PhD project: Measuring particle size in messy systems using machine learning for sustainable (re)formulation

Project description

Reliably measuring the particle-size distribution in colloidal suspensions is important in academic research, for example to predict whether crystallization will occur or to accurately determine particle concentration. In addition, it is important in industrial processes including the production of polymers, pharmaceuticals, and additives in the food, personal-care, paint and dairy industry. Characterization of model suspensions, i.e. samples in which there is only one population of well-defined particles, is well established. However, particle sizing in 'messy' systems, for example multiple populations of particles of different materials or a broad range of particle sizes, is more challenging and less well explored. This is an opportune moment to address this challenge, as sustainable (re)formulation is likely to involve working with natural (colloidal) ingredients, which often display more variability than synthetic ones.

In this project, you will use microscopy and scattering techniques to measure particle size distributions in multimodal model systems, messy model systems, and industrially relevant complex systems. You will also explore the use of machine learning in analysing the microscopy and scattering data. You will then apply this methodology to systems where particle sizing is both relevant and challenging, for example time-dependent monitoring of aggregating colloidal systems. There may be opportunities to collaborate with colleagues in the Edinburgh soft matter physics group on aspects of particle-size characterization and machine learning, and to collaborate with industry via the Edinburgh Complex Fluids Partnership (ECFP).

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