Quantum Dot synthesis in Continuous Flow via Self-Optimising Nanoscale Manufacturing Platforms

通过自优化纳米级制造平台在连续流中合成量子点

• 批准号: 2783323
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2783323
• 关键词: Quantum; Dot; synthesis; Continuous; Flow

This project will build platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products (quantum dots) without loss in the precise control over structural dimensions which are achieved in research laboratories. It will build laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate formation conditions to the final product particle's properties. It will incorporate and optimise machine learning algorithms which will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions. The optimised laboratory nanoparticle formation processes will be transferred to several manufacturing environments, including R&D process laboratories and manufacturing facilities at industrial sites and scale up will be demonstrated. The position will be based at the School of Chemistry and the Institute of Process Research and Development at Leeds. It aligns directly with the EPSRC Digital Manufacturing theme and there will be interaction with partners of the EPSRC grant NanoMan, DeepMatter and CPI, to develop automated reactor platforms for quantum dot generation and work with collaborators at the University of Sheffield in the integration of machine learning algorithms.

该项目将建立平台技术，包括以计算智能为基础的先进化学反应器，可以扩大先进纳米粒子产品（量子点）的生产规模，而不会失去在研究实验室中实现的对结构尺寸的精确控制。它将建造可编程的实验室反应器，以实时监测纳米颗粒的形成过程，并将形成条件与最终产品颗粒的性质联系起来。它将整合并优化机器学习算法，通过条件的“自我优化”来指导反应堆达到预期的规格。优化的实验室纳米颗粒形成过程将被转移到几个制造环境中，包括研发过程实验室和工业基地的制造设施，并将展示规模。该职位将在利兹大学化学学院和过程研究与开发研究所工作。它与EPSRC数字制造主题直接一致，将与EPSRC资助的NanoMan、DeepMatter和CPI的合作伙伴进行互动，开发用于量子点生成的自动化反应堆平台，并与谢菲尔德大学的合作者合作，整合机器学习算法。