Tissue engineering - aimed at developing "lab-grown" organs and tissue by combining appropriate scaffolds and cells - could solve one of the biggest medical problems of our times, the shortage of donor organs. While the pool of scaffold materials is large (e.g. natural/synthetic biomaterials), there is consensus that the extracellular matrix (ECM) of the target tissue is an excellent choice as it possesses native structural and biomechanical properties. ECMs can be derived from cadaver tissue (e.g. from animals) through a process called decellularization, by which the tissue undergoes several cycles of flushing with detergents and enzymes. A successfully decellularised tissue is characterised by the absence of cellular material and the presence of an intact ECM. Imaging, for assessing the ECM, is an extremely important tool for the development of decellularisation methods that are simultaneously gentle and effective. This project is about developing a new imaging tool for characterising decellularised tissue based on x-ray micro computed tomography (CT). Since micro-CT is a non-destructive technique, the inspected samples can be used further in longitudinal studies or be implanted into animals to test their performance in vivo. In comparison, the current gold standard techniques for inspecting ECMs (histology, electron microscopy) require that samples are sliced, sectioned and/or stained in preparation for being imaged, prohibiting using them in any further studies. A number of substantial developments will be needed before micro-CT can become a valuable tool for validating decellularisation techniques and other methodologies in tissue engineering. Currently, micro-CT fails to meet the complex imaging needs of this field, which often requires multi-scale and multi-contrast approaches. First, a micro-CT machine with zooming in capabilities would be required to inspect the multi-level structure of ECMs. Second, decellularised tissue generally exhibits weak x-ray attenuation; hence, the micro-CT machine should provide access to phase contrast alongside attenuation contrast, which is known to provide a much better visualisation of tissue scaffolds than the latter. The micro-CT machine proposed here will have both these functionalities. It will exploit an innovative imaging mechanism that is underpinned by the idea to structure the x-ray beam into an array of narrow (micrometric) beamlets via a mask placed immediately upstream of the sample. This provides flexibility in terms of spatial resolution, as this metric - unlike in conventional micro-CT scanners - is not defined by the blur of the source and detector. Instead, resolution is driven by the beamlet width, which can be made smaller than the intrinsic system blur, bearing unique potential for fast resolution switching and multi-scale imaging. Second, it provides access to complementary contrast channels (phase, ultra-small angle x-ray scattering). These channels result from small x-ray photon deviations which occur alongside attenuation when x-rays interact with matter. While most conventional micro-CT scanners are blind to these effects, the machine proposed here will enable their detection, allowing to reconstruct three sets of complementary tomographic images for each sample. While the phase channel can provide a much higher contrast-to-noise ratio than the attenuation channel, the ultra-small angle x-ray scattering channel encodes the presence of sub-resolution features in a sample. The latter bears unique potential for image-guided zooming in. The project will culminate in the design, construction and test of an experimental prototype for image-guided multi-scale and multi-contrast imaging with a field of view of up to 10 cm by 10 cm, which may be expanded to larger dimensions in the future. A broad range of decellularised tissues will be scanned, and the results benchmarked against the current gold standard (histology or electron microscopy).

Most advanced materials are actually composite systems where each part is specifically tailored to provide a particular functionality often via doping. In electronic devices this may be p- or n-type behaviour (the preference to conduct positive of negative charges), in optical devices the ability to emit light at a given wavelength (such as in the infrared for optical fibre communications), or in magnetic materials the ability to store information based on the direction of a magnetic field for example. To enable the realisation of new devices it is essential to increase the density of functionality within a given device volume. Simple miniaturisation (i.e. to fit more devices of the same type but of smaller size) is limited in scope as the nanoscale regime is reached, not only by the well-known emergence of quantum effects, but by the simple capability to control the materials engineering on this scale. Self-assembly methods for example enable the creation of 0D (so called 'quantum dots' or 'artificial atoms'), 1D (wire-like) and 2D (sheet-like) materials with unique properties, but the subsequent control and modification of these is non-trivial and has yet to be demonstrated in many cases. This research aims to establish a Platform for Nanoscale Advanced Materials Engineering (P-NAME) facility that incorporates a new tool which will provide the capability required to deliver a fundamental change in our ability to design and engineer materials. The principle of the technique that we will adapt, is that which revolutionised the micro-electronics industry in the 20th century (ion-doping) but applied on the nanoscale for the first time. Furthermore, the P-NAME tool will be compatible with a scalable technology platform and therefore compatible with its use in high-tech device manufacture. Without this capability the production of increasingly complex materials offering enhance functionality at lower-power consumption will be difficult to achieve. The P-NAME facility will be established within a new UK National Laboratory for Advanced Materials (the Henry Royce Institute) at the University of Manchester. Access to the tool will be made available to UK academics and industry undertaking research into advanced functional materials and devices development.

