Cement production is responsible for 8 % of global CO2 emissions, which mainly come from the processing of limestone. CO2Valorize proposes a new approach to drastically reduce these emissions by partly replacing some of the limestone content with supplementary cementitious materials (SCM). Such materials are additionally carbonated using captured CO2, so this partreplacement process utilises captured CO2. Promising, calcium silicates rich SCM can come from waste materials such as mine tailings and recycled concrete, all of which are available in large quantities. The carbonation process of such materials is complex and barely understood to date. Our networks aim to lay the scientific foundations to create fundamental knowledge on the mechanisms, reaction kinetics, the physico-chemical subprocess, and the performance of the modified cement in order to provide a proof-ofconcept and show that a CO2 reduction by 50 % per tonne of cement produced is feasible. The project is driven by leading companies that represent important parts of the value chain and ensure a fast uptake of the results with the potential to commercialise new equipment, processes and software during and after the project. The structured approach combines complementary research for each individual project in the academic and industry sector. This is accompanied by a balanced mix of high-level scientific courses and transferable skills delivered by each partner locally and in dedicated training schools and workshops at network level. This way, each doctoral candidate builds up deep scientific expertise and interdisciplinary knowledge to deliver game-changing cleantech innovations during and after the project. CO2Valorize is impact-driven and strives for portfolios of high-class joint publications in leading journals and patents. The transfer of the results into first-of-its-kind engineering solutions contribute to the next generation of cement processes that can mitigate climate change.

Context and Long-term Goal Plastic waste is one of the great, global challenges facing society today. At present, limited end-of-life options mean that a significant majority of plastics are either dumped, landfilled, or incinerated, thus also contributing significantly to the wider climate crisis. The mission of UK-based SME Aquapak is to help facilitate the shift to a less polluting world, through the manufacture of novel polymer products which are both biodegradable and recyclable, yet maintain - or even exceed - the functionality of conventional plastics. The complexity of the twin-screw extrusion process used by Aquapak - which involves solid, liquid, and gaseous phases, evolving rheologies, and complex chemical kinetics - means conventional, empirical models are entirely inadequate. As such, scale-up and optimisation of these systems is a time-, cost-, and labour-intensive process, and the time to market for new products is considerable. The goal of the present project is to alleviate these issues through the co-creation of a new, digital approach to development, scale-up and optimisation. Through this approach, we will help widen and expedite the adoption of Aquapak's products and thus, in the long term, play a small but significant role in reducing plastic waste, and thus helping to fight the wider climate crisis. Project Aims and Objectives Due to the aforementioned complexity of Aquapak's primary process, a diversity of tools, skills and expertise is required to achieve our aims. To this end, we have assembled an interdisciplinary team of chemists, physicists, mechanical engineers and chemical engineers with expertise in nuclear and optical imaging, hydrodynamic and chemical kinetic modelling, spectroscopic analysis, and diverse machine-learning and artificial intelligence methods. Utilising these tools, we will: (1) Through the application of diverse experimental techniques - including x-ray and neutron diffraction, positron emission particle tracking, IR and Raman spectroscopy, hot-stage and scanning electron microscopy, and flash differential scanning calorimetry - gain a uniquely detailed, holistic understanding of Aquapak's twin-screw extrusion process, gaining direct insight into the dynamic, kinetic, and structural evolution of their products, and the dependency thereof on key process parameters. (2) Using the data from (1) as a basis, develop quantitatively accurate simulation models, incorporating both the dynamics and chemical kinetics of the process. (3) Using the models from (2), coupled to evolutionary algorithms developed by the applicants, develop a fully-automated workflow for the optimisation of Aquapak's processes so as to maximise throughput, scale up production and enhance efficiency. (4) To develop new products with enhanced functionality, applying the optimisation strategies of (3) to minimise the time from laboratory-scale testing to commercial-scale production. Applications and Benefits Through the development and adoption of a genuinely unique, digitally-driven approach to process optimisation and scale-up, enhanced by experimental methodologies and AI tools unique to the University of Birmingham, Aquapak stand to become world-leaders in the production of planet-friendly plastic products, placing the UK at the forefront of a vitally-important, burgeoning field. Thanks to the widespread desire for companies across the globe to enhance the sustainability of their products, Aquapak - and thus the wider UK economy - stand to gain significant inward investment. The future success of this partnership between two Birmingham-based institutions also stands to derive benefits on a local level, through the creation of secure, high-quality jobs in a region where 59.6% of households are considered to be deprived. Finally, by creating better end-of-life options for plastics, the project stands to elicit genuine impact on the global scale by helping to address the plastic waste crisis.

