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

这个由托马斯·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（阿斯利康工艺化学）共同发起的工业赞助项目将实现高通量、大规模流动氢化，从而实现稳健、连续的生产。为了实现这一目标，该项目将制造新型的碳基颗粒状催化剂材料，结合超硬核心，证明结构强度和尺寸所需的最小化整个流动反应器柱的压降，与创新的，基于纳米反应器的多孔外壳设计，以最大限度地提高表面积，纳米颗粒稳定和反应速率。这些催化剂将集成到位于利兹的自优化反应器平台中，能够在流动中进行非均相加氢，将能够优化与阿斯利康合作确定的示例活性药物成分目标流动中的非均相催化化学反应。这将包括在AZ Macclesfield的一个职位，包括最先进的连续流动加氢装置的培训和与同事在工艺开发方面的合作。研究和培训将包括：自动化反应器平台的构建，验证和测试，流动中的有机合成化学;非均相催化剂的制备，表征和利用;使用一系列技术和自我优化算法分析反应结果。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.