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In-situ Characterisation of the Crystal Growth of Organic Materials through Microscopic Imaging, Image Processing and Machine Learning


   Faculty of Engineering and Physical Sciences

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  Prof K Roberts, Prof D Hogg, Dr Cai Yun Ma, Dr R Hammond  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

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 molecular-scale complexity of organic materials can directly impact on the physical chemical properties of crystals, notably their crystallisation in low symmetry crystallographic structures which have anisotropic external crystal morphologies and surface properties. Changes to, or variability in, these properties can affect their performance, e.g. their purity, bioavailability, powder flow, stability and manufacturability. Current crystal size measurements can be over-simplistic in terms of shape characterisation being focussed mostly on spherical particles. Such methods do not reflect the facetted (polyhedral) crystal morphologies common found within organic solids where different crystal habit faces can exhibit different surface chemistry which can expose different intermolecular binding interactions within the chemical processing environment. Currently, there is a critical capability gap in terms of being able to relate the molecular structure of a material to its performance in its crystalline particulate form. This knowledge gap has led to increasing interest in fusing in-situ experimental crystallisation studies with a knowledge of its core molecular structure and its simulated surface properties based upon crystallographic data linked to artificial intelligence (AI) and machine learning approaches. 

This project addresses the above need by applying digital AI-enabled technology to develop morphologically-based shape descriptors with targeted utility for the precise 3D characterisation of crystalline particulates in-situ. Overall, the project aims to enable the capability to design organic crystalline ingredients and the products resulting from their formulation to a much tighter specification notably higher quality, more consistency and less variability. The proposed project is directly associated with an EPSRC research project (https://eps.leeds.ac.uk/research-project/1/faculty-of-engineering-and-physical-sciences/4417/advanced-crystal-shape-descriptors-for-precision-particulate-design-characterisation-and-processing-shape4ppd).


Funding Notes

A funded EPSRC Doctoral Training Partnership Studentship consisting of the award of tuition fees together with an enhanced maintenance grant (currently £19,062 for session 2022/23) for 3.5 years. This opportunity is open to UK applicants only.  All candidates will be placed into the EPSRC Doctoral Training Partnership Competition and selection will be based on academic merit.

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