Correlating a material's atomic-scale structure to its functionality is central to our understanding of the physical and chemical world, and hence to most technological development. Scanning transmission electron microscopy (STEM) now dominates high resolution materials characterisation in the physical sciences, routinely revealing structural details that are otherwise indiscernible. It excels in the analysis of aperiodic structures including defects, inhomogeneities and interfaces that are below the resolution of other microscopies and cannot be studied using diffraction. These structures are important because they often dominate a material's properties, for better or worse. Atomic-scale resolution also underpins the development of devices, which may now contain features of only a few tens of atoms in dimension, often to harness quantum effects that can only be controlled on the nanoscale. Frustratingly, many materials remain inaccessible to atomic resolution STEM. One example is that the magnetic fields used to focus a STEM instrument interfere with magnetic samples, so that their intrinsic behaviour cannot be studied. We propose to capitalise on our expertise to address this problem. First, we will exploit improved electron lens designs to provide a three-fold improvement in 'field-free' imaging resolution. We will be able to visualise a sample's own electromagnetic fields on the atomic scale, facilitating novel studies of magnetic, quantum, microelectronic and plasmonic technologies alongside geological and chemical samples with nano-magnetic properties. An improved sensitivity to magnetic structure will enable the analysis of challenging samples such as synthetic antiferromagnets and low moment materials, which are of technological importance. We will also enhance time resolution and sensitivity by integrating the latest noise-free electron detectors for imaging and spectroscopy, providing enhanced capabilities for high-speed, high sensitivity analysis, particularly of delicate, beam-sensitive materials. We have been at the forefront of development in both of these areas and are exceptionally well-placed to grow an acknowledged UK research strength.

Cryo focused ion beam scanning electron microscope (FIB-SEM) tomography allows 3-dimensional (3D) sections of biological materials to be serially milled and imaged at the nanoscale [1]. These experiments are however limited by time, both in the removal of material and in imaging, and by damage due to the inherently destructive nature of both the ion and electron beams. Compressive sensing (CS) [2,3] and a novel targeted sampling method [4] are proposed to be valuable tools in overcoming these limitations of cryo FIB-SEM tomography. These have been successfully simulated in silico to test the validity of the method (Figures 2 and 3) [4]. Compressive sensing is a method of forming incomplete optimally acquired data followed by a form of data reconstruction to allow the data to be analysed. CS has seen positive applications to transmission electron microscopy and scanning transmission electron microscopy in recent years, in particularly through the application of probe subsampling; the manipulation of the electron beam scanning coils to follow a sampling pattern, rather than a traditional space-filling raster scan [5]. The data is then typically recovered using an inpainting algorithm, of which many exist. These methods provided an overall increase in reconstructed image quality, as determined by the calculated structural similarity index measure (SSIM), at all sampling percentages tested and were particularly effective at low sampling rates, where the most benefit of CS can be gained. Unlike deep learning approaches to de-noising and increase resolution applied to the final 3D reconstruction, Inpainting addresses the individual micrographs, massively reducing the electron dose, and data processing demands, required to form an individual micrograph and the number of micrographs required to form the final 3D reconstruction. If developed and implemented for cryo FIB-SEM volume EM (vEM), it has the potential to overcome the limitations of chemically processed tissue, with their associated artefacts, the electron dose limitations of vitrified samples and challenges associated with charging of the sample surface and acquired data being of low signal to noise. This enabling research technology, has the potential for transformative impact by allowing cells and tissues to be studied close to their native biological state, generating 3D ultrastructural information in the low nanometre range, at shorter acquisition times over extended x-y-z ranges for the study of the interrelationship between multiple cells and their individual subcellular compartments.

