11 research outputs found

    ์ดˆ์ƒ์ž์„ฑ ์‚ฐํ™”์ฒ  ๋‚˜๋…ธํด๋Ÿฌ์Šคํ„ฐ ์ค‘์‹ฌ@๋ฐฉ์‚ฌํ˜• ์‹ค๋ฆฌ์นด ํ”ผ๋ง‰์˜ ๋‚˜๋…ธ๋ณตํ•ฉ์ฒด์— ์ด์‚ฐํ™”ํ‹ฐํƒ€๋Š„ ๋‚˜๋…ธ์ž…์ž๊ฐ€ ๋ถ€์ฐฉ๋œ ์žฌ์‚ฌ์šฉ์ด ๊ฐ€๋Šฅํ•œ ๊ด‘์ด‰๋งค์— ๋Œ€ํ•œ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๋ถ€, 2017. 2. ๋ฐ•์›์ฒ .A magnetically recoverable photocatalyst was prepared by supporting TiO2 nanoparticles on a superparamagnetic iron oxide nanocluster (SION) core@fibrous silica shell (FSS) nanocomposite. Using the raspberry-shaped magnetic iron oxide nanocluster core as a seed, FSS with uniform thickness was grown directly on the core surface by a sol-gel process. The preparation method was optimized to have a single core for each nanoparticle by adjusting the amount of the silica source. The FSS has a fanning structure of radial pores, which enable large amounts of TiO2 nanoparticles to be supported easily on the pore surface. SION@FSS with amorphous TiO2 loaded on the pores (SION@FSS@Am-TiO2) was crystallized to the anatase phase (SION@FSS@A-TiO2), which shows good photocatalytic effect. When used for water purification, SION@FSS@A-TiO2 shows faster dye degradation kinetics compared to commercial P25 nanoparticles. The as-prepared SION@FSS@A-TiO2 photocatalyst could be magnetically recovered easily from water after decolorization of dye.Chapter 1. Introduction 1 1.1. TiO2 Photocatalyst 1 1.2. Mesoporous silica (MSN) supports 1 1.3. Fibrous silica materials 2 1.3.1. Characteristics and applications 2 1.3.2. Synthesis 3 1.4. Magnetically recoverable photocatalyst 4 1.4.1. Magnetic recollection 4 1.4.2. Superparamagnetic iron oxide nanocluster (SION) core 4 1.4.3. SION Core@Fibrous Silica Shell (FSS) 5 1.4.4. SION@FSS@Anatase TiO2 (A-TiO2) 6 Chapter 2. Experimental 7 2.1. Materials 7 2.2. Preparation of SION 7 2.3. Preparation of SION@FSS 8 2.4. Preparation of SION@FSS@Am-TiO2 and SION@FSS@A-TiO2 9 2.5. FSS thickness control of SION@FSS 11 2.6. Material Characterization 11 2.7. Photodecolorization Test 12 2.8. Recollection and Reusability Test 13 Chapter 3. Result and discussion 14 3.1. Synthetic procedures 14 3.1.1. Overview 14 3.1.2. SION core 15 3.1.3. FSS coating on SION core (SION@FSS) 15 3.1.4. Control of FSS thickness 19 3.1.5. Supporting TiO2 on SION@FSS and crystallization 20 3.1.6. Control of TiO2 amounts 22 3.2. characterization 26 3.2.1. Size and morphology 26 3.2.2. Crystal structure 29 3.2.3. Pore structure analysis 31 3.2.4. Magnetic property 33 3.3. Photodegradation of MetB 35 Chapter 4. Conclusion 42 References 43 ์š”์•ฝ(๊ตญ๋ฌธ์ดˆ๋ก) 48Maste

    ํƒœ๊ตญ ์—์ด์ฆˆ ๊ฐ์—ผ์ธ์˜ ์˜์•ฝํ’ˆ ์ ‘๊ทผ๊ถŒ ์šด๋™

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฅ˜ํ•™๊ณผ,2008.2Maste

    Discovery and Structure-Activity Relatioship of Novel AIMP2-DX2 Inhibitors as Anti-cancer Agents