Since the early 1990s, we have been able to use imaging methods such as functional MRI (fMRI) to look into the brain to see how it works. This non-invasive technology has transformed the way that doctors and neuroscientists can answer questions about how the brain is organised and how it processes information, in the way a healthy brain functions, or how it interacts with illness and disease. However, fMRI data can be susceptible to corruption due to motion and physiological fluctuations that reduce image quality, particularly as technological progress leads to imaging at higher spatial resolutions and higher magnetic field strengths, stretching the capabilities of our MRI systems. Nearly everyone has had experience trying to capture images of moving objects in poor lighting conditions (e.g. people in a dimly lit room), often resulting in blurry and terrible looking photos. Now imagine trying to take pictures using a camera that operates quite slowly and indirectly (i.e. an MRI scanner), of a living, breathing human brain that won't sit still. Even for a head that is motionless, physiological factors like breathing and heart beats cause the brain inside to pulse, move and cause unwanted image corruption. This is particularly problematic in lower parts of the brain, like the brain-stem, which is involved in important physiological functions like processing pain and modulating blood pressure, for example. Coupled with the fact that the brain activity signals we want to extract are quite subtle, these physiological image corruptions can significantly impact the quality of the imaging data we can acquire in these clinically important brain regions. There are two primary ways of dealing with this problem using existing methods. The first approach modifies the acquisition of data through a process referred to as "gating", which synchronises imaging with a certain part of the cardiac cycle. The second approach uses image post-processing to try and "correct" the corrupted images. However, gating is inefficient and image post-processing can be imperfect, presenting a large opportunity for significant improvement in the efficiency and quality of functional brain imaging data. This proposal brings new developments in multi-dimensional ("tensor") signal processing to bear on this problem. Tensor-based methods allow us to represent and manipulate signals with higher dimensionality, allowing us to resolve more features in our data. For example, a black and white movie might have dimensions corresponding to space and time, but a colour movie has dimensions of space, time and colour, where the extra dimension allows us to capture more information about the signals of interest. For our physiological corruption problem, we use these new tools to represent our 3D brain images over not only time, but also across different points in the breathing and heart beat cycles, to effectively separate, rather than mix all of these signals contributions together. To do this, we will combine new sophisticated methods for acquiring the raw MRI data with advances in image reconstruction to develop a technique for producing imaging data free of physiological corruption, in a time efficient way. This project brings together knowledge and resources across a broad spectrum of fields, ranging from hardware control of MRI systems to nonlinear signal processing and image analysis, to provide better tools for medical and neuroscientific study of the human brain-stem.