Metal manufacturing is responsible for 8% of global CO2 emissions and if carbon neutrality is to be achieved by 2050, we critically need to transition to more sustainable processes. In this project we address the underlying science and understanding to allow a higher utilisation of low embedded-carbon, higher impurity recycled metal as a feedstock for metal manufacturing. Current manufacturing approaches are highly dependent on energy-intensive primary metal as they rely on tightly controlled compositions with very low impurity contents to provide the required materials properties. We believe that the new understanding needed to provide transformative and efficient methods to manufacture high grade metal alloys using a much higher fraction of lower embedded-carbon recycled material as a feedstock can be delivered by leveraging the combined power of multi-modal X-ray imaging and in-line artificial intelligence. We will develop a new wholistic characterisation system comprising both newly developed hardware and AI algorithms named Artificial Intelligence X-ray Imaging (AIXI) as an intelligent tool to investigate the solidification of impurity-rich alloys in experimental conditions comparable to those found in industrial processes such as continuous casting, direct chill casting, shape casting and additive manufacturing for a wide range of aluminium and steel alloy compositions. AIXI will provide a significant advantage over existing approaches as AI will be embedded in the data acquisition system and used to interpret raw data in real-time, drastically reducing the complexity and time required for data analysis and significantly increasing the analytical power of the system. The new knowledge will allow us to finally understand the role that impurities and minor alloy additions play in the developing solidification microstructure, and to develop methodologies to mitigate their deleterious effects. It will also promote a shift to a more holistic approach for alloy design in which the solidification microstructure is engineered to both provide enhanced properties and to facilitate subsequent downstream processes with minimised environmental impact. The newly acquired knowledge will foster the development of science for `sustainable' alloys, which will: enhance metal recyclability by reducing the need for dilution of recycled scrap with energy intensive primary metal; encourage greater use of lower-grade scrap, widely available in the UK but currently exported; decrease the number of downstream processing steps (process intensification), especially heat treatment practices; simplify component recoverability by reducing the reliance on tight compositions specifications; and enhance materials properties by improving control over the final microstructure. We will uncover and apply the missing science to control phase transformations to create more benign and impurity tolerant microstructures and allow more efficient use of expensive and potentially scarce alloy additions, which will substantially cut resource use in the CO2-intensive metal industries. Furthermore, we envisage that the application of the developed hardware/AI analysis could potentially facilitate rapid scientific development in many fields of materials science and beyond where efficient, rapid collection and analysis of complex and large multi-modal datasets is critical to unlock the necessary understanding

X-Ray Imaging (XRI) has a fundamental role in medicine and security, and is instrumental in the automotive, aerospace, pharmaceutical industries and in manufacturing in general. Cultural heritage relies on XRI, as do materials science, biology, and many other scientific fields. Through our established collaboration between Nikon X-Tek Systems (NXTS, Nikon's UK based x-ray division) and UCL, we are targeting the next paradigm shift in XRI. Our vision is that this will involve the incorporation of phase effects in the image formation process ("Phase-based" XRI) coupled with energy-resolved ("colour") XRI and new data reconstruction and interpretation algorithms. "Colour" XRI could be seen as the x-ray equivalent of the transition from black and white to colour photography, meaning a much wider spectrum of information can be obtained from the imaged sample. Phase-based XRI enables contrast increases of up to two orders of magnitude, thus allowing the detection of features classically considered "x-ray invisible". Our vision is to marry UCL's world-class research and expertise on phase-based XRI, inverse problems and nanofabrication with NXTS's innovation on scatter analysis, image reconstruction and colour x-ray imaging in order to achieve the next step change in XRI technology, with the UK industrial and academic communities firmly at the centre. This will deliver transformative solutions that are practicable in an industrial context and beneficial to a wide user base, while also enabling new science. Our ambition is to replace conventional attenuation based XRI with energy-resolved, phase-based technology combined with scatter retrieval and novel algorithms in most application areas. At synchrotron facilities, UCL researchers have used phase-based XRI to image rocks, metals, tissues, animals, humans, cells, foams, fabrics, batteries, manufacturing processes, food, and heritage artefacts. They have done this statically and dynamically, in situ and in operando, in vivo and ex