The project introduces a novel type of silicon radiation detectors constituted by an entirely digital circuit. This is a total change of design approach with respect to the current, well established architecture of solid state sensors. These have been very successful and are the main tool for many applications in science and technology. One strength of silicon sensors is their position resolution. They have been introduced and developed for tracking charged particles in high energy physics experiment and have gone through the years to a continuous series of improvements. Nonetheless their hit position resolution has not improved for many years. A value of 1 micron was already achieved over 30 years ago, and the current best devices have resolution larger than a few microns. The main reason for the inability of improving the hit location precision on a pixel sensor is the minimum size required by the analogue circuit amplifying the signal released by the ionising radiation, making the minimum pixel dimensions of the order of a few tens of microns. The approach here proposed to realise a breakthrough for the hit resolution performance of silicon sensors consists in designing a sensor based on an entirely digital circuit. The sensing mechanism is binary, with a sensor cell changing state from one to the other of two possible values when ionising radiation is crossing a given pixel (similar to the operation of a solid state digital memory). This digital circuit is comprising a limited number of transistors (from 3 to 10), allowing for a very small pixel footprint. Depending on the feature size of the selected CMOS technology node, a single pixel could be as small as 100x100 nm2, enabling an enhancement of up to two orders of magnitude in resolution when compared to current state-of-the-art. The concept of a digital radiation sensor with the above characteristics has been validated by the proponents of the project, with successful measurements of the charge generated by a pulsed blue laser and alpha particles from a 141 Am radioactive source. The initial measurements on the very first digital sensor prototypes have also indicated the subsequent research steps to improve the detection efficiency performance (number of recorded hits over the total number of crossing ionising particles) of these devices. The results have shown that a very shallow charge collection was achieved with the prototype resulting in a reduced efficiency, limited to hits happening in correspondence of the sensitive transistor gate, rather then over the whole sensor area. This project will correct this inefficiency with dedicated design of the sensitive node and produce very precise resolution pixel sensors with high efficiency over the full ionising radiation spectrum (minimum ionising particles, charged ions, photons). The new sensors would have countless applications. In science, they would revolutionize experiments in nuclear and particle physics, allowing for a large reduction of the tracking volume, with great benefits in terms of the scope and cost of future experiments. The new sensors will also be able to track particle paths shorter than 1 micron inside a single silicon layer, allowing for directional detection of recoiling nuclei or electrons. This would enable their use for detection of elusive Weakly Interactive Massive Particle (WIMP) candidates for Dark Matter. WIMPs can interact with nuclei in the silicon lattice causing these to recoil over distances a few hundred nm. Detecting these short tracks and being able to determine the direction of the incoming particle provides a powerful handle to extract the WIMP signal from otherwise insurmountable neutrino background. These are only examples of the huge scope of these novel devices.

This project for the first time places foreigners, migrants and minority groups at the centre of the long Renaissance in Italy. It explores the complex ties and interdependencies supporting cultural production in this period, unsettling long-held assumptions about what was 'Italian' about the 'Italian Renaissance'. Moving beyond the study of specific minorities (e.g. Jews, Greeks, Black Africans), it investigates the fundamental diversity and connectedness of Italian society and culture. Our research holds that every aspect of the Italian Renaissance resulted from an encounter, and that within each encounter, every actor possessed a varying level of 'belonging' and conversely of 'extraneity' (Cerutti, 2012). This conceptual model allows us, first, to recognize the contribution of foreigners, migrants, and minority groups to Renaissance cultural production; second, to identify these contributions as fundamental to the social fabric of Italy, where 'the diversity of urban populations was hard-wired into the lives of whole regions' (Rubin, 2020); and third, to integrate those contributions into wider patterns of cultural exchange and production across the peninsula. We focus on objects and spaces which reveal the influence of foreign actors, materials, designs and production techniques on the culture of the Renaissance. Objects (such as Turkish rugs, German bedclothes or garments made of 'damask' or 'scotch tweed'), contextualised using archival, textual and visual sources, evoke material exchanges, sociability, and the layering of real and imagined interactions. Spaces of encounter (workshops, inns, fairs, churches) frame the human and material contacts underpinning the Renaissance and allow us to assess levels of extraneity and belonging, as they varied by circumstance and place. Our project guards against the conventional skewing of Italian history towards Venice, Florence and Rome with fresh research on less-studied coastal and frontier regions of southern, central and northern Italy. The timeframe, 1450-1650, covers a period of