• 제목/요약/키워드: Pt nanocatalyst

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Catalytic Activity of Au/$TiO_2$ and Pt/$TiO_2$ Nanocatalysts Synthesized by Arc Plasma Deposition

  • Jung, Chan-Ho;Kim, Sang-Hoon;Reddy, A.S.;Ha, H.;Park, Jeong-Y.
    • 한국진공학회:학술대회논문집
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    • 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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    • pp.245-245
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    • 2012
  • Syntheses of oxide supported metal catalysts by wet-chemical routes have been well known for their use in heterogeneous catalysis. However, uniform deposition of metal nanoparticles with controlled size and shape on the support with high reproducibility is still a challenge for catalyst preparation. Among various synthesis methods, arc plasma deposition (APD) of metal nanoparticles or thin films on oxide supports has received great interest recently, due to its high reproducibility and large-scale production, and used for their application in catalysis. In this work, Au and Pt nanoparticles with size of 1-2 nm have been deposited on titania powder by APD. The size of metal nanoparticles was controlled by number of shots of metal deposition and APD conditions. These catalytic materials were characterized by x-ray diffraction (XRD), inductively coupled plasma (ICP-AES), CO-chemisorption and transmission electron microscopy (TEM). Catalytic activity of the materials was measured by CO oxidation using oxygen, as a model reaction, in a micro-flow reactor at atmospheric pressure. We found that Au/$TiO_2$ is reactive, showing 100% conversion at $110^{\circ}C$, while Pt/$TiO_2$ shows 100% conversion at $200^{\circ}C$. High activity of metal nanoparticles suggests that APD can be used for large scale synthesis of active nanocatalysts. We will discuss the effect of the structure and metal-oxide interactions of the catalysts on catalytic activity.

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유전알고리즘을 이용한 이원계 나노입자의 원자배열 예측 (Prediction of Atomic Configuration in Binary Nanoparticles by Genetic Algorithm)

  • 오정수;류원룡;이승철;최정혜
    • 한국세라믹학회지
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    • 제48권6호
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    • pp.493-498
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    • 2011
  • Optimal atomic configurations in a nanoparticle were predicted by genetic algorithm. A truncated octahedron with a fixed composition of 1 : 1 was investigated as a model system. A Python code for genetic algorithm linked with a molecular dynamics method was developed. Various operators were implemented to accelerate the optimization of atomic configuration for a given composition and a given morphology of a nanoparticle. The combination of random mix as a crossover operator and total_inversion as a mutation operator showed the most stable structure within the shortest calculation time. Pt-Ag core-shell structure was predicted as the most stable structure for a nanoparticle of approximately 4 nm in diameter. The calculation results in this study led to successful prediction of the atomic configuration of nanoparticle, the size of which is comparable to that of practical nanoparticls for the application to the nanocatalyst.