This project will, for the first time, connect a detailed scientific understanding of the mechanisms of coatings failure with state-of-the-art machine learning to deliver a design framework for the optimization of protective coatings and nanocomposite materials. It will be game changing for an industry (paint) which is often taken for granted, despite its ubiquity - the screen you are looking at, the color of your car, the protection for the aircraft you fly in, the longevity of bridges, wind turbine masts and other infrastructure. Indeed, almost all materials are made suitable for purpose or given function by the application of coatings. In the UK there are over 10,000 employees involved in manufacturing coatings and the coatings industry directly contributes over £11bn to the economy, supporting UK manufacturing and construction sectors worth around £150bn. The annual costs of corrosion damage in the UK lies in the range of 2-3% of Gross National Product (~£60 bn, 2016) and leads to premature loss of amenity in infrastructure and equipment; hence to environmental damage through accelerated extraction and resource use. Protective organic coatings (i.e. paints) are highly cost effective in limiting early materials damage due to corrosion however these are complex products where the underlying mechanistic links between the formulation and performance are lacking. The increasing need to use environmentally sustainable materials, reduce time-to-market and increase performance requires detailed mechanistic understanding across functions and length scales from the molecular to the macroscopic. With brands such as Dulux, Hammerite and International, AkzoNobel are one of the world's largest manufacturers of protective and decorative coatings and have extensive manufacturing and research operations in the UK. AkzoNobel invests heavily in research, both in its global research hub for performance coatings in the NE of England as well as in UK universities. In particular the company (and its predecessor bodies) has collaborated in polymer science with the University of Sheffield, and in corrosion protection with The University of Manchester, for over 30 years. This prosperity partnership between EPSRC and AkzoNobel/ International Paint with the Universities of Manchester and Sheffield, will enable for the 1st time, a fundamental mechanistic understanding of how the performance of protective organic coatings arises - essentially it will tell us "how paint works". The scope of the program is well beyond the capacity of an individual company, institution or funder and, hence, the collaborative partnership is essential in order to tackle this problem head-on. Success will allow industry to side-step the current trial-and-error approaches and to incorporate digital design (i.e. Industry 4.0) into the development of paints and similar nanocomposite materials resulting in the confidence to utilize sustainable materials, comply with legislative and customer drivers and maintain and extend performance in more extreme environments. Overall the project will deliver understanding and tools that underpin the rapid-to-market development of environmentally sustainable protective organic coatings and nanocomposites by rational design.

A significant proportion of materials are produced in crystalline form. Many of these crystals are obtained by nucleation and growth from solution. This type of crystal production is often referred to as industrial crystallization. Crystallization is a key separation and purification unit in most of the pharmaceutical, food and fine chemical processes, with a significant impact on the efficiency and profitability of the overall process. Over 90% of all pharmaceutical products contain active ingredients produced in crystalline form and typical raw material cost for a single batch of active pharmaceutical ingredient is $1 to $2 million. Failure to meet product specifications incurs significant costs. For efficient downstream operation (such as filtration and drying) and product effectiveness (e.g. bioavailability, tablet stability) the control of crystal purity, size distribution and shape can be critically important. The crystal size and shape affect the dissolution rate, which is an important property of crystals for medicinal use. In the pharmaceutical industry, the relative impact of drug benefit versus adverse side effects can depend on the dissolution rate. Control of crystal size and shape enables the optimization of the dissolution rate to maximize the benefit while minimizing the side effects. Poor control of crystal size and shape can also result in unacceptably long filtration or drying times, or in extra processing steps, such as recrystallization or milling, and can influence the purity of the product which is especially important in the food and pharmaceutical industries, in which the crystals are consumed. Improved control of crystallization processes offer possibilities for better product quality and improved process efficiency, for example by reducing time to market (and extending the length of time before patent expiration), and the reduction of compromised batches, therefore providing significant increase in quality of life, for example by making new drugs available more quickly and at lower cost. However, controlling crystallization is challenging due its high nonlinearity and its high sensitivity to process conditions. The aim of the research is to develop a systematic and comprehensive framework for controlling pharmaceutical crystal formation that incorporates first-principles simulation models, efficient dynamic optimization and model based control algorithms, as well as novel mathematical analysis techniques. The approach will allow to control the shape of the crystal and the overall form of the size distribution by repeatedly solving a constrained nonlinear optimization problem in real-time that will adjust the operating conditions to achieve the desired targets, and guarantees that the process operates within feasible conditions. Uncertainties in the operating conditions will be incorporated in the controller design to reduce variability of the product quality from its desired value. Measurements provided by in situ process analytical technology will be used in real-time by the feedback control strategy to estimate and predict the product quality for different operating conditions. This technique will be useful in treating several industrially important key problems in crystallization, such as controlling the formation of desired polymorphs and/or achieving consistent product quality despite of uncertainties due to scale-up. The end result of the project will be a novel methodology for crystallization control, which will provide a comprehensive framework (including model, algorithm, software and equipment) for the robust design of desired polymorph, crystal shape as well as the form of the crystal size distribution for specific applications (e.g. drug delivery and dosage, or proteomics), opening the way toward systematic crystal engineering in the future.

Friction plays a central role in life; in transport, in manufacturing, in process engineering, in medical devices and in everyday human activities. Friction has commanded the attention of Amontons, Coulomb and Da Vinci and their simplistic, empirical laws have been the cornerstone of friction theory. At the conceptual and theoretical levels the vast modern day friction literature has revealed the enormous complexity of even the simplest processes and the limitations of the early friction laws. Friction is intimately linked to both adhesion, contact geometry and wear and all require an appreciation of the highly non-equilibrium and non-linear processes occurring over multiple length scales. The challenge presented is that friction in realistic engineering contacts cannot be predicted. Understanding the physical and chemical processes at contacting interfaces is the only route to cracking the tribological enigma. The research gap addressed in this Programme Grant is linked to the development of accurate experimental and numerical simulations of friction. We appreciate that the search for a unified model for friction prediction is futile because friction is system dependent. However, the goal to predict friction is achievable. We have identified 4 key areas where there are current challenges in understanding the origins of friction because of different complexities as outlined below: - Reactive surfaces; in many systems the frictional contact brings about chemical reactions that can only be described by non-equilibrium thermodynamics. We need accurate kinetic rate data for reactions which can only be provided by advanced in-situ chemical analysis - Extreme interfaces; these can be described as any interfaces that are inducing high strain rate material deformation and combined with electrochemical or chemical reactions. Simulation and sensing are key to improving the understanding. - Non-linear materials; in engineering and in biological systems we see the evolution of "soft" materials for tribological applications. Predicting friction in these systems relies on understanding the rheology/tribology interactions. - Particles and 2nd phase materials; for materials processing or for understanding the transport of wear particles in a contact we need to understand particle-particle friction in complex contact conditions where fracture/deformation are occurring.