The following may be a key to the faster growth of powders in spray dryers: - control of droplet and particle collisions and appropriate outcomes {e.g. droplet coalescence as opposed to separation or bounce-off}, - generation of appropriate droplet or particle concentration 'patterns' - increased 'clustering' in space and time - to increase probability of collisions, - maintaining these qualities over a wider range of operating conditions. Although spray drying is an old technology - 'drying' including the process of collision and agglomeration of droplets, semidried and dried particles amongst each other - remarkably little fundamental knowledge exists which would reliably answer the simple question: given this geometry of spray drying tower, what kind of spray(s) and air flow(s) should I produce in order to manufacture powder of the following quality (e.g. humidity, mean size, size distribution, morphology)? The answer must include an explanation of how and why small modifications to the location of atomisers and flow conditions radically changes powder quality. To formulate the answer, we need better and more extensive measurements of the 'fundamental' processes than hitherto: and we must generate new understanding and ideas that will advance our ability to calculate the location, number and outcome (bouncing, coalescence/agglomeration, sticking) of collisions in turbulent flows for realistic liquids (initially feedstock frequently has the consistency of toothpaste on a cold day) and geometries - and check the advance against fundamental, simple yet representative flow geometries. The overall aim of the proposal is to innovate powder manufacturing in spray dryers by improving the understanding of the probability of droplet or particle collisions in turbulent flows and of the outcome of droplet-droplet, particle-particle or droplet-particle collisions with emphasis on liquid properties and geometries used in powder production through spray drying or other similar processes. The research hypothesis is that the growth of powders in spray drying processes can occur much faster than currently believed through simultaneous multiple droplet collisions, initiated by binary droplet collisions. This is because collisions have a relatively long duration and result in deformed transient shapes (ligaments, discs, etc.) with relatively large 'target' surface area. The ligaments formed during droplet pair collisions can interact with the flow turbulence during a collision event and break up, instead of coalescing into larger droplets. Both these events are not currently taken into account in the design of industrial spray dryers nor in computational models of droplet collisions used for predictions. Our approach will be to make novel time- and spatially- resolved measurements of the liquid, spray and gas motions using optical instrumentation in flow configurations that allow the study of the microscale physics of droplet collisions and in model spray dryers that allow the study of macroscale processes. We will use these results to establish new 'collision kernels' and unique semi-empirical correlations of droplet collision outcomes in turbulent flows and breakup of non-spherical ligaments and propose novel methods for control of powder manufacturing in, and new computational models for predictions of, spray dryers.

Fibrous structures are versatile materials. They are abundant in nature, as observed in feathers, hairs, spider webs and adhesive pads of insects. They are also widely exploited in engineered systems, from the familiar examples of papers and textiles to high-precision micro- and nano-technologies. For many fibrous materials, their interaction with liquids is of paramount importance. Due to the small size of the fibres, capillary action (as observed e.g. in the drawing up of liquids in plants) often plays the dominant role. Furthermore, the action of capillarity deforms the fibres, which results in an opposing force due to elasticity. This competition between elasticity and capillarity - elastocapillarity - finds its relevance in a wide range of applications, including liquid penetration in wipes and nappies, and the clumping of hairs in the tarsi of insects and of barbules in marine bird feathers. Despite the numerous industrial applications and common occurrence in nature, our understanding of elastocapillary response of wet fibres is still very limited. Recent experiments suggest that pattern formation in nanostructures can be manipulated by elastocapillary dynamics; the capture of drops and their splashing, of relevance to the application of pesticides or herbicides among others, depend on the fibre geometry and flexibility. These are just a few of many elastocapillary phenomena ripe for investigations, especially using computer simulations, since the intricate interplay between fibre geometry, elasticity and capillarity quickly makes analytical treatments intractable. Unfortunately, simulation methods that can capture solid deformation, flow of liquids, and capillary forces are currently not available. Thus, it is my aim in this project is to deliver a numerical platform able to tackle such a challenge. This project is rooted in my recent research advances in simulation techniques for wetting phenomena. As a proof of principle, to demonstrate that the novel method can capture wetting dynamics on soft materials, I will examine the spreading of small droplets on two elastic fibres oriented at various angles with respect to each other, and I will study the removal of these droplets under the action of a body force such as gravity. These are paradigmatic examples for understanding the arrangement and cleaning properties of natural and synthetic wet fibre assemblies. My new simulations will be validated against experimental data provided by Procter and Gamble, where wetting of elastic fibres is relevant for many of their products, ranging from adsorbent materials (including nappies and wipes) to personal hygiene products (e.g. shampoos and laundry detergents). If successful, my novel approach will open an unprecedented route to model static and dynamic elastocapillary phenomena embedded in complex geometries. As such, it will advance our understanding of elastocapillarity, and help channel fundamental scientific insights into design principles for practical applications.