exceptional mobility, resulting from the persecution of Jews and Muslims in the Spanish kingdoms, the slave trade, the Reformations, religious wars and shifting European-Ottoman relations. We expect this new history of the Renaissance to be unfamiliar, unsettling and uncomfortable. It engages with structures of inequality and discrimination inherent to Renaissance Italy while teasing out the potential for intercultural collaboration and innovation within that hostile, prejudiced world. By revealing the creative consequences of migration and multiculturalism, we challenge one of the key myths of 'western civilization'. Our work thus presents a new critical approach to the Italian Renaissance. The field, long defined as the study of the intellectual and artistic output of humanist scholars and elite artists, has faced recent accusations of irrelevance and narrow elitism. We argue, by contrast, that the astonishing cultural production of the period was neither narrow nor elite, but germinated in its very encounters and interdependence with richly diverse networks of minority groups. The project is timely. It answers a pressing need to demonstrate the diversity and connectedness of pre-modern communities and to recast European history in the light of Brexit, BLM, and the decolonization of the curriculum. It also deploys innovative historical methods to challenge old assumptions about national identity and the nature of creativity. In a famed costume book of 1590, Cesare Vecellio attributed the dazzling array of clothes in Italy to its defining mix of peoples: 'For this reason, it is no wonder that we can see a greater diversity of dress here than in any other major nation or region.' By studying the fecundity of encounters, we aspire to paint an alternative picture of the Renaissance in Italy, enriched by minorities, migrants and outsiders.

Largely driven by material scientists, the flexible electronic research thus far has focussed on the materials and fabrication techniques. Whilst these are important areas, device modelling and circuit design is critical for taking the research closer to manufacturing. The acceptable degree of bendability for reliable operation of devices and circuits is a question that has not been addressed so far. This is a challenging because the standard transistor models for circuit simulation programs such as SPICE do not take into account the dynamic bendability induced effects. FLEXELDEMO will address these challenges by systematically characterizing the ultra-thin chips, identifying various parameters that change with bending, and suggesting improved BSIM models for devices over bendable substrates. This project has several anticipated benefits over a range of time-scales. In the short-term, this project will substantially improve our understanding of changes in various device parameters as a result of bending (uni-axial, bi-axial or twisting etc.), which has traditionally been under-studied. In the medium-term, it will enable designing of electronics on bendable substrate and predicting the behaviour of bendable electronics just like we do currently for planar electronics. In the long-term, the project will lead to intelligent use of bendability in improving circuit design. For example, location or shape dependent strain-field variations will be used to design location-/shape-aware circuits or to compensate electronic artefacts (e.g. self-calibration). The approach could also lead to design on bendable electronics based on ensemble of nanowires. Formulating the design rules and integration strategies through modelling will help in stabilizing the nascent flexible electronics technology. By adequately supporting the fabrication activities with modelling and simulation, this project will add significant new perspective to the fields of flexible electronics and electronics design.

Additive manufacturing (AM), or 3D printing, is an exciting new form of industrial production that promises to revolutionise sectors as diverse as healthcare, energy, aerospace, and transport. By allowing stronger, lighter, and more complex components to be formed from a variety of materials, AM will play a critical role in meeting emerging technological needs over the coming decades. One area in which AM is already generating huge excitement is in bone tissue engineering for the production of implants for patients who have degenerative diseases or who need, for example, facial reconstruction following an accident or cancer. However, making large and load-bearing implants reproducibly is still a significant challenge. AM theoretically allows the reproduction of extremely complex geometries while also accounting for variation in the structural, mechanical, and cellular properties of bone tissue. Such flexibility will be essential to produce load-bearing 3D printed bones that have the strength to replace metal-based implants but which also mimic intricate vascular networks. Much of the flexibility of AM arises from its use of composites which combine the desirable properties of several different materials. Increasingly, in a form of AM that uses a laser to continually melt (sinter) the composite material, polymers are mixed with nano-carbon to make materials stronger and more conductive. However, an outstanding challenge in the field is to ensure that the carbon is evenly distributed throughout the matrix polymer to produce printed components with reliable properties. We also need to be able to monitor nanocarbon distribution in real time during AM which will require new, innovative methods of advanced metrology. Using the unique facilities and experience of our team, we will address these engineering challenges to provide the AM community with a step-change in their ability to produce bespoke high-quality components. To do this, we will build on