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- 저자 : 오유근(You Keun Oh) (검색결과 5 건)
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CeO<sub>2</sub> 코팅을 통한 Cu 입자의 입성장 억제 효과에 관한 연구
유희준,문지웅,오유근,문주호,황해진,Yoo Hee-Jun,Moon Ji-Woong,Oh You Keun,Moon Jooho,Hwang Hae Jin 한국분말야금학회 2005 한국분말재료학회지 (KPMI) Vol.12 No.6
Copper is able to work as a current collector under wide range of hydrocarbon fuels without coking in Solid oxide fuel cells (SOFCs). The application of copper in SOFC is limited due to its low melting point, which result in coarsening the copper particle. This work focuses on the sintering of copper powder with ceria coating layer. Ceria-coated powder was prepared by thermal decomposition of urea in $Ce(NO_3)_3\cdot6H_2O$ solution, which containing CuO core particles. The ceria-coated powder was characterized by XRD, ICP, and SEM. The thermal stability of the ceria-coated copper in fuel atmosphere $(H_2)$ was observed by SEM. It was found that the ceria coating layer could effectively hinder the grain growth of the copper particles.
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문지웅,임용호,오유근,이미재,최병현,황해진,Moon, Ji-Woong,Lim, Yong-Ho,Oh, You-Keun,Lee, Mi-Jai,Choi, Byung-Hyun,Hwang, Hae-Jin 한국세라믹학회 2005 한국세라믹학회지 Vol.42 No.12
Cathode material, $(Ba_{0.5}Sr_{0.5})_{0.99}Co_{0.8}Fe_{0.2}O_{3-\delta}$, for low temperature SOFC was prepared by the Glycine-Nitrate synthesis Process (GNP). Characteristics of the synthesized powders were studied with controlling the pH of a precursor solution. Highly acidic precursor solution increased a perovskite forming temperature. It is considered that Ba and Sr cannot complex by carboxylic acid group of glycine, because under highly acidic condition the caboxylic group mainly combined with H+ insead of alkaline earth cations. A lack of bond between cations and glycine resulted in selective precipitation of the elements during evaporation of the precursor solution. In case of using precursor solution with pH %2\~3$, a single perovskite phase was obtained at $1000^{\circ}C$. Polarization resistance of $(Ba_{0.5}Sr_{0.5})_{0.99}Co_{0.8}Fe_{0.2}O_{3-\delta}$ was measured by AC impedance spectroscopy from the two electrode symmetric cell. Area specific resistance of the $(Ba_{0.5}Sr_{0.5})_{0.99}Co_{0.8}Fe_{0.2}O_{3-\delta}$ air electrode at $500^{\circ}C\;and\;600^{\circ}C$ were $0.96{\Omega}{\cdot}cm^2\;and\;0.16{\Omega}{\cdot}cm^2$, respectively.
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나노다공성 NiO-SiO<sub>2</sub> 가교화 점토의 합성 및 가교물질의 표면개질 연구
윤주영,심광보,문지웅,오유근,Yoon, Joo-Young,Shim, Kwang-Bo,Moon, Ji-Woong,Oh, You-Keun 한국세라믹학회 2004 한국세라믹학회지 Vol.41 No.1
점토를 이용한 나노 다공성 촉매 제조를 목적으로 $Ni^{2+}$ 이온으로 피복된 $SiO_2$ 나노 졸 입자를 2차원 충상점토 화합물의 층간에 삽입, 가교화 시켜 비표면적 및 다공도가 우수한 $NiO-SiO_2$ 가교화 점토($NiO-SiO_2$-PILM)를 합성하였다. 나노 크기의 실리카 졸 입자는 tetraethyl orthosilicate(TEOS)를 가수분해하여 합성하였고, 여기에 $Ni^{2+}$ 수용액을 첨가한 다음 NaOH 용액을 적정하여 $Ni^{2+}-SiO_2$ 혼합 나노 졸입자를 완성하였다. 이렇게 제조된 혼합 졸 용액을 1wt%의 점토 수분산액에 첨가하여 $60{\circ}C$에서 5h 이온교환 반응을 통해 층간에 삽입, 수세, 건조 후 $40^{\circ}C$에서 2시간 열처리 하므로써 다공성 가교화 점토를 제조하였다. 나노 졸 입자의 가교화에 따라 점토의 층간거리($d_{001}$)는 $45{\AA}$ 정도 크게 증가하였고 $600^{\circ}C$까지도 다공구조가 안정하게 유지되었다. 또한 질소 흡착-탈착 등온선 분석 결과 비표면적($S_{BET}$)이 최대 $760m^2/g$으로 다공 구조가 매우 잘 발달되어 있음을 확인하였고, $NiO-SiO_2$ 졸 가교화 점토의 경우 $NiO-SiO_2$ 나노입자가 층간에 이중층으로 배열되어 있음을 알 수 있었다. Nanoporous materials with nanometer-sized pores, are of great interest in the various applications such as selective adsorbents, heterogeneous catalysts and catalyst supports because of their high porosity, surface area, and size selective adsorption properties. This study is aimed to prepare nanoporous catalytic materials on the basis of two-dimersional clay by pillaring of $SiO_2$ sol particles. $SiO_2$ Pillared Montmorillonite (Si-PILM) was prepared by ion exchanging the interlayer $Ni^{2+}$ ions of clay with $SiO_2$ nano-sized particles of which the surface was modified with nicked polyhydroxy cations sach as $Ni_4(OH)_4^{4+}$. Nano-sized $SiO_2$ particles were formed by the controlled hydrolysis of tetraethyl orthosilicate (TEOS). Upon pillaring of $Ni^+$-modified $SiO_2$ nano particles between the clay layers, the basal spacing was expanded largely to $45{\AA}$ and the extremely large specific surface area ($S_{BET}$) of $760m^2/g$ was obtained.