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.

Metals have been used to control microbes in agriculture, food handling, domestic hygiene, medicine and more broadly as an additive to preserve perishables. The industrial partner, Procter and Gamble, have on-going programmes to develop new metal-based biocides to replace existing preservatives and to increase the efficacy of current metal-based anti-microbials (ensuring compliance with shifting legislative guidelines). Ionophores can help minimise the amount of metal added to products and there is interest in using more subtle combinations of metals and/or chelators. Historically, the exploitation of metals has been empirical but the discovery of natural metal-based antimicrobial mechanisms and of bacterial systems that sense and adapt to metals, presents opportunities to improve these additives through mimicry and subversion respectively. Metals are implicated in several natural anti-microbial mechanisms. For example hosts and pathogens have evolved to compete for iron. Copper is pumped into the phagolysosomal compartment of macrophages about eight hours post infection most probably as a biocide to drive the Fenton reaction. Neutrophils are thought to starve microbes of zinc and manganese by releasing the metal-binding protein calprotectin. Bacteria (in common with all other types of cells) have evolved elaborate homeostatic mechanisms to balance the buffered intracellular concentrations of various metals within critical thresholds; critical to ensure that vast numbers of metalloproteins acquire their correct metal-cofactors. Central to such metal homeostasis is a set of metal-sensing proteins that detect when the buffered limits have been exceeded. The sensors trigger expression of proteins that restore the correct metal-balance. Having discovered bacterial metal-sensors and identified properties that determine which metals they 'can' sense in vitro, the next step is to investigate how metal-specificity in vivo (the metals the sensors 'do' sense inside cells) is a shared function of a set of metal-sensors. We will begin to model the network of interactions between the different sensor proteins. This programme will explore a fundamental question central to the cell biology of metals, coincidentally providing insight needed to formulate additives that subvert bacterial metal-sensing networks.

De-mixing is one of the most ubiquitous examples of self-assembly, occurring frequently in complex fluids and living systems. It has enabled the development of multi-phase polymer alloys and composites for use in sophisticated applications including structural aerospace components, flexible solar cells and filtration membranes. In each case, superior functionality is derived from the microstructure, the prediction of which has failed to maintain pace with synthetic and formulation advances. The interplay of non-equilibrium statistical physics, diffusion and rheology causes multiple processes with overlapping time and length scales, which has stalled the discovery of an overarching theoretical framework. Consequently, we continue to rely heavily on trial and error in the search for new materials. Our aim is to introduce a powerful new approach to modelling non-equilibrium soft matter, combining the observation based empiricism of machine learning with the fundamental based conceptualism of physics. We will develop new methods in machine learning by addressing the broader challenge of incorporating prior knowledge of physical systems into probabilistic learning rules, transforming our capacity to control and tailor microstructure through the use of predictive tools. Our goal is to create empirical learning engines, constrained by the laws of physics, that will be trained using microscopy, tomography and scattering data. In this feasibility study, we will focus on proof-of-concept, exploring the temperature / composition parameter space for a model blend, building the foundations for our ambition of using physics informed machine learning to automate and accelerate experimental materials discovery for next generation applications.