When combined with certain synthetic biomaterials, NSCs may be even more effective in 'engineering' the damaged CNS towards reconstitution. Human NSCs were isolated from the forebrain of an aborted fetus at 13 weeks of gestation and were grown as neurospheres in cultures. After the characterization of human NSCs in preclinical testing and the approval of the IRB, a clinical trial of the transplantation of human NSCs into patients with severe perinatal HI brain injury has been performed. The existing data from these clinical trials have shown to be safe, well tolerated, and of neurologically-some benefits. Therefore, long-term and large scale multicenter clinical study is required to determine its precise therapeutic effect and safety.ope
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