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

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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