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    ๋ณต์žกํ•œ ๋™ํŠน์„ฑ์„ ๊ฐ–๋Š” ๋‹ค์ƒ ๋ฐ˜์‘๊ธฐ์˜ ์„ค๊ณ„๋ฅผ ์œ„ํ•œ ๊ณ„์‚ฐ ํšจ์œจ์ ์ธ ๋ชจ์‚ฌ ๋ฐ ์ตœ์ ํ™” ์ „๋žต

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€,2020. 2. ์ด์ข…๋ฏผ.๋ณธ ๋ฐ•์‚ฌํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” ๋ฉ€ํ‹ฐ ์Šค์ผ€์ผ ๋ชจ๋ธ๋ง, ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ๋ณด์ •๋ฒ•, ์ตœ์ ํ™” ์ˆœ์œผ๋กœ ์ง„ํ–‰๋˜๋Š” ์‚ฐ์—…์šฉ ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ์˜ ์„ค๊ณ„ ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ๋ฐ˜์‘๊ธฐ๋Š” ํ™”ํ•™ ๊ณต์ •์—์„œ ์ œ์ผ ์ค‘์š”ํ•œ ๋‹จ์œ„์ด์ง€๋งŒ, ๊ทธ ์„ค๊ณ„์— ์žˆ์–ด์„œ๋Š” ์ตœ์‹  ์ˆ˜์น˜์  ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค๋Š” ์—ฌ์ „ํžˆ ๊ฐ„๋‹จํ•œ ๋ชจ๋ธ์ด๋‚˜ ์‹คํ—˜ ๋ฐ ๊ฒฝํ—˜ ๊ทœ์น™์— ์˜์กดํ•˜๊ณ  ์žˆ๋Š” ํ˜„์‹ค์ด๋‹ค. ์‚ฐ์—… ๊ทœ๋ชจ์˜ ๋ฐ˜์‘๊ธฐ๋Š” ๋ฌผ๋ฆฌ, ํ™”ํ•™์ ์œผ๋กœ ๋ชน์‹œ ๋ณต์žกํ•˜๊ณ , ๊ด€๋ จ ๋ณ€์ˆ˜ ๊ฐ„์˜ ์Šค์ผ€์ผ์ด ํฌ๊ฒŒ ์ฐจ์ด๋‚˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„ ์ˆ˜ํ•™์  ๋ชจ๋ธ๋ง ๋ฐ ์ˆ˜์น˜์  ํ•ด๋ฒ•์„ ๊ตฌํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋ชจ๋ธ์„ ๋งŒ๋“ค๋”๋ผ๋„ ๋ถ€์ •ํ™•ํ•˜๊ฑฐ๋‚˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„์ด ๋„ˆ๋ฌด ๊ธด ๋ฌธ์ œ๊ฐ€ ์žˆ์–ด ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ ์šฉํ•˜๊ธฐ๊ฐ€ ํž˜๋“ค๋‹ค. ๋ฐ˜์‘๊ธฐ ๋‚ด ํ˜„์ƒ์˜ ๋ณต์žก์„ฑ๊ณผ ์Šค์ผ€์ผ ์ฐจ์ด ๋ฌธ์ œ๋Š” ๋ฉ€ํ‹ฐ ์Šค์ผ€์ผ ๋ชจ๋ธ๋ง์„ ํ†ตํ•ด ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „์‚ฐ์œ ์ฒด์—ญํ•™ ๊ธฐ๋ฐ˜ ๊ตฌํš ๋ชจ๋ธ(CFD-based compartmental model)์„ ์ด์šฉํ•˜๋ฉด, ๋ถˆ๊ท ์ผํ•œ ํ˜ผํ•ฉ ํŒจํ„ด์„ ๋ณด์ด๋Š” ๋Œ€ํ˜• ๋ฐ˜์‘๊ธฐ์—์„œ๋„ ๊ธด ์‹œ๊ฐ„ ๋™์•ˆ์˜ ๋™์  ๋ชจ์‚ฌ๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด ๋ชจ๋ธ์€ ํฐ ๋ฐ˜์‘๊ธฐ๋ฅผ ์™„๋ฒฝํ•˜๊ฒŒ ๊ท ์ผํ•œ ์ž‘์€ ๊ตฌํš๋“ค์˜ ๋„คํŠธ์›Œํฌ๋กœ ๊ฐ„์ฃผํ•˜๊ณ , ๊ฐ ๊ตฌํš์„ ๋ฐ˜์‘ ์†๋„์‹๋“ค๊ณผ CFD ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ๊ฐ€์ ธ์˜จ ์œ ๋™ ์ •๋ณด๊ฐ€ ํฌํ•จ๋œ ์งˆ๋Ÿ‰ ๋ฐ ์—๋„ˆ์ง€ ๊ท ํ˜• ๋ฐฉ์ •์‹์œผ๋กœ ํ‘œํ˜„ํ•œ๋‹ค. ๊ธฐ์ฒด, ์•ก์ฒด, ๊ณ ์ฒด 3์ƒ์ด ์ƒํ˜ธ์ž‘์šฉํ•˜๋ฉฐ ๋ณต์žกํ•œ ์œ ๋™์„ ๋ณด์ด๋Š” ์ˆ˜์„ฑ ๊ด‘๋ฌผ ํƒ„์‚ฐํ™” ๋ฐ˜์‘๊ธฐ๋ฅผ ์ด ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•ด ๋ชจ๋ธ๋งํ•˜์˜€๋‹ค. ์ด ๋•Œ ๋ชจ๋ธ์€ ๋ฏธ๋ถ„ ๋Œ€์ˆ˜ ๋ฐฉ์ •์‹(DAE)์˜ ํ˜•ํƒœ๋ฅผ ๋ ๋ฉฐ, ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ƒ ๋ชจ๋“  ๋ฐ˜์‘๋“ค(๊ธฐ-์•ก ๊ฐ„ ๋ฌผ์งˆ ์ „๋‹ฌ ๋ฐ˜์‘, ๊ณ ์ฒด ์šฉํ•ด ๋ฐ˜์‘, ์ด์˜จ ๊ฐ„ ๋ฐ˜์‘, ์•™๊ธˆ ์นจ์ „ ๋ฐ˜์‘)๊ณผ ์œ ์ฒด ์—ญํ•™, ๋ฐ˜์‘์—ด, ์—ด์—ญํ•™์  ๋ณ€ํ™” ๋ฐ ์šด์ „ ์ƒ์˜ ์ด๋ฒคํŠธ ๋ฐœ์ƒ์„ ๋ชจ๋‘ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์ด์‚ฐํ™”ํƒ„์†Œ ์ œ๊ฑฐ ํšจ์œจ, pH ๋ฐ ์˜จ๋„ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•˜์—ฌ ์‹ค์ œ ์šด์ „ ๋ฐ์ดํ„ฐ์™€ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํ†ตํ•œ ๋ณด์ •์ด ์ „ํ˜€ ์—†์ด๋„ 7 % ์ด๋‚ด์˜ ์˜ค์ฐจ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋ชจ๋ธ์˜ ๋ถ€์ •ํ™•์„ฑ ๋ฌธ์ œ๋Š” ๋ชจ๋ธ๋ง ํ›„ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•œ ๋ชจ๋ธ ๋ณด์ •์œผ๋กœ ๊ทน๋ณต ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ด‘๋ฌผ ํƒ„์‚ฐํ™” ๋ฐ˜์‘๊ธฐ ๋ชจ๋ธ์„ ๋ฒ ์ด์ง€์•ˆ ๋ณด์ •(Bayesian calibration)์„ ํ†ตํ•ด ๊ฐ•ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ๋จผ์ € ๋ชจ๋ธ ์ค‘ ๋ถˆํ™•์‹คํ•œ ๋ถ€๋ถ„์— 8๊ฐœ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๋„์ž…ํ•œ ํ›„, ๋ฒ ์ด์ง€์•ˆ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ •๋ฒ•(Bayesian parameter estimation) ๋ฐ ์‹คํ—˜์‹ค ๊ทœ๋ชจ์—์„œ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์„ ์ด์šฉํ•˜์—ฌ ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์˜ ์‚ฌํ›„ ํ™•๋ฅ  ๋ถ„ํฌ๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ์–ป์–ด์ง„ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ํ™•๋ฅ  ๋ถ„ํฌ๋“ค์€ ๋ชจ๋ธ ๋ฐ ์‹คํ—˜์˜ ๋ถˆ์™„์ „์„ฑ์œผ๋กœ ์ธํ•ด ๋‚˜ํƒ€๋‚˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ๋ถˆํ™•์‹ค์„ฑ ๋ฐ ๋‹ค์ค‘ ๋ด‰์šฐ๋ฆฌ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ž˜ ๋”ฐ๋ผ๊ฐ€๋Š” ํ™•๋ฅ ๋ก ์  ๋ชจ๋ธ ์˜ˆ์ธก์น˜(stochastic model response)๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. 16๊ฐœ์˜ ์‹คํ—˜ ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ํ…Œ์ŠคํŠธ์…‹์˜ ํ”ผํŒ… ์—๋Ÿฌ(fitting error)๋Š” ๊ฒฐ์ •๋ก ์ ์ธ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜(deterministic optimization)์„ ์‚ฌ์šฉํ•  ๋•Œ๋ณด๋‹ค ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ์ธก์ •๋˜์—ˆ๋‹ค. ์ˆ˜ํ•™์  ์ตœ์ ํ™”์— ์“ฐ์ด๊ธฐ์— ๋„ˆ๋ฌด ๊ธด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„ ๋ฌธ์ œ๋Š” ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์—ฌ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋‹ค. ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ ์„ค๊ณ„ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹ค์ค‘ ๋ชฉ์  ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™”(Multi-objective Bayesian Optimization, MBO)๋ฅผ ์‚ฌ์šฉํ•ด ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํšŸ์ˆ˜๋ฅผ ์ตœ์†Œํ™” ํ•˜๋Š” CFD ๊ธฐ๋ฐ˜ ์ตœ์  ์„ค๊ณ„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์—ฌ์„ฏ ๊ฐ€์ง€ ์„ค๊ณ„ ๋ณ€์ˆ˜๋ฅผ ๊ฐ€์ง€๋Š” ๊ธฐ-์•ก ๊ต๋ฐ˜ ํƒฑํฌ ๋ฐ˜์‘๊ธฐ์—์„œ ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ตœ์†Œํ™”ํ•˜๊ณ  ๊ฐ€์Šค ๋ถ„์œจ(gas holdup)๋ฅผ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์ด ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•œ ๊ฒฐ๊ณผ, ๋‹จ 100 ํšŒ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋งŒ์œผ๋กœ ์ตœ์  ํŒŒ๋ ˆํ†  ์ปค๋ธŒ(Pareto curve)๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์ตœ์  ์„ค๊ณ„์•ˆ๋“ค์€ ๋ฌธํ—Œ์— ๋ณด๊ณ ๋œ ๊ธฐ์กด ๋ฐ˜์‘๊ธฐ๋“ค๊ณผ ๋น„๊ตํ•ด ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. . ๋ณธ ๋…ผ๋ฌธ์„ ํ†ตํ•ด ์ œ์•ˆ๋œ CFD ๊ธฐ๋ฐ˜ ๊ตฌํš ๋ชจ๋ธ๋ง๋ฒ•, ๋ฒ ์ด์ง€์•ˆ ๋ชจ๋ธ ๋ณด์ •๋ฒ• ๋ฐ ๋ฒ ์ด์ง€์•ˆ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์€ ๋ณต์žกํ•œ ๋ฌผ๋ฆฌ์  ๋ฐ ํ™”ํ•™์  ํŠน์ง•์„ ๊ฐ–๋Š” ์‚ฐ์—… ๊ทœ๋ชจ์˜ ํ™”ํ•™ ๋ฐ˜์‘๊ธฐ์— ์ ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.This thesis presents a design strategy for industrial-scale chemical reactors which consists of multi-scale modeling, post-modeling calibration, and optimization. Although the reactor design problem is a primary step in the development of most chemical processes, it has been relied on simple models, experiments and rules of thumbs rather than taking advantage of recent numerical techniques. It is because industrial-size reactors show high complexity and scale differences both physically and chemically, which makes it difficult to be mathematically modeled. Even after the model is constructed, it suffers from inaccuracies and heavy simulation time to be applied in optimization algorithms. The complexity and scale difference problem in modeling can be solved by introducing multi-scale modeling approaches. Computational fluid dynamics (CFD)-based compartmental model makes it possible to simulate hours of dynamics in large size reactors which show inhomogeneous mixing patterns. It regards the big reactor as a network of small zones in which perfect mixing can be assumed and solves mass and energy balance equations with kinetics and flow information adopted from CFD hydrodynamics model at each zone. An aqueous mineral carbonation reactor with complex gasโ€“liquidโ€“solid interacting flow patterns was modeled using this method. The model considers the gas-liquid mass transfer, solid dissolution, ionic reactions, precipitations, hydrodynamics, heat generation and thermodynamic changes by the reaction and discrete operational events in the form of differential algebraic equations (DAEs). The total CO2 removal efficiency, pH, and temperature changes were predicted and compared to real operation data. The errors were within 7 % without any post-adjustment. The inaccuracy problem of model can be overcome by post-modeling approach, such as the calibration with experiments. The model for aqueous mineral