Continuous Manufacturing with Carbon Nanoreactor Supported Nanoparticle Catalysts for the Self-Optimisation of Heterogenous Reduction Reactions

这个由工业赞助的项目，由托马斯·张伯伦博士和理查德·伯恩博士(利兹大学)以及凯文·莱斯利博士和格雷姆·克莱门斯博士(阿斯利康过程化学公司)共同完成，将实现高通量、大规模流动加氢，从而实现稳健、连续的生产。为了实现这一目标，该项目将制造新型碳基颗粒催化剂材料，结合超硬核心，证明将流动反应器柱上的压力降降至最低所需的结构强度和尺寸，并采用创新的基于纳米反应器的多孔外壳，旨在最大化表面积、纳米颗粒稳定性和反应速度。这些催化剂将被集成到利兹的自我优化的反应器平台中，能够在流程中执行非均相加氢，从而使与阿斯利康合作确定的示范活性药物成分目标的流程中的非均相催化化学反应得到优化。这将包括在AZ Macclesfield的安置，包括最先进的连续流动加氢设备的培训，以及与工艺开发同事的互动。研究和培训将包括：自动化反应平台的建造、验证和测试，流动中的有机合成化学；多相催化剂的制备、表征和使用；使用一系列技术分析反应结果和开发自我优化算法。该项目将涉及流动化学和应用催化，并使用最先进的自动化技术。

This industrially sponsored project, between Dr Thomas Chamberlain and Dr Richard Bourne (University of Leeds) and Dr Kevin Leslie and Dr Graeme Clemens (Process Chemistry at AstraZeneca), will enable high throughput, flow hydrogenation at scale enabling robust, continuous manufacture. To achieve this the project will fabricate novel carbon based, pelleted catalyst materials, incorporating an ultra-hard core, proving the structural strength and dimensions required to minimize pressure drop across a flow reactor column, with an innovative, nanoreactor based porous outer shell designed to maximize surface area, nanoparticle stabilization and the rate of reactions. These catalysts will be integrated into the self-optimizing, reactor platform, at Leeds, capable of performing heterogeneous hydrogenation in flow, will enable optimisation of heterogeneously catalysed chemical reactions in flow of exemplar active pharmaceutical ingredient targets determined in partnership with AstraZeneca. This will include a placement at AZ Macclesfield, including training on state-of-the-art continuous flow hydrogenation apparatus and engagement with colleagues in process development.The research and training will include: the construction, validation, and testing of automated reactor platforms, organic synthetic chemistry in flow; heterogeneous catalysts preparation, characterisation and utilisation; analysis of reaction outcomes using a range of techniques and exploitation of self-optimising algorithms. This project will involve flow chemistry and applied catalysis, and in the use of state-of-the-art automated techniques.