vivo, invariably detecting key features that were invisible to other methods. Making this available through standard, lab-size machines would be nothing short of a revolution, leading to economic and societal impact through the multi-disciplinary applications, making NXTS the commercial leader in the field, and cementing UK's leading research status. In our vision this will be strengthen even further by its combination with "colour" imaging, and with new ways of handling scattered radiation such that the "structured" scatter signal leading to additional information is exploited, while the uniform background that limits image contrast and therefore detail visibility is rejected. We will pursue this vision through a combination of modelling and experimental work. Using experimentally validated simulation software developed jointly by the UCL and NXTS teams, we will model experiments before they are carried out, compare simulated and experimental results, refine models and setups until all discrepancies are clarified, and only then proceed to the next step. This will enable us to develop systems where i) we keep all parameters under control and have full understanding of their effects and implications, and ii) we can steer the design towards effective solutions to specific problems. Cutting-edge nanofabrication methods (available at UCL's Photonic Innovations Lab and London Centre for Nanotechnology) will enable the development of beam modulators that allow the exploitation of phase effects with the conventional x-ray sources routinely used by NXTS. We will apply the novel technologies to a range of high-impact applications, including non-destructive testing of composite materials and additive manufacturing processes and products, biomaterials and tissue-engineered organs, digital histology, improved detection of concealed explosives and forensics.

Taken together the imaging Facilities on the Rutherford Campus will be without equal anywhere in the world. The suite of synchrotron X-ray, neutron, laser, electron, lab. X-ray, and NMR imaging available promises an unprecedented opportunity to obtain information about material structure and behaviour. This infrastructure provides an opportunity to undertake science changing experiments. We need to be able to bring together the insights from different instruments to follow structural evolution under realistic environments and timescales to go beyond static 3D images by radically increasing the dimensionality of information available. This project will use many beamlines at Diamond and ISIS, combining them with laser and electron imaging capability on site, but especially exploiting the 3.3M investment by Manchester into a new imaging beamline at Diamond that will complete in Spring 2012.Traditionally a 3D images are reconstructed from hundreds or thousands of 2D images (projections) taken as the object is rotated. This project will:1) Deliver 3D movies of materials behaviour. 2) Move from essentially black and white images to colour images that reveal the elements inside the material and their chemical state which will be really useful for studying fuel cells and batteries.3) Create multidimensional images by combining more than one method (e.g. lasers and x-rays) to create an image. Each method is sensitive to different aspects.4) Establish an In situ Environments Lab and a Tissue Regeneration lab at the Research Complex. The former so that we can study sample behaviour in real time on the beam line; the latter so that we can study the cell growth and regeneration on new biomaterials. A key capability if we are to develop more effective hard (e.g. artificial hip) and soft tissue (artificial cartilage) replacements.These new methods will provide more detail about a very wide range of behaviours, but we will focus our experiments on materials for Energy and Biomaterials. In the area of energy it will enable us to:Recreate the conditions operating inside a hydrogen fuel cell (1000C) to find out how they degrade in operation leading to better fuel cells for cars and other applicationsStudy the charging and discharging of Li batteries to understand better why their performance degrades over their lifetime.Study thermal barriers that protect turbine blades from the aggressive environments inside an aeroengine to develop more efficient engines.Study the sub-surface corrosion of aircraft alloys and nuclear pressure vessels under realistic conditions improving safetyStudy in 3D how oil is removed from the pores in rocks and how we might more efficiently store harmful CO2in rocks.In the area of biomaterials it will enable us to recreate the conditions under which cells attach to new biomaterials and to follow their attachment and regeneration using a combination of imaging methods (laser, electron and x-ray) leading to:Porous hard tissue replacements (bone analogues) made from bio-active glasses with a microstructure to encourage cell attachmentSoft fibrous tissue replacements for skin, cartilage, tendon. These will involve sub-micron fibres arranged in ropes and mats.Of course the benefits of the multi-dimensional imaging we will establish at Harwell will extend much further. It will provide other academics and industry from across the UK with information across time and lengthscales not currently available. This will have a dramatic effect on our capability to follow behaviour during processing and in service.