There is an urgent need to devise processes for recycling plastics, with an estimated 460 million metric tonnes of plastics being utilised worldwide in 2019 alone, of which only 10% is recycled globally, the remainder going to incineration, landfill or export. Burning of polymers contribute to CO2 production, causing global warming, and pollution of rivers and oceans occurs through discarding to the environment. Current mechanical and thermal recycling techniques can be used to produce lower grade products such as clothing, insulation, garden and road furniture, but these have inferior colour or mechanical properties, in comparison to virgin polymer, necessitating chemical recycling to produce virgin monomer. The principal polymer selected for study in this proposal is PET, with its wide industrial and consumer applications in bottles, packaging and clothing. In the USA 30 % of PET is currently recycled, in the EU the figure is 52 %, whilst world demand for PET resin is ~23.5 million tonnes and production capacity ~30.3 million tonnes, making a potentially large feedstock for recycling. Virgin PET resin has a much higher value at £1084/tonne compared with used PET bottles priced at £222.50/tonne, making chemical recycling to produce the virgin polymer the more economically attractive route than mechanical or thermal recycling. Chemical recycling of PET can follow a number of routes including reaction with alcohols, glycols, amines and ammonia, sometimes catalysed by basic materials like sodium bicarbonate, or more recently developed ionic organocatalysts or metal salt/organic base dual catalysts. However potential scale up for industrial production is hampered by the difficulties of separating the catalyst from the product mixture and efficient recycling. Also, there is a need to isolate and purify the product BHET from a mixture which may contain contaminants from the polymer, including dyes and additives. This proposal aims to create solutions to these problems by developing supported catalysts and separation technologies to enable a scaled-up process for PET depolymerisation, which could potentially be deployed industrially. Catalyst supports will be developed based on thermally responsive polymers, which can be solubilised to contact the reacting mixture, or solidified via simple temperature cycling to aid recovery by filtration. Key considerations will include understanding the reaction kinetics of the system, including any mass transport resistances, and optimisation of reaction conditions to achieve an attractive rate of reaction. We will experiment with polymer structures to find the optimal catalyst/support combination. In addition to catalyst recovery by temperature cycling, we will study recovery of BHET product via membrane separation. Strategies will include testing of commercial membranes and development of mixed matrix membranes incorporating zeolites to enhance the permeate flow. The proposed technologies will provide more attractive and commercially viable solutions for chemical recycling. In order to realise the benefits of the research, we have engaged Project Partners from across the recycling and polymer production sectors including Dupont Teijin Films and Siemens PSE, and academic collaborator Pennsylvania State University. They will provide, or advise on, samples for depolymerisation, provide software, technical consultation on the work plan, access to facilities and advise on routes to commercialisation and impact delivery as outlined in their letters of support.

Currently, most of the manufacturing the high-value chemicals such as agrochemicals and pharmaceuticals, are performed in 'batch' reactors, where the chemical feedstocks (largely petrochemicals based) are converted into the product through a sequence of 'units of operations', which includes several chemical transformations, and purification steps. As the volume of each reactor is fixed, some of these operations, if not the entire sequence, have to be repeated, in order to meet the market demand. Very often, batch-to-batch variation in quality can result, which has to be monitored closely at each stage of the process in order to meet stringent regulatory requirements for product purity. Conversely, in a continuous flow process, the individual units of operation are integrated to enable an uninterrupted flow of material and product. Inline analytics (sensors and detectors) can also be implemented to monitor the quality of the produced product in real-time. As the entire process operates non-stop ('steady state'), the volume of production is no longer limited by the reactor size. Potentially, a continuous process is more efficient in saving costs, energy, and time, without comprising product quality. Traditionally, high-value chemical products, such as agrochemicals and pharmaceuticals, are produced using batch reactors, as they are usually required in small volumes. In more recent years, there are significant economical and sustainability drivers for the chemical industry to adopt the use of continuous flow processes. However, their implementation is not easy; as continuous reactors tend to be less flexible, in terms of modifying them to produce different products. The ambition of the IConIC Partnership is to redesign the continuous process: from a fully-integrated, single-purpose unit, towards a flexible 'plug-and-play' system, where each unit of operation ('module') can be replaced or substituted easily without affecting the overall performance of the continuous process. This will require a better understanding of how the interplay between molecular properties, timescales of reactions (reaction kinetics), and process parameters. For industrial implementation, additional factors (e.g. costs, sustainability and regulatory requirements) also need to be taken into consideration to justify the capital investment needed to switch from batch to flow production. Over the past 5 years, BASF has been working with ICL to foster an active 'Flow Chemistry' community involving 50 researchers at both institutions. The IConIC partnership will not cement the relationship by initiating a programme of exciting and ambition research projects to translate the benefits of Flow Chemistry from the R&D lab into industrial practice. An important aspect is an emphasis on a seamless data flow and translation process across the WPs, including decision-making under uncertainty, multi-fidelity design of experiments, transfer learning, and proof-of-concept demonstration for scale-up. A key feature of IConIC is the inclusion of a number of other UK-based industrial partners to form a 'vertical consortium' along the value chain. Over the period of the grant, the Partnership will be expanded to include additional academic and industrial partners at the appropriate junctures, to leverage synergistic values. Ultimately this will enable the UK to take leadership in continuous flow manufacturing.