Development of a Flexible Flow System for the Synthesis of Macro and Nanocrystals

晶体的大小、纯度、结构和习惯等性能对晶体的物理化学性能和随后的特定应用中的性能有相当大的影响利用快速合成定制晶体的能力在几个化学工业中非常重要。连续冷却结晶法是一种环保的替代传统间歇结晶法的方法，能够促进快速原位过程分析技术(PATS)。持续结晶有局限性本项目将通过详细的工艺和计算优化来解决这些领域，目的是在冲突的可持续性和晶体特性性能目标之间找到非主导的解决方案。该项目将对可持续发展目标9(工业、创新和基础设施)、12(负责任的消费和生产)、13(气候行动)产生重大影响，通过降低成本、材料利用和能源使用以及加强废物最小化来改善现有结晶器的可持续性方面。这项研究为英国Net Zero Research&Innovation带来的几个挑战做出了贡献--通过在流程结晶中使用多变量、多目标贝叶斯算法，开发数字解决方案并释放资源和能源效率。这将完全消除人为错误，增加安全性和筛选吞吐量，但也将在结晶成核和生长的机理理解方面看到巨大的飞跃。香草醛(宏尺度)和铯铅卤化物钙钛矿(纳米尺度)被选为冷却结晶的广泛参考模型示例，这将通过三段流和动态调节自动输入结晶器(KRAIC)的迭代来实现。工艺优化将从评估定义三段流的试剂开始，以确定可持续性、产品产量和分段规律性之间的折衷。将评估公用事业和工艺流的物理布置，以调查对产品产量的影响，同时也寻找机会回收和移除溪流，以帮助将工艺废物降至最低。核心硬件项目将使用添加制造(AM)等快速技术进行系统优化，在提高产品产量的同时，基于材料选择和工艺效率改善可持续性方面。探头将与PATS一起现场监测过程变量，例如将在宏观尺度上实施的UV-Vis光谱，以提供快速的成分信息，并与光致发光光谱相结合，在纳米尺度上提供关于颗粒浓度、形状和尺寸的信息。补充的成分信息和详细的结构信息将被发现使用单晶(SC-X射线)和粉末(PX射线)X射线衍射技术。使用诺丁汉大学的非现场设备和国家设施的现场设备，如利兹大学的Flow-XL和钻石光源。计算优化的开始是从现场PAT中提取数据，并将其转换为适合于基于Python的机器学习算法处理的格式。多变量算法将负责接收用户定义的输入和现场过程数据，并形成知情输出，该输出将操纵过程输入以达到最初的单一目标(形态)，然后在多个相互冲突的目标之间找到一组非支配解(帕累托前沿)。这两个平台都是为了在我选择的模型之外应用于流动结晶而创建的，评估我的宏观和纳米系统是否适用于合成其他类似规模的晶体将证明该系统是灵活的。

Crystal properties such as size, purity, structure, and habit have a considerable impact on a crystals physiochemical properties and subsequent performance in specific applications harnessing the ability to rapidly synthesise bespoke crystals is extremely important in several chemical industries. Continuous cooling crystallisation emerge as an environmentally conscious alternative to traditional batch crystallisation capable of facilitating rapid in-situ process analytical techniques (PATs). Continuous crystallisation has limitations this project will look to address these areas through detailed process & computational optimisation, with the intention of finding the non-dominated solution between conflicting sustainability and crystal property performance objectives.This project will have a significant impact on the Sustainable Development Goals, 9 (Industry, Innovation and Infrastructure),12(Responsible Consumption and Production),13(Climate Action) by improving on the sustainability aspect of pre-existing crystallisers through reduced costs, material utilization and energy usage with enhanced waste minimisation. This research contributes to several UK Net Zero Research & Innovation Challenges-'Developing digital solutions and unlocking resource and energy efficiency' through the use of multi-variable, multi-objective Bayesian algorithms in flow crystallisation. This will completely negate human induced errors, increase safety and screening throughput but would also see a huge leap in mechanistic understanding of the nucleation and growth in crystallisation. Vanillin (macroscale) and Caesium Lead Halide Perovskites (nanoscale) have been selected as widely referenced model examples of cooling crystallisation, which will be delivered through tri-segmented flow and an iteration of the kinetically regulated automated input crystalliser (KRAIC). Process Optimisation will begin with evaluating the reagents that define tri-segmented flow to determine the compromise between sustainability, product yield and segmentation regularity. The physical arrangement of utility and process streams will be evaluated to investigate the impact on product yield while also looking for opportunities to recycle and remove streams to aid with process waste minimisation. Core hardware items will be optimised systematically using rapid techniques such as Additive Manufacturing (AM), improving on sustainability aspects based on material choice and process efficiency while increasing product yield. Probes will be implemented to monitor process variables In-situ along with PATs such as UV-Vis spectroscopy which will be implemented at macroscale to provide rapid compositional information, & coupled with Photoluminescence Spectroscopy provides information on particle concentration, shape, and size at nanoscale. Supplementary compositional information and detailed structural information will be uncovered using single crystal (SC-XRD) & powder (PXRD) x-ray diffraction techniques. Performed using ex-situ equipment at the University of Nottingham & in-situ equipment at national facilities such as Flow-Xl at the University of Leeds, and Diamond Light Source. Computational optimisation begins with extracting data from in-situ PATs & converting it into a format that is suitable for manipulation by python-based machine learning algorithms. The multi-variable algorithms will be responsible for receiving user defined inputs and in-situ process data, & formulating an informed output that will manipulate process inputs to reach what will initially be a single objective (morphology), before progressing to finding the group of non-dominated solutions (pareto front) between multiple conflicting objectives. Both platforms have been created with the intention of being applied to flow crystallisation outside of my chosen models assessing whether my macro & nanoscale systems are applicable to the synthesis of other crystals of similar scale will justify the system as flexible