The UK has recently become the first major economy in the world committed to bring all greenhouse gas emission to net zero by 2050. The emphasis of the metal industry, a vital part of the UK's foundation industries, but a challenging area to deep decarbonise, is to develop new ways to produce and recycle metallic materials in an energy-efficient, low-cost and sustainable manner. Solidification is an important route for manufacturing and recycling of metals and alloys. Use of magnetic fields to control solidification has been researched for several decades with a variety of applications ranging from metal purification to advanced liquid metal processing. Successful examples include removing ceramic particles from aluminium melts and improving the internal quality of cast steels. There is huge potential for magnetic fields to be used in new applications such as metal recycling and advanced processing. Magnetic fields have a strong interaction with molten metals and alloys. The interaction is governed by the induced Lorentz force, which modulate the flow of the liquid molten alloys. My recent article [1] demonstrated that the interaction between magnetic fields and molten alloys can be controlled , paving the way towards novel methods for optimizing how magnetic fields can be used in industrial-scale manufacturing and recycling processes. I believe this technology will produce substantial improvements over the current state-of-the-art in process efficiency and materials performance. My recent patent (WO2020/012199A1) using this concept has shown that contaminated iron element in aluminium alloys can be driven out by magnetic fields when aluminium alloys are at the molten state, and subsequently the impurity can be removed effectively, a challenge that metallurgists have struggled to overcome after 40 years of research. The overarching aim of the Fellowship is to develop innovative magnet assemblies for materials manufacturing and recycling. This work will be underpinned by fundamental studies to uncover key underlying mechanisms. Based on my previous discovery and feasibility studies, in this Fellowship, I will develop patentable techniques utilizing magnetic fields for (1) the purification of recycled Al alloys, (2) the property improvement of high temperature alloys and (3) the microstructure control of metal additive manufacturing (3D printing). The Fellowship will accelerate the process of bringing the innovation from the lab to the market, as it provides unique opportunities to work with key industry partners. I will also address the underlying mechanisms for MHD control using a multidisciplinary approach, building upon my Turing Fellowship, coupling synchrotron based 4D (3D plus time) observation, data-driven analytics, and multi-physics modelling. This will not only lay strong foundations for process optimization, but also accelerate the development of entirely new solutions for incorporating MHD in manufacturing and recycling. The success of the Fellowship will increase the competitiveness of the UK's metal industries including aluminium recycling, casting, and additive manufacturing. [1] Cai et a. Acta materialia, 2020(196): 200-209 https://doi.org/10.1016/j.actamat.2020.06.041

Advanced materials lie at the heart of a huge number of key modern technologies, from aerospace and automotive industries, to semiconductors through to surgical implants. The transmission electron microscope (TEM) is a key enabling technology for advanced material research because it offers two important pieces of atomic information: firstly the location of atoms can be determined from studies of elastic scattering of electrons by the sample, and secondly the chemical composition of atomic sites within the materials structure can be recovered from spectroscopic studies of the inelastic transfer of energy to the sample (either from direct energy loss or by the detection of characteristic X-rays). These two pieces of information underpin a huge research area exploring the relationship between materials microscopic structure and the macroscopic properties it exhibits. With the drive towards nanotechnologies and quantum devices the ability to discover the most precise understanding of individual atoms is an essential capability for facilities supporting research of advanced materials. The aim of the project is to develop, for the first time, an analytical TEM that not only offers cutting edge spectroscopy performance but which also is designed with artificial intelligence and automated workflows at its core. The first goal will be achieved through the incorporation of the latest generation of X-ray detectors and spectrometers to provide order of magnitude improvements in chemical sensitivity and precision. This capability is essential for the move to studying devices as small as a single atomic defect as well as for efficient analysis of large areas at atomic resolution. To achieve artificial intelligence (AI)-assisted experiments the project will tackle a number of technical challenges: i. Computer control of the TEM will be developed, allowing the computer to automatically adjust the sample stage and beam to address specific regions of interest and perform auto-tuning the experimental parameters to achieve detailed high resolution imaging and diffraction based analysis of nanometric regions without the need for continuous operator interaction. ii. The mechanism to identify regions of interest will utilise the full range of machine learning (ML) approaches to segment lower resolution data, which might come from fast large-area scanning in the TEM or be the result of ex-situ analysis by optical imaging, scanning probe microscopies, scanning electron microscopy or optical approaches to name but a few. iii AI training will allow the microscope control computer to build functional relationships between experimental results in the same way a human operator does, allowing faster and more systematic identification of novel features. Our proposed new smart automated TEM (AutomaTEM) offers the opportunity to gain at least an order of magnitude increase in the volume of data that is readily accessible through automated workflow analysis. Features of interest will be determined either through user-defined parameters or through the AI identification of significant features in the collective data. This will allow meaningful statistics to be gathered about the size, shape, atomic structure, composition, electronic behaviour of potentially hundreds or thousands of regions in a given sample. This in turn will enable more complete understanding of nanostructure heterogeneity and structure-property relationships in materials.

As you read this you are probably sitting down. When you sat down, were you concerned that the chair would fail? You likely did not even consider it as you may have sat in this same chair hundreds, if not thousands of times before. You used your empirical knowledge that this chair is safe for you to sit in. What if this was a new chair to you? If the chair was brand new, you would take comfort in the fact that the chair has been manufactured to a standard and been subject to some level of quality control. If the chair belongs to an organisation then you would expect that organisation to take responsibility if the chair failed and would have been replaced if reported by another user. Failure of the chair will, very rarely, be due to a poor design. The chair will be able to withstand any expected loads assuming that it has been manufactured correctly; however, the material that it is made from will be inherently variable and contain defects that are not always apparent at the point of a manufacturing inspection. The degree of that material variability may be slight and the defect sizing understood, but making sure that the design takes account of this variability through life (especially when the chair is mistreated) is often not considered. To some extent we are all materials engineers when we make a judgement that the chair appears to be 'sturdy' before we sit down, but we do this based on our empirical knowledge and not on the science that is available to us. Are you sure the next time you sit in the chair it won't fail? Your empirical knowledge only informs you of what happened last time not what 'will' happen in the future. The application of materials science knowledge will inform the future performance. Bridging the gap between the atomistic world of materials science that defines the best estimate of mechanical performance and the bounding estimates required in materials engineering that takes account of the variability and defects is key to improving trust in applying materials science to engineering structures. Assurance is about the trust that we place that the quality system has not failed. The chair may have been subject to a level of quality control before it left the factory, at this stage we need to have trust in the manufacturer. If the chair belongs to an individual or organisation, we trust that as responsible owners, that they would replace the chair if broken and that they have systems in place to check if the chair is broken before someone sits on it. This fellowship is about developing a similar level of trust for future high integrity or critical applications. We cannot use empirical knowledge, i.e. we don't have thousands of years of experience with building fusion reactors or producing high integrity power transmission systems for aerospace applications, so we must use science. Developing a similar level of trust in the predictive modelling capability in the application of materials science to these complex and high value systems, to the empirical knowledge we all have of our usual chair is key to unlocking the public trust in the safe performance of future critical systems.