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

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堆，这些技术可以扩大先进纳米粒子产品(量子点)的生产，而不会损失研究实验室实现的对结构尺寸的精确控制。它将建造实验室反应堆，可以对其进行编程，以实时监控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。它将结合和优化机器学习算法，这些算法将指导反应堆通过条件的“自我优化”达到所需的规格。优化的实验室纳米颗粒形成过程将转移到几个制造环境，包括研发过程实验室和工业现场的制造设施，并将进行放大演示。该职位将设在利兹的化学学院和工艺研究与开发研究所。它直接与EPSRC数字制造主题保持一致，并将与EPSRC拨款NanoMan、DeepMatter和CPI的合作伙伴互动，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在机器学习算法的集成方面进行合作。

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.