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황산 제1철을 이용한 방추형 괴타이트 나노 입자의 합성
한양수,유희준,문지웅,오유근,Han, Yang-Su,You, Hee-Joun,Moon, Ji-Woong,Oh, You-Keun 한국세라믹학회 2005 한국세라믹학회지 Vol.42 No.11
A wet-chemical route was utilized to obtain nanosized crystalline goethite ($\alpha$-FeOOH) particle, which was known as an oxidation catalyst in reducing carbon monoxide (CO) and dioxine during incineration. A cost-effective $FeSO_4{\cdot}7H_2O$ was used as starting raw material and a successive process of hydrolysis-oxidation was utilized as synthetic method. The effects of the initial $Fe^{2+}$ concentration, hydrolysis time and oxidation period on the crystalline phase and particle characteristics were systematically investigated by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and BET analyses. It was found that the spindle-shaped crystalline $\alpha$-FeOOH particle with the width of 70 nm and the length of 200 nm could be obtained successfully when the initial concentration of 1.5 M, hydrolysis time of 4h, and oxidation period of 10 h, respectively. In addition, it was observed that the spindle-shaped $\alpha$-FeOOH particle consisted of nano-sized primary crystallites of $30\~50\;nm$, which were de-agglomerated into individual particle and successively reagglomerated into spherical or irregular-shaped agglomerates beyond certain periods in the hydrolysis and oxidation process.
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딥러닝 기반의 자율주행 공용 모빌리티를 위한 하차 안전 시스템 설계
이정연(Jung Yeon Lee),김채은(Chae Eun Kim),정유경(Yoo Kyung Jeong),임덕신(Dokshin Lim),오유근(You Keun Oh) 대한기계학회 2022 대한기계학회 논문집. Transactions of the KSME. C, 산업기술과 혁신 Vol.10 No.2
버스와 같은 공용 모빌리티에서 하차 중 발생하는 안전사고 해결을 위해 안전 센서의 정밀도 향상과 같은 제한적인 접근 방법이 있지만, 운전 기사가 존재하지 않는 미래 자율주행 공용 모빌리티에서는 새로운 접근이 필요하다. 본 연구에서는 공용 모빌리티의 승객들의 불안함과 부정확한 상황 인식을 해결하기 위해 딥러닝 기반의 하차 안전 시스템을 제안하였다. 하차 안전 시스템은 1. 시간 표시등(time light), 2. 바닥 표시등(projection), 3. 비전 센서(vision sensor), 4. 끼임 방지 장치(entrapment preventer)로 구성된다. 비전 센서의 딥러닝 기술로 각 정차 지점에서 하차하는 인원을 파악하여 하차 문이 열려 있는 시간을 조절하며, 하차 상황에 대한 정보를 승객들에게 효과적으로 전달하여 승객들이 하차 상황을 인지하고 안전하게 하차할 수 있도록 돕는다. 제안된 하차 안전 시스템 프로토타입을 평가하여 효과가 있는지 검증하고 시사점까지 제안하였다. There has been improvements made on the precision of safety sensors to prevent safety accidents during get-off from the public mobility, such as buses. Future self-driving public mobility where no driver exists, requires considerations when passengers get off. This study implements a deep learning-based drop-off safety system that takes care of passengers anxiety and poor situational awareness. The proposed public mobility get-off safety system consists of 1. Time light, 2. Projection, 3. Vision sensor, and 4. Entrapment preventer. Our system effectively delivers information on the exit door so that passengers can recognize the exit situation by themselves and get off safely. The deep learning model using a vision sensor predicts the number of people getting off at each stop and adjusts the exit door opening duration. Furthermore, a prototype was set to validate whether it is practical and useful by providing useful cognitive information to the user during the drop-off process.
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