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ, 2017. 2. ๋ฐ•ํ˜•๊ทผ.P38๋กœ๋„ ์•Œ๋ ค์ง„ AIMP2(ARS interacting multi-functional protein2)๋Š” Aminoacyl tRNA synthetase์˜ ๋ณด์กฐ๋‹จ๋ฐฑ์งˆ ์ค‘ ํ•˜๋‚˜๋กœ MSC(Multi-tRNA Synthetase Complex)๋ผ๋Š” ๋ณตํ•ฉ์ฒด๋ฅผ ์ด๋ฃจ๋ฉฐ ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ์— ๊ด€์—ฌํ•œ๋‹ค. ๊ธฐ๋ณธ์ ์œผ๋กœ AIMP2๋Š” TGF-beta์— ์˜ํ•œ ์„ฑ์žฅ ์–ต์ œ ์‹ ํ˜ธ๋ฅผ ๊ฐ•ํ™”์‹œํ‚ค๊ณ  p53์˜ ์œ ๋น„ํ€ดํ‹ดํ™”์„ ์–ต์ œํ•˜์—ฌ ์„ธํฌ์ž๋ฉธ์‚ฌ๋ฅผ ์œ ๋„ํ•˜๋Š” TNF๋ฅผ ๋งค๊ฐœํ•˜๋Š” ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜์—ฌ ์•”์„ ์–ต์ œํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค. AIMP2๋Š” ์—‘์† 1๋ฒˆ๋ถ€ํ„ฐ 4๋ฒˆ๊นŒ์ง€ ๋ชจ๋‘ ๋ฐœํ˜„๋œ full length๋กœ ์กด์žฌํ•˜์ง€๋งŒ ์Šคํ”Œ๋ผ์ด์‹ฑ ๋ณ€์ด์ฒด๋กœ ์—‘์† 2๋ฒˆ์ด ๊ฒฐ์—ฌ๋˜์–ด์žˆ๋Š” AIMP2-DX2(ARS interacting multi-functional protein2-Exon2 deleted)๋Š” AIMP2-Full length์™€ ์ƒ๋ฐ˜๋œ ์ž‘์šฉ์„ ํ•˜์—ฌ ๊ณผ ๋ฐœํ˜„ ๋  ๊ฒฝ์šฐ ๋ฐœ์•”์„ ์œ ๋ฐœํ•˜๊ฒŒ ๋˜๋ฉฐ ํŠนํžˆ ํ์•”์„ธํฌ์—์„œ ๊ณผ ๋ฐœํ˜„ ๋˜์–ด์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ์—ฐ๊ตฌ๋Š” AIMP2-Full length๋Š” ์ €ํ•ดํ•˜์ง€ ์•Š๊ณ  AIMP2-DX2๋งŒ์„ ์„ ํƒ์ ์œผ๋กœ ์ €ํ•ดํ•˜๋Š” ํ™”ํ•ฉ๋ฌผ์„ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ์‹คํ—˜์‹ค in-house library๋ฅผ ํ†ตํ•ด ์–ป์€ ์„ ๋„ ๋ฌผ์งˆ์˜ ์ €ํ•ด ์„ ํƒ์„ฑ, ํ™œ์„ฑ๊ณผ ๋ฌผ์„ฑ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด ์„ ๋„ ๋ฌผ์งˆ์˜ ๊ตฌ์กฐ๋ฅผ ์„ธ ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋ˆ„์–ด ๋‹ค์–‘ํ•œ ์ž‘์šฉ๊ธฐ ๋„์ž…ํ•˜์˜€์œผ๋ฉฐ ํ•ฉ์„ฑ ๊ฒฝ๋กœ์˜ key step์œผ๋กœ szuki coupling, aromatic nucleophilic substitution reaction, ๊ทธ๋ฆฌ๊ณ  microwave irradiation-mediated aromatic amination reaction์„ ์ด์šฉํ•ด ํ•ฉ์„ฑ ๊ณผ์ •์„ ์ตœ์ ํ™” ํ•˜์˜€๋‹ค. ํ•ฉ์„ฑ๋œ ์œ ๋„์ฒด๋“ค์˜ AIMP2-Full length & AIMP2-DX2 luciferase์™€ WI-26 & A549 cell line์— ๋Œ€ํ•œ cytotoxicity๋ฅผ ํ‰๊ฐ€ํ•˜์—ฌ SAR์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ AIMP2์— ๋น„ํ•ด AIMP2-DX2์— ๊ทธ๋ฆฌ๊ณ  ์ •์ƒ์„ธํฌ ๋Œ€๋น„ ์•”์„ธํฌ ์ €ํ•ด์— ๋›ฐ์–ด๋‚œ ์„ ํƒ์„ฑ์„ ๊ฐ€์ง€๋ฉฐ submicromolar ์ˆ˜์ค€์˜ AIMP2-DX2์ €ํ•ด ํ™œ์„ฑ๊ณผ in vitro ํ•ญ์•”ํ™œ์„ฑ์„ ๊ฐ€์ง€๋Š” ์‹ ๊ทœ ์ €๋ถ„์ž ํ™”ํ•ฉ๋ฌผ์„ ์ˆ˜์ข… ๊ฐœ๋ฐœํ•˜์˜€๋‹ค.I. ์„œ๋ก  1 1. ํ์•” ์น˜๋ฃŒ์ œ ๊ฐœ๋ฐœ์˜ ํ•„์š”์„ฑ 1 2. AIMP2-DX2 ์ €ํ•ด์— ๋”ฐ๋ฅธ ์•” ์–ต์ œ ํšจ๊ณผ 2 3. AIMP2-DX2 ์ €ํ•ด ์ €๋ถ„์ž ๋ฌผ์งˆ์„ ํ†ตํ•œ ํ•ญ์•” ์ž‘์šฉ 4 4. ์„ ํ–‰ ์—ฐ๊ตฌ 6 II. ๋ณธ๋ก  10 1. ์œ ๋„์ฒด ์„ค๊ณ„ ์ „๋žต 10 2. ํ•ฉ์„ฑ 12 2.1. A part modification 13 2.2. B part modification 15 2.3. C part modification 16 2.4. Position exchange 19 3. ํ™œ์„ฑ ํ‰๊ฐ€ 21 3.1. In vitro DX2 nanoluciferase activity results of A part modified analogues 21 3.2. In vitro DX2 nanoluciferase activity results of B part modified analogues 24 3.3. In vitro DX2 nanoluciferase activity results of C part modified analogues 25 3.4. In vitro DX2 nanoluciferase activity results of position exchanged analogues 28 3.5. ํˆฌ์—ฌ๋Ÿ‰ ์˜์กด์  ๋ฐ˜์‘๊ณผ ์„ ํƒ์„ฑ 29 III. ๊ฒฐ๋ก  31 IV. Experimental 33 References 80 Abstract 82Maste

