Closed-loop Optimisation of Home and Personal Care Formulations

Studentship to work on 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 our everyday life; whether it would be a shower gel helping to maintain the health and softness of our skin, a shampoo to keep our hair clean and looking attractive or a toothpaste to protect our teeth. They make it possible for these products to function effectively and without harm. And yet, considering the volume of surfactant consumption, they must be sustainable and safe for the environment.

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.

The student will be involved in the creation, manipulation and characterisation of microstructure using sustainable ingredients in aqueous solution driven by real-time analysis of microstructure and properties measured by milli-fluidics. They 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.

Nearly all shampoo formulations are built on the same basic surfactant chassis, with active ingredients, colours and fragrance differentiating markets by both price and geographic location. Being able to optimise the shampoo chassis for particular applications and ingredient availability will de-risk the move to more sustainable products, with a greater emphasis on the local availability of more sustainable ingredients.

This project will develop a combinatorial approach for structural characterisation of surfactant formulations using optical microscopy, rheology, X-ray scattering techniques using milli-fluidic devices. The PhD candidate will design and construct a milli-fluidic system to survey the large formulation space for a shampoo formulation and work as part of a team to implement a machine learning approach to deliver to closed loop optimisation of more sustainable & environmentally friendly personal care products.

They will be a member of a polymer research group and a regular user of Sheffield's state-of-the-art Soft Matter Analytical Laboratory specializing in scattering and rheology techniques (https://www.sheffield.ac.uk/small).