Closed-loop Optimisation of Home and Personal Care Formulations

This is a research project at University of Sheffield in collaboration with our industrial partner - Unilever (https://www.unilever.co.uk/), a world-leading company that manufactures a range of home, personal care and food products (such as Dove, Lynx, Surf, Cif, Hellmann's, etc) that are used by 3.4 billion consumers daily and employs over 138,000 people. Unilever is one of the foremost global industrial research organisations with an equally strong reputation in chemistry and the wider sciences. In its publicly stated vision of 'Making Sustainable Living Commonplace, Unilever has prioritised the development of next generation products that are sustainable and environmentally friendly.

The project is supervised by two academics - Dr. Oleksandr Mykhaylyk and Prof. Anthony Ryan, who are experts in structural characterisation of polymers and colloids, and physical chemistry of polymers and surfactants, respectively.
Surfactants play a vital role in nearly all personal care products that we use in everyday life; whether it would be a shower gel, a shampoo, or a toothpaste. They make it possible for these products to function effectively and without harm. The path to net-zero has been addressed for the packaging of personal care products (by recycling or reuse of the plastics) but the pressure is on to deal with the stuff inside the bottle, from both a legislative and consumer driven perspective.

Personal care products have complex microstructures built from surfactants and polymers that affect product quality, in-use sensory perception and delivery of key active compounds to skin, hair and teeth. They are an essential part of our daily routine, however, they put expensive, carbon-intensive chemicals down the drain. This project focuses on development of new chemical formulations for sustainable personal care products of the future. The current empirical and theoretical knowledge, accumulated on surfactants and colloids, will be applied for finding new compositions via machine learning (ML) algorithms where experimental results will be used as feedback to guide predictive computational tools
to optimise the search for sustainable surfactant formulations. You will be involved in the creation, manipulation and characterisation of microstructure using sustainable ingredients in aqueous solution driven by real-time analysis of properties measured using milli-fluidics. You will design and build the equipment to mix ingredients and measure their properties. The data generated will be interrogated through machine learning techniques and closed-loop feedback
(in collaboration with ML-focussed PhD students) to optimise multi-component systems against a range of metrics.

During this project the PhD candidate will develop a combinatorial approach for characterisation of surfactant formulations using optical microscopy, rheology, X-ray scattering techniques using milli-fluidic devices. They will work as part of a team to implement a machine learning approach to deliver to closed loop optimisation of sustainable & environmentally friendly products. They will be a member of a polymer research group and a user of Sheffield's state-of-the-art Soft Matter Analytical Laboratory specializing in scattering and rheology techniques (https://www.sheffield.ac.uk/small).