Developing Asymmetric Catalysts Using Modular Ligands

在这个由化学部化学合成计划资助的项目中，犹他州大学化学系的Matthew Sigman教授将探索技术的发展，以更有效地优化各种对映选择性反应。 传统上，催化剂优化主要是经验性的，其中需要评估大量的配体，通常是结构上不相关的，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，其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/塔夫脱空间参数）和实验统计方法的设计以促进催化剂设计和优化。 这项工作可能会导致新的对映选择性催化剂的挑战，综合有用的转换和新的方法的发展机制分析和催化剂优化。 这些领域的成功将对任何需要分子合成和催化剂优化的活动领域产生影响，例如制药，化学，农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，从大学预科到博士后，包括那些来自科学领域历史上代表性不足的群体的学生。

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.