ANTENNA - Advanced tools for predictive cleaning in a world of resource scarcity
Lead Research Organisation:
Durham University
Department Name: Chemistry
Abstract
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.
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.
Publications
Hendrikse RL
(2023)
A many-body dissipative particle dynamics parametrisation scheme to study behaviour at air-water interfaces.
in Soft matter
Donina L
(2023)
Composition and temperature effects on the solution structure of SDS/octanol/brine by SANS, NMR and microscopy.
in Soft matter
Tyagi G
(2022)
Light Responsiveness and Assembly of Arylazopyrazole-Based Surfactants in Neat and Mixed CTAB Micelles
in JACS Au
Torquato L
(2023)
Microfluidic in-line dynamic light scattering with a commercial fibre optic system
in Lab on a Chip
Wilson M
(2022)
Molecular Simulation Approaches to the Study of Thermotropic and Lyotropic Liquid Crystals
in Crystals
Pellegrino L
(2022)
Phase behaviour of model triglyceride ternary blends: triolein, tripalmitin and tristearin
in Physical Chemistry Chemical Physics
Fischer J
(2023)
Shear-induced sponge-to-lamellar transition in bicontinuous microemulsions evidenced by microfluidic-SANS.
in Journal of colloid and interface science
Tyagi G
(2022)
Solution Structures of Anionic-Amphoteric Surfactant Mixtures near the Two-Phase Region at Fixed pH.
in Langmuir : the ACS journal of surfaces and colloids
| Title | Oil-water interfacial tension screening tool |
| Description | Model and method to automatically calculate the interfacial tensions at water-oil interfaces for surfactant molecules (and different oils) just from a knowledge of chemical structure. The method has been taken up by P&G and incorporated into a web interface for screening surfactant structures. |
| Type Of Technology | Webtool/Application |
| Year Produced | 2023 |
| Impact | The method is being used within P&G as part of the development of new surfactants. |
| Title | Surface tension screening tool |
| Description | Model and method to automatically calculate the reduction in surface tension at a water-air interface for surfactant molecules just from a knowledge of chemical structure. The method has been taken up by P&G and incorporated into a web interface for screening surfactant structures. |
| Type Of Technology | Webtool/Application |
| Year Produced | 2023 |
| Impact | This has already been used by P&G in a patent application. |