ANTENNA - Advanced tools for predictive cleaning in a world of resource scarcity

Lead Research Organisation: Durham University
Department Name: Chemistry


The ANTENNA programme aims to deliver new analysis and modelling tools to provide a step-change in formulation chemistry. These new tools will provide a transformation in the mechanisms, predictive models and experimental methods that will translate consumer cleaning tasks into tomorrow's formulations. Thereby providing a means of meeting the sustainability challenges of reduced water use, reduced energy use, fewer microfibres and the use of more sustainable chemistries.

The Consumer Goods Cleaning Sector is facing four key sustainability challenges:
1) Water: In many parts of the world water is becoming a scarce resource. Globally sustainable levels of water consumption (50 L/person/day, or ~1/3 of current UK usage) require a redesign of everyday cleaning tasks.
2) Energy & Emissions: 80% of a washing machine's energy consumption arises from heating the water. The use phase of cleaning products, especially those that use hot water, provides an excellent opportunity to reduce domestic indirect Greenhouse Gas Emissions in homes.
3) Garment lifespan: recent research shows that quick and cool wash cycles are key to reducing the number of microfibers released into the environment. Sensitive cleaning can extend garment lifespan and reduce clothes waste to landfill.
4) Sustainable & efficient chemistry: reducing the environmental footprint of cleaning chemistries where today conventional materials are used in excess to compensate for water hardness, slow kinetics and non-selective modes of action.

These sustainability challenges (relevant for the UK and beyond) are set to disrupt the industry and will require transformative solutions to redesign consumer cleaning tasks, based on a mechanistic understanding of the underpinning science and engineering. They cannot be developed incrementally from current products, even when augmented with High Throughput Experimentation & Machine Learning.

These challenges require new formulations for cleaning products. However, to design these formulations is a formidable task.
i) Unfavourable interactions between formulation components is a major problem in the development of new products (e.g., incompatibilities between bleach, enzymes and complex surfactant-polymer interactions).
ii) The ability to deposit surface protection agents (e.g., soil release, anti-microbial) is often required in a wash environment that is usually optimised for cleaning and complete removal of soils from substrates.
iii) The kinetics of favourable/unfavourable transformations must be understood to introduce novel sequenced chemistry solutions with minimum environmental footprint.

The overall vision of this proposal is, therefore, to provide new "game-changing" experimental and theoretical tools to improve on the current formulation development process and tackle the sustainability challenges identified above. These tools will accelerate testing of cleaning actives, allow the effects of the chemistry to be quantified and separated from flow and mechanical agitation; and will vastly improve the speed of formulation development and screening.


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