Characterisation of Crystalline Materials through Imaging, Image Processing and Machine Learning for 3D Shape Description

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The crystal growth of organic materials is of significant importance within the speciality and fine chemical industries. This reflects its utility in materials purification and its use in preparing a wide range of compounds which have the well-defined crystal size, shape and polymorphic form needed for optimal product performance. The latter is important e.g., in ensuring the reproducible dissolution and stability behaviour needed to maintain the safety and efficacy of ingredients within formulated products. The inherent complexity of organic fine chemicals directly impacts on their physical chemical particulate properties, notably their crystallisation in low symmetry crystallographic structures with anisotropic morphologies and surface properties. Changes to, or variability in, these properties can affect downstream performance of the material, e.g., bioavailability, powder handling, stability and manufacturability. Impurities, that form solid solutions are known to influence morphology (shape as well as surface properties). Current particle sizing measurements can be over-simplistic in terms of shape characterisation being focussed mostly on spherical particles. Such methods do not reflect crystal morphologies important in fine chemicals where different crystal faces can have different surface chemistry and hence different intermolecular interactions with their processing environments. Currently, there is a critical gap in capability to be able to relate molecular structure to performance of the same material in particulate form. This knowledge gap has led to increasing interest in fusing molecular crystallographic data, simulated properties and artificial intelligence (AI) approaches. The aim of this project is to address the above need by applying digital AI-enabled technology to develop morphologically-based shape descriptors for precisely characterising crystalline particulates in 3D. To do this machine learning will be applied to map the images from in-process microscopy to a description of 3D crystal shape and functional properties. The project will help enable the design of organic crystalline materials to a much tighter particle size/shape specification with more consistency and less variability. The project will explore the impact of process impurities and aim to model the impact of solid solutions on crystal growth and surface properties. The student will integrate the crystallisation technology with AI/machine learning for crystallisation process engineering with a focus on 3D crystal characterisation. The project aims encompass 1. Intensive literature review 2. Development and commissioning of a new in-situ cell for the dynamic characterisation of crystal morphology and growth 3. Experimental studies of the morphological characterisation and crystal growth kinetics collecting crystal images prepared under varying process conditions 4. Using molecular and crystallographic modelling to characterise in-situ microscopy data integrating this with AI/machine learning techniques 5. Extending approaches to online imaging for monitoring the dynamics of the growth of a population of crystals during a batch processing. The project is centred around the areas of particle technology and AI/machine learning with experts within the supervisory team in both areas. This project will be experimentally intensive with making use of the crystallisation laboratories at Leeds which are well equipped with e.g., in-situ microscopes, crystallisation systems (1mL to 20L), solid-state characterisation (Keyence Digital Microscope, IGC/GC, DSC, TGA, FTIR, UV-vis, Morphologi G3 etc). Support by the project's industrial partner, Syngenta, will provide placement opportunities for industrial case studies.