Developing Asymmetric Catalysts Using Modular Ligands

使用模块化配体开发不对称催化剂

• 批准号: 1110599
• 负责人: Matthew Sigman
• 金额: $ 39万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1110599&HistoricalAwards=false
• 关键词: Developing; Asymmetric; Catalysts; Using; Modular

In this project funded by the Chemical Synthesis Program of the Chemistry Division, Professor Matthew Sigman of the Department of Chemistry at the University of Utah will explore the development of techniques to more efficiently optimize diverse enantioselective reactions. Classically, catalyst optimization has remained mainly empirical, wherein evaluation of a significant number of ligands, often structurally unrelated, is required to develop a mature chiral catalyst. To overcome this impediment, Professor Sigman will use modular ligands, which permit the introduction of systematic changes to the ligand structure, in combination with physical organic mechanistic tools (i.e., Hammett electronic parameters and Charton/Taft steric parameters) and design of experimental statistical approaches to facilitate catalyst design and optimization. This work could lead to both new enantioselective catalysts for challenging, synthetically useful transformations and the development of new approaches for mechanistic analysis and catalyst optimization. Success in each of these areas will have an impact on any area of activity in which the synthesis of molecules and catalyst optimization is needed, such as the pharmaceutical, chemical, agricultural industries and their biological and chemical research activities. In addition, this project will provide training of students, from pre-undergraduate to post-doctoral, including those from groups historically underrepresented in the sciences.

在这个由化学系化学合成项目资助的项目中，犹他大学化学系的Matthew Sigman教授将探索更有效地优化各种对映选择反应的技术发展。传统上，催化剂优化仍然主要是经验性的，其中需要评估大量的配体，通常结构不相关，以开发成熟的手性催化剂。为了克服这一障碍，Sigman教授将使用模块化配体，它允许引入配体结构的系统变化，结合物理有机机械工具（即Hammett电子参数和Charton/Taft立体参数）和实验统计方法的设计，以促进催化剂的设计和优化。这项工作可能会导致新的对映选择性催化剂的挑战，合成有用的转化和机制分析和催化剂优化的新方法的发展。这些领域的成功将对需要分子合成和催化剂优化的任何活动领域产生影响，例如制药，化学，农业工业及其生物和化学研究活动。此外，该项目将为学生提供培训，从本科预科到博士后，包括那些来自历史上在科学领域代表性不足的群体。