课题基金 / 基金详情

New Strategies for Regulating Non-Living Olefin Polymerization Catalysis

New Strategies for Regulating Non-Living Olefin Polymerization Catalysis
调控非活性烯烃聚合催化的新策略
批准号:
2154532
负责人:
Loi Do
金额:
$48.78万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-12-01 至 2025-11-30

项目摘要

项目成果

Loi Do的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
With the support of the Chemical Catalysis program in the Division of Chemistry, Loi Do of the University of Houston is studying how to improve the synthesis of polyolefins, polymers that account for more than 60% of plastics used worldwide. Polyolefins are some of the most attractive synthetic materials known because of their light weight, resistance to damage by water, oil, and solvent, and ability to be easily shaped into consumer items. A large percentage of polyolefins are made using a catalytic process known as living polymerization. However, such processes are inefficient because only one polymer chain is produced for each catalyst reactive site. In contrast, polymerizations using non-living catalysts yield many polymer chains per site. The main limitation with the non-living catalysts is that it is difficult to control the polymer products they generate. The Do team's research will focus on using earth abundant metal additives to modify non-living polymerizations. This work seeks to develop more sustainable ways to synthesize polyolefins and provide access to previously unknown polymer structures with technologically useful properties. The objective of the Do team's outreach activities is to enhance the research experiences of undergraduate students in the Gulf Coast region of the United States by creating a Texas Chemistry Consortium focused on sharing scientific resources and expertise, exchanging students for training, hosting site visits, and creating professional networking opportunities for students.Under this research project, the Lo group at the University of Houston strives to improve catalytic efficiency and precision in polyolefin synthesis. Polyolefins can be synthesized from olefin monomers using either living or non-living catalysts. Living catalysts provide excellent control over the chain growth process but yield only a single polymer chain per metal. In contrast, non-living catalysts lack control over polymer chain propagation but afford many polymers per metal. Because polyolefins derived from living catalysts contain high metal content, they must also be subjected to further purification steps. To combine the benefits of both living and non-living reactions, Dr. Do and his team are developing a cation-based strategy to regulate polymerizations by coordination insertion catalysis. Under dynamic switching conditions, polymer chain growth occurs from a single catalyst that continuously interconverts between the monometallic and bimetallic forms. The key to success is that cation exchange must proceed slower than chain growth but faster than chain termination. The Do group is also designing new catalysts to achieve stereocontrolled polymerization of functional olefins. A library of chiral catalysts will be assembled by pairing chiral metal auxiliaries with a common catalyst platform, which will provide access to molecular structures that are inaccessible using conventional organic scaffolds. Because polyolefins represent greater than 60% of the commercial plastic produced around the world, improvements in the way they are manufactured on an industrial scale could dramatically reduce cost, energy, and environmental impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
CAREER: Site-Differentiated Bimetallic Catalysts for Precise Polyolefin Synthesis
  • 批准号:
    1750411
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $62.63万
  • 财政年份:
    2018
  • 负责人:
    Loi Do
  • 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