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New Strategies for Catalytic C-H Bond Activation Reactions

New Strategies for Catalytic C-H Bond Activation Reactions
催化C-H键活化反应的新策略
批准号:
1011891
负责人:
Chae Yi
金额:
$39.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-01 至 2013-08-31

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中文摘要
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英文摘要
Professor Chae S. Yi of Marquette University is supported by the Chemical Catalysis Program in the Division of Chemistry to investigate the synthetic and mechanistic aspects of transition metal-catalyzed C-H bond activation reactions. Multinuclear ruthenium-hydride catalysts will be synthesized and used to achieve this objective. Another objective of the proposed research is to design and use chiral ruthenium-oxazoline catalysts for the asymmetric C-H bond oxidation reactions.Catalytic C-H bond oxidation reactions are extensively used for the industrial production of oxygenated compounds from ubiquitous hydrocarbon compounds. The proposed research will contribute to our knowledge in homogenous catalysis, and will advance the development of energy efficient and environmentally compatible catalysts for important industrial processes. Students at Marquette University will be trained in organometallic chemistry and homogeneous catalysis and will be exposed to an international exchange experience in chemical catalysis. Minority students from Alcorn State University and the local community colleges in Milwaukee will be involved in this research.
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Development of Catalytic Coupling Methods via Unreactive C-C and C-N Bond Activation
  • 批准号:
    2153885
  • 项目类别:
    Standard Grant
  • 资助金额:
    $52.72万
  • 财政年份:
    2022
  • 负责人:
    Chae Yi
  • 依托单位:
Development of Green Catalytic Coupling Reactions via Unreactive Bond Activation
  • 批准号:
    1664652
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $45.0万
  • 财政年份:
    2017
  • 负责人:
    Chae Yi
  • 依托单位:
Catalytic Coupling Reactions via C-H, C-C and C-N Bond Activation
  • 批准号:
    1265830
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $45.5万
  • 财政年份:
    2013
  • 负责人:
    Chae Yi
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis