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Transformative Strategies for the Production of Hybrid Luminescent and Semiconducting Molecular, Polymeric, and Self-Assembled Materials

Transformative Strategies for the Production of Hybrid Luminescent and Semiconducting Molecular, Polymeric, and Self-Assembled Materials
混合发光和半导体分子、聚合物和自组装材料生产的变革策略
批准号:
RGPIN-2018-04240
负责人:
Gilroy, Joe
金额:
$3.5万
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

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中文摘要
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英文摘要
As the technological demands of our country continue to evolve, so does the need for new and readily-accessible functional materials that will help to address these challenges. The proposed research program will produce luminescent and semiconducting molecular, polymeric, and self-assembled materials for use in a variety of organic electronics (e.g., sensors, memory devices, light-emitting diodes, field-effect transistors, and solar cells). We will employ our expertise in the creation of novel hybrid architectures that take advantage of the best traits of purely organic materials in combination with the required functionality of inorganic elements through the use of nitrogen-based ligands derived from formazans and hydrazones.******Our program embraces the ‘discovery' theme of this flagship NSERC program, as it offers a broad scope of potentially transformative ideas and a calculated balance of high risk, high reward ventures that will place us at the forefront of our field. We believe that the elements of risk that feature throughout the proposal are justified based on our demonstrated ability to address similar ambitious research challenges, our careful design of the molecular and polymeric targets, and the potential impact that the research outcomes will have on the functional synthetic materials and organic electronics fields. Furthermore, our unique expertise and local infrastructure are ideally suited to the proposed research. Without fundamental advances such as those proposed, innovation and development of future generations of functional materials and organic electronics will simply not be possible.******The proposed program is both multidisciplinary and collaborative. It will provide an excellent training ground for highly qualified personnel (HQP) at the BSc, MSc, and PhD levels. They will work toward the development of guiding principles for the synthesis of hybrid materials, the establishment of detailed structure-property relationships for the luminescent and semiconducting hybrid materials that they produce, and self-assembly protocols that will transform the way materials scientists approach the design of semiconducting polymers. These initiatives will provide a platform for new and continued collaborations with Canadian industry that will directly involve HQP, further increasing the impact of the training experiences created. HQP will graduate with skill sets ideally suited to the Advanced Materials and Manufacturing Sector of the Canadian economy and academia. ********
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Transformative Strategies for the Production of Hybrid Luminescent and Semiconducting Molecular, Polymeric, and Self-Assembled Materials
  • 批准号:
    RGPIN-2018-04240
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.5万
  • 财政年份:
    2022
  • 负责人:
    Gilroy, Joe
  • 依托单位:
The development of phosphinated styrene-butadiene rubber
  • 批准号:
    566998-2021
  • 项目类别:
    Alliance Grants
  • 资助金额:
    $3.82万
  • 财政年份:
    2021
  • 负责人:
    Gilroy, Joe
  • 依托单位:
Transformative Strategies for the Production of Hybrid Luminescent and Semiconducting Molecular, Polymeric, and Self-Assembled Materials
  • 批准号:
    RGPIN-2018-04240
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.5万
  • 财政年份:
    2021
  • 负责人:
    Gilroy, Joe
  • 依托单位:
Transformative Strategies for the Production of Hybrid Luminescent and Semiconducting Molecular, Polymeric, and Self-Assembled Materials
  • 批准号:
    RGPIN-2018-04240
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.5万
  • 财政年份:
    2020
  • 负责人:
    Gilroy, Joe
  • 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