significant breakthroughs we have recently made in developing new methods of hyperspectral imaging, that is, techniques that allow us to map the chemical and structural properties of a material and how these change under different conditions. Using electrons as a probe provides information on how nanocarbon particles interact with each other and their environment, for example, when heated with a laser. Such information is critical to optimise AM processes but, because this technique operates at the nanometer level, it is not practical for monitoring whole components whilst they are printed. For this, we will use another method of hyperspectral imaging based on thermal emission, similar to how we can measure temperature from the familiar glow emitted by hot coal in a fire. By combining these methods of electron imaging and thermal emission detection, we will be able to control how nanocarbon is distributed throughout a composite material and how this affects critical macroscale properties such as porosity, conductivity, strength, and surface finish. Together, this new hyperspectral imaging framework will benefit researchers and industry using AM for various applications leading to gains in cost, yield, energy efficiency, and lifetime. Once our framework is established, we will demonstrate its effectiveness by applying it to AM of bone tissue scaffolds from a novel composite we will develop containing nanocarbon mixed with a biocompatible polymer. By optimizing the laser heating process and controlling nanocarbon distribution and state, we will make scaffolds that are fit for clinical use, as validated through tests with our industry partner Lucideon. Other partners include NPL, ASTeC, YPS, Spintex, and FBK who will enhance the impact of our project through applications in Li ion batteries, pharmaceuticals, energy materials, and accelerator technologies.

The UK is world leading in Artificial Intelligence (AI) and a 2017 government report estimated that AI technologies could add £630 billion to the UK economy by 2035. However, we have seen increasing concern about the potential dangers of AI, and global recognition of the need for safe and trusted AI systems. Indeed, the latest UK Industrial Strategy recognises that there is a shortage of highly-skilled individuals in the workforce that can harness AI technologies and realise the full potential of AI. The UKRI Centre for Doctoral Training (CDT) on Safe and Trusted AI will train a new generation of scientists and engineers who are experts in model-based AI approaches and their use in developing AI systems that are safe (meaning we can provide guarantees about their behaviour) and are trusted (meaning we can have confidence in the decisions they make and their reasons for making them). Techniques in AI can be broadly divided into data-driven and model-based. While data-driven techniques (such as machine learning) use data to learn patterns or behaviours, or to make predictions, model-based approaches use explicit models to represent and reason about knowledge. Model-based AI is thus particularly well-suited to ensuring safety and trust: models provide a shared vocabulary on which to base understanding; models can be verified, and solutions based on models can be guaranteed to be correct and safe; models can be used to enhance decision-making transparency by providing human-understandable explanations; and models allow user collaboration and interaction with AI systems. In sophisticated applications, the outputs of data-driven AI may be input to further model-driven reasoning; for example, a self-driving car might use data-driven techniques to identify a busy roundabout, and then use an explicit model of how people behave on the road to reason about the actions it should take. While much current attention is focussed on recent advancements in data-driven AI, such as those from deep learning, it is crucial that we also develop the UK skills base in complementary model-based approaches to AI, which are needed for the development of safe and trusted AI systems. The scientists and engineers trained by the CDT will be experts in a range of model-based AI techniques, the synergies between them, their use in ensuring safe and trusted AI, and their integration with data-driven approaches. Importantly, because AI is increasingly pervasive in all spheres of human activity, and may increasingly be tied to regulation and legislation, the next generation of AI researchers must not only be experts on core AI technologies, but must also be able to consider the wider implications of AI on society, its impact on industry, and the relevance of safe and trusted AI to legislation and regulation. Core technical training will be complemented with skills and knowledge needed to appreciate the implications of AI (including Social Science, Law and Philosophy) and to expose them to diverse application domains (such as Telecommunications and Security). Students will be trained in responsible research and innovation methods, and will engage with the public throughout their training, to help ensure the societal relevance of their research. Entrepreneurship training will help them to maximise the impact of their work and the CDT will work with a range of industrial partners, from both the private and public sectors, to ensure relevance with industry and application domains and to expose our students to multiple perspectives, techniques, applications and challenges. This CDT is ideally equipped to deliver this vision. King's and Imperial are each renowned for their expertise in model-driven AI and provide one of the largest groupings of model-based AI researchers in the UK, with some of the world's leaders in this area. This is complemented with expertise in technical-related areas and in the applications and implications of AI.