    ๋‹ค์ค‘๊ฐ์ฒด์ถ”์ ์„ ์œ„ํ•œ ํŠน์ง• ๊ธฐ๋ฐ˜์˜ ํŒŒํ‹ฐํด ํ•„ํ„ฐ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 8. ์œ ์„์ธ.This paper proposes an advanced particle filter for multi-target tracking with speed-up robust features. In this study, a mixture of the Gaussian Background Model and the SURF algorithm are used for target representation and localization. This approach transforms an image into a large collection of local feature vectors, each of which is invariant to the images translation, scaling, and rotation. Additionally, it is also partially invariant to illumination changes and affine or 3D projection. Lastly, NN algorithm is used for segmenting multiple objects into a single-object state space. Several experimental results show that the proposed algorithm has good performance for object tracking in the presence of object translation, rotation and partial occlusion. Overall, this approach makes the system robust to occlusions and allows false positive detections in the background to be identified and removed.Chapter 1 Introduction Chapter 2 Related Work 2.1 Object Detection 2.1.1 Geometry-based Object Detection 2.1.2 Appearance-based Object Detection 2.1.3 Feature-based Object Detection 2.2 Data Association 2.3 Multi-targets Tracking 2.3.1 Bayesian Filtering Chapter 3 Object Detection and Matching 3.1 Fast Interest Point Detection 3.2 Descriptor of Interest Point 3.3 Object Matching Chapter 4 Particle Filter for Object Tracking Chapter 5 Experimental Result 5.1 Environment 5.2 Result 5.2.1 Single Object Detection 5.2.2 Key Points Extraction 5.2.3 Key Points Matching 5.2.4 Multi-Objects Tracking 5.2.5 Comparing performance Chapter 6 Conclusion Bibliography AbstractMaste

    Urban Regeneration and Gentrification

    No full text
    TRU
    corecore