There is currently a high level of interest in continuous manufacturing in the pharmaceutical industry with this process gradually replacing the traditional step-by-step manufacturing process in the majority of modern industries. Despite these advances, there are gaps and challenges that must be addressed to enable continuous manufacturing to be a viable approach in the pharmaceutical industry. To ensure the robustness of this process in regards to extended release formulations, it is greatly important to understand the swelling kinetics of tablet compacts as they give insights into the possible behavior of drug release in vivo. The development of a method that allows the swelling kinetics of various hydrophilic compacts to be studied and modelled would be of great importance to material and formulation scientists in both industry and academia. A proper understanding of this would also give greater insights that would allow quality by design formulations that may potentially drive down costs for industry thereby increasing the impact of this work. This project will use advanced imaging techniques such as dissolution imaging, X-ray micro-tomography and a Focus Variation (FV) optical surface metrology instrument from AliconaTM as a tool for validation, qualification and PAT testing in generating a mathematical predictive models.

The number of passenger vehicles on UK roads has reached 40m in 2022, resulting in increased travel times and congestion at the inconvenience of 115hrs per citizen per year. Intelligent traffic management is required to optimise the control and routing of vehicles to battle congestion and its side effects, and Connected Autonomous Vehicles (CAVs), which can extend the capabilities of traffic management systems and improve efficiency and safety for passengers, are entering the market. These new technologies are being released without adequate testing and consideration of how they could be vulnerable to misuse. The challenges associated with maintaining a strong security posture in intelligent transport management approaches are significant. Trust can be compromised when an individual is engaging in deceitful behaviour. Detection is required but has significant challenges: (i) decentralised architecture makes it challenging to have oversight and detect deceitful behaviour, (ii) different vehicle manufacturers and models result in heterogeneity making it difficult to detect unusual and deceitful behaviour. The aim of this project is to discover new knowledge to understand and mitigate cooperative multi-agent deceptive CAVs in smart traffic infrastructure through the cross-discipline understanding of the technologies, how they are perceived, and how they can be misused.

"Industrial Symbiosis (IS) is defined as the development of mutually beneficial relationships between two or more industries, by exchanging/sharing material, energy, services and/or knowledge. Over the last years, the development of such schemes has been halted due to several barriers, mostly financial and social. IS practitioners have agreed that there is need for facilitators that can help overcome those barriers. Computational algorithms, and their application through Information and Communication Technology (ICT) tools, can play this role. At the same time, the increasing digitalization of industrial ecosystems (Industry 4.0) and the widespread deployment of Internet of Things (IoT) networks, leads to the generation and capture of huge amounts of data. Artificial intelligence can provide a wide range of robust technologies that can deliver intelligence through processing the acquired data and supporting the management of complex and dynamic aspects of IS ecosystems. However, only a limited number of the developed tools have exploited AI tools to address problems related to IS development, mostly focusing on data analysis related to industrial facilities, waste production and supply chain economics. The objective of this project will be to lay the theoretical foundations and develop an AI framework for the dynamic IS optimization, through opportunity identification, efficiency assessment and control, and assisting in the decision process of the involved stakeholders as the basis towards enhancing the foundation of IS ecosystems. A previously developed tool, which has been developed for the facilitation of IS schemes (based on solid, liquid and gaseous waste streams), will be improved and extended by leveraging on recent advances in the artificial intelligence field. Existing work demonstrated the suitability of a set of machine learning techniques to identify IS opportunities, including recommender algorithms such as association rule mining, case-based reasoning, collaborative filtering, knowledge-based recommendation, and rule-based recommendation. The developed algorithm will be able to: (i) Detect favourable geographic areas and industrial sectors to establish IS schemes; (ii) Detect anomalies in the flow of materials, waste and energy that need to be treated to optimise the process; (iii) Analyse and predict significant events that may affect the demand and supply of resources providing the basis for predictive demand-supply balancing and logistics optimisation; (iv) Support the optimisation of IS matches based on user-defined preferences; (v) Automatically identify additional suitable waste products to be considered by IS schemes, and suggest an optimized value chain, by considering symbiosis with stakeholders already included in the schemes."

"The purpose of this project is to develop enzyme catalysed inter- and intramolecular couplings of phenols to access natural product-like scaffolds for natural product synthesis and drug discovery. The project will involve the generation of novel enzymes and the evaluation of their efficacy in couplings of phenols. In particular, issues of regioselectivity and enantioselectivity in the couplings will be a major focus of this work. Phenol coupling reactions are one of the main processes used by enzymes to prepare secondary metabolites (i.e. natural products) and biopolymers such as lignin and melanin. Chemical space mapping studies have shown that natural products and drugs occupy similar chemical space, therefore developing synthetic methods to access novel natural product-like compounds is of critical importance to the future of drug discovery. Synthetic chemists have made many advances in the field of oxidative phenol coupling reactions and a diverse assortment of chiral ligands and natural products have been prepared. Unfortunately, these efforts mostly rely on the use of high loadings of expensive transition metal catalysts, diamond electrodes, or stoichiometric oxidants that are not atom economical. Crucially, many phenol coupling patterns remain inaccessible, and couplings of mono-substituted phenols are particularly difficult. Directed evolution of enzymes is a Nobel prize winning technique that allows for the rapid generation of novel enzymes. In this project, we will use sequence saturation mutagenesis techniques to generate novel enzymes. These enzymes will then be evaluated in the regioselective coupling of phenols in order to develop a facile access to a range of biaryls. In particular, the generation of compounds with coupling patterns not readily accessible by chemocatalysis will be targeted."

"Additive manufacturing is experiencing exponential growth across most sectors of engineering. High impact is felt in medical engineering where personalised devices have great benefit to patients. One area that is taking the lead in this respect is dentistry. For any advantages to be fully exploited the performance of AM dental implants needs to be understood. Creation of a closed loop process in which wear performance can be analysed will allow for further optimisation of the implant manufacturing process and allow for the development of suitable laboratory testing and screening protocols. Creation of this closed loop process which feeds back to both manufacturers and clinicians is the basis of this PhD."