carbonation reactor was intensified via Bayesian calibration. Eight parameters were intrduced in the uncertain parts of the rigorous reactor model. Then the calibration was performed by estimating the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. The developed Bayesian parameter estimation framework involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflected the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They were used to earn stochastic model responses which show good fits with the experimental results. The fitting errors of all the 16 datasets and the unseen test set were measured to be comparable or lower than when deterministic optimization methods are used. The heavy simulation time problem for mathematical optimization can be resolved by applying Bayesian optimizaion algorithm. CFD based optimal design tool for chemical reactors, in which multi-objective Bayesian optimization (MBO) is utilized to reduce the number of required CFD runs, is proposed. The developed optimizer was applied to minimize the power consumption and maximize the gas holdup in a gas-sparged stirred tank reactor, which has six design variables. The saturated Pareto front was obtained after only 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. It is anticipated that the suggested CFD-based compartmental modeling, post-modeling Bayesian calibration, and Bayesian optimization methods can be applied in general industrial-scale chemical reactors with complex physical and chemical features.1. Introduction 1 1.1. Industrial-scale chemical reactor design 1 1.2. Role of mathematical models in reactor design 2 1.3. Intensification of reactor models through calibration 5 1.3.1. Bayesian parameter estimation 6 1.4. Optimization of the reactor models 7 1.4.1. Bayesian optimization 9 1.5. Aqueous mineral carbonation process : case study subject 10 1.6. Outline of the thesis 12 2. Multi-scale modeling of industrial-scale aqueous mineral carbonation reactor for long-time dynamic simulation 14 2.1. Objective 14 2.2. Experimental setup 15 2.3. Mathematical models 19 2.3.1. Reactor model 19 2.3.2. CFD model 28 2.3.3. Numerical setting 30 2.4. Results and discussions 32 2.4.1. CFD-based compartmental model for industrial-scale reactor. 