Microfluidics for high-throughput screening and optimisation of nucleation and growth kinetics of crystals

用于高通量筛选和优化晶体成核和生长动力学的微流体

• 批准号: 2746418
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2746418
• 关键词: Microfluidics; throughput; screening; optimisation; nucleation

Crystallisation is one of the key unit operations used in the Pharmaceutical, Food, Agrochemical and Particulates industries. The physical properties of a crystal including solubility, rate of dissolution, and growth and nucleation kinetics govern its potential applications, and production at an industrial scale. These parameters also strongly depend on the solvent/ solvent mixtures used, temperature and hydrodynamics. Multiple experiments need to be performed to determine the crystal properties and relevant crystallisation data, making it a slow and laborious process. This project will develop a novel and versatile next-generation high-throughput platform for rapid and precise characterization of key physical and kinetic properties of crystals. The system is also suitable for screening polymorphs by achieving accurate temperature control to identify polymorphic forms of crystals. Several approaches involving micro and milli fluidic flow systems, static arrays of droplets and small volume arrays will be explored to evaluate optimal process conditions - solvents, droplet size and temperatures. The platform will be designed such that it can be used with multiple solvents and temperatures, that solutions can be automatically dispensed and mixed, and that continuous detection can be achieved. Different analytical techniques including XRD and Raman will be integrated into the platform to measure growth and dissolution rates. Image analysis techniques will be developed for rapid analysis of the image-based crystallisation data obtained using microscopy techniques. Handling massive data is a challenge and this will be addressed by generating automated digital image analysis algorithms that can enable rapid and accurate analysis of large datasets.

结晶是制药、食品、农药和颗粒工业中使用的关键单元操作之一。晶体的物理性质，包括溶解度、溶解速率以及生长和成核动力学决定了其潜在的应用和工业规模的生产。这些参数还强烈地取决于所使用的溶剂/溶剂混合物、温度和流体动力学。需要进行多个实验来确定晶体性质和相关的结晶数据，这使得它成为一个缓慢而费力的过程。该项目将开发一种新型多功能的下一代高通量平台，用于快速准确地表征晶体的关键物理和动力学性质。该系统还适用于通过实现精确的温度控制来筛选多晶型物以鉴定晶体的多晶型形式。将探索涉及微流体流动系统、液滴静态阵列和小体积阵列的几种方法，以评估最佳工艺条件-溶剂、液滴尺寸和温度。该平台将被设计为可以与多种溶剂和温度一起使用，可以自动分配和混合溶液，并且可以实现连续检测。包括XRD和拉曼在内的不同分析技术将被集成到该平台中，以测量生长和溶解速率。将开发图像分析技术，用于快速分析使用显微镜技术获得的基于图像的结晶数据。处理海量数据是一个挑战，这将通过生成自动化数字图像分析算法来解决，这些算法可以快速准确地分析大型数据集。