32 2.4.2. Design and simulation of higher-scale reactors 42 2.5. Conclusions 47 3. Model intensification of aqueous mineral carbonation kinetics via Bayesian calibration 50 3.1. Objective 50 3.2. Experimental methods 51 3.2.1. Solution and gas preparation 51 3.2.2. Laboratory-scale mineral carbonation process 53 3.3. Mathematical models 56 3.3.1. Kinetics of aqueous mineral carbonation process 56 3.3.2. Differential algebraic equation (DAE) model for the reactor 65 3.3.3. Discrete events for simulation procedure 71 3.3.4. Numerical setting 72 3.4. Bayesian parameter estimation 72 3.4.1. Problem formulation 73 3.4.2. Bayesian posterior inference 76 3.4.3. Sampling 81 3.5. Results and discussions 82 3.5.1. Stochastic output response 82 3.5.2. Quality of parameter estimtates 86 3.5.3. Assessment of parameter uncertainties 91 3.5.4. Kinetics study with the proposed model parameters 99 3.6. Conclusions 103 4. Multi-objective optimization of chemical reactor design using computational fluid dynamics 106 4.1. Objective 106 4.2. Problem Formulation 107 4.3. Optimization scheme 113 4.3.1. Multi-objective optimization algorithm 113 4.3.2. CFD-MBO optimizer 120 4.4. CFD modeling 125 4.4.1. Tank specifications 125 4.4.2. Governing equations 125 4.4.3. Simulation methods 127 4.5. Results and discussion 128 4.5.1. CFD model validation 128 4.5.2. Optimization results 130 4.5.3. Analysis of optimal designs 139 4.6. Conclusions 144 5. Concluding Remarks 146 Bibliography 149 Abstract in Korean (๊ตญ๋ฌธ์ดˆ๋ก) 163Docto

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    ์ž„์ƒ๋ณ‘๋ฆฌํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] Vancomycin์€ ์„ธํฌ๋ฒฝ์˜ ํ•ฉ์„ฑ์„ ์–ต์ œํ•˜์—ฌ ์„ธ๊ท ์— ๋Œ€ํ•œ ํ•ญ๊ท ์ตธ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” glycopeptide๊ณ„ ํ•ญ์ƒ ๋ฌผ์งˆ๋กœ์„œ ๊ทธ๋žŒ ์–‘์„ฑ์„ธ๊ท ์œผ๋กœ ์ธํ•œ ๊ฐ์—ผ์น˜๋ฃŒ์— ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜๋ฉฐ, ํŠนํžˆ methicillin ๋‚ด์„ฑ ํฌ๋„์ƒ๊ตฌ๊ท ์™€ ์„ ํƒ์  ์น˜๋ฃŒ์ œ๋กœ ์“ฐ์ด๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ์ž„์ƒ๊ฒ€์ฒด์—์„œ๋„ ์ค‘๋“ฑ๋„์˜ ๋‚ด์„ฑ์„ ๊ฐ€์ง€๋Š” ํฌ๋„์ƒ๊ตฌ๊ท  (Mu5O: MIC 8 ใŽ/ใŽ–)์ด ๋‚˜ํƒ€๋‚˜๊ธฐ ์‹œ์ž‘ํ•˜์˜€๊ณ  ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ์—ฌ๊ฑด์ƒ ๊ตญ๋‚ด์—์„œ๋„ ๋‚ด์„ฑ๊ท ์ฃผ๊ฐ€ ๋ถ„๋ฆฌ๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค๊ณ  ์‚ฌ๋ฃŒ๋˜์–ด ์ž„์ƒ๊ฒ€์ฒด ์ค‘ methcillin ๋‚ด์„ฑ ํฌ๋„์ƒ๊ตฌ๊ท ์„ ๋Œ€์ƒ์œผ๋กœ vancomycin ๊ฐ์ˆ˜์„ฑ ๋ฐ ๋‚ด์„ฑ ๋นˆ๋„ ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜๊ณ  ์ด์— ๋”ฐ๋ฅธ ๋‚ด์„ฑ ๊ธฐ์ „์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์‹คํ—˜ ๊ฒฐ๊ณผ 107 ์ฃผ(ๆ ช)์˜ methiciILin ๋‚ด์„ฑ๊ท ์ฃผ ์ค‘ 23.3%๊ฐ€ vancomycin์— ๋Œ€ํ•˜์—ฌ ๋‚ด์„ฑ์„ ๋ณด์˜€์œผ๋ฉฐ vancomycin ๋‚ด์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ํ‘œ์ค€ ๊ท ์ฃผ์ธ Mu5O๊ณผ Mu3์˜ ์ค‘๊ฐ„์ •๋„์˜ ๋‚ด์„ฑ๋นˆ๋„๋ฅผ ๋ณด์˜€๋‹ค. ์ค‘ํ•ฉํšจ์†Œ ์—ฐ์‡„๋ฐ˜์‘์„ ํ†ตํ•ด ์žฅ๊ตฌ๊ท ์˜ vancomycin ๋‚ด์„ฑ์— ๊ด€์—ฌํ•˜๋Š” vanA, vanB, vanCl, vanC2, vanH ํŠน์ด ์œ ์ „์ž๋Š” ์ฆํญ๋˜์ง€ ์•Š์•˜๋‹ค. SDS-PAGE๋ฅผ ์‹ค์‹œํ•˜์—ฌ 81kDa,58kDa,33kDa,28k0a ๋“ฑ์˜ ์ฃผ์š”๋‹จ๋ฐฑ ๋ถ„ํš์„ ํ™•์ธํ•˜์˜€๊ณ , Mu5O์—์„œ 45kDa์˜ ํŠน์ง•์ ์ธ ๋‹จ๋ฐฑ ๋ถ„ํš์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. LDH assay์—์„œ๋Š” ํ•œ ๊ฐœ์˜ ๊ฒ€์ฒด๊ฐ€ Mu5O๊ณผ ํ•จ๊ป˜ ๋†’์€ LDH ํ™œ์„ฑ์„ ๋ณด์˜€๋‹ค. ์•ž์œผ๋กœ ์ •ํ™•ํ•œ ๋‚ด์„ฑ๊ธฐ์ค€์„ ๋ฐํžˆ๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด์•ผ ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ] The vancomycin, one of the family of glycopeptide antibiotics, inhibits the synthesis of bacterial cell wall peptidoglycan and has been widely used againstgram-positive bacterial infections, especially for a treaoent of methicillinresistant 5. 7[ureus infection. However, clinical isolate which was intermediatelyresistant to vancomycin (Mu5O: MIC 87g17) was isolated in recent years. Inthis study we performed vancomycin susceptibility test with the incrementmethod and population analysis with clinical isolates 5. aureus. It was alsoperformed that several kinds of tests with three selected isolates (s129: MIC 77g/m7, s134: MIC 7 7g1m7, s135: MIC 8 791m7) to find out possible mechanismof vancomycin resistance. As a result, the prevalence of vancomycin resistant 5.aureus isolates among 5. aureus strains resistant to methicillin was 23.3%(25/107. The vancomycin resistances of isolated strains of 5. aureus werebetween those of Mu5O and Mu3 strains. By PCR analysis, none of the isolateswith decreased vancomycin susceptibility contained known vancomycin resistant genes such as vanA, vanB, vanCl, vanC2, and vanH. Maior bands of 81 kDa, 58kDa, 33 kDa, 28 kDa were demonstrable in whole cell Iysates by SDS-PAGE from all three isolates as well as reference strains. And especially, 45 kDa protein was overproduced in Mu5O strains. Among them increased production of NAD**+ -linked- ^^D -lactate dehydrogenase (dnLDH) were detected from one clinical strain (s135) and Mu5O strain. From these data, we suggest that the mechanism of vancomycin resistance in these isolates are distinct from that in enterococci.restrictio

    A Study on the chemical vapor deposition of Ru and RuOโ‚‚thin film with ruthenocene precursor

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    A Study of Leisure Sport, Smart Phone Addiction, Sleep Quality, and School Adaptation among Elementary School Students

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