As we approach the theoretical limit of 3 nm transistor channel lengths, manufacturing challenges of CMOS architectures become exponentially more difficult and more expensive to overcome. Simultaneously, a seismic shift is occurring in the computational workload, away from offline processing to real-time big-data applications driven by the Internet of Things (IoT), robotics and autonomous agents. This combination of factors has led to an intensified exploration of alternative computing methodologies that span the entire Boolean computational stack from physical effects, to materials, devices, architectures, and data representations. It also includes novel, non-Boolean methods of computing such as quantum, wave and neuromorphic computation, Boltzmann machines and others. Exactly which combination of computational elements will evolve from this plethora of options is far from clear. However, it is possible to state general requirements future computing platforms must meet. First, any new computing methodology must be compatible with the existing multi-trillion-pound infrastructure associated with current CMOS based computing. Second, it must be scalable through multiple generations of incremental hardware and software improvements. Third, the performance/cost metric must greatly exceed that of Boolean CMOS processors, and, fourth, the new technology must provide a much more energy-efficient alternative to existing technology. Reservoir Computing (RC) leverages fast nonlinear dynamics in analogue physical systems to map a system's spontaneous transient response to solutions of traditionally hard problems such as classification tasks and signal prediction. This technique effectively ties memory and processing tasks to the intrinsic materials properties. The specific details of the physical system in which RC is implemented, however, are not relevant so long the following key criteria are met: dynamical non-linearity, high phase space dimensionality, uniquely reproducible initial state, easy out-of-equilibrium perturbation, and readability of dynamical state. The main quest is to identify a system suitable for the task, which is not plagued by real world-incompatible requirements. Our proposed solution is based on driven spin-wave excitations which guarantee both sufficiently complex transient responses, controlled chaoticity, as well as providing a natural spintronic platform for straightforward driving and reading of dynamical magnetic states. Our proposed work aims at demonstrating the versatility of spin-wave interference as the key candidate for the implementation of RC in a real-world device. We believe that spin-waves in magnetic nanostructures are ideal candidates for developing drop-in substitutes for circuit components, as well as stand-alone devices. Success in this endeavour would prove groundbreaking for the development of real-time pattern detection technologies with the potential for high-impact deployment in areas ranging from medical monitoring to climate modelling. Complex pattern recognition tasks could be performed on RC hardware with square-micrometre surface area, 100 micro-W power consumption and 10 ns inference time. Compared to the server stacks currently used by industry leaders (Google, Apple, Facebook, etc.) to satisfy global demand, success in this action will pave the way for massively more resource efficient big-data solutions.

Aerosol particles are key drivers of reduced air quality and provide significant offsetting of warming by greenhouse gases. The organic fraction is frequently observed to dominate mass of fine particulate matter (PM) and secondary organic aerosol (SOA) is the major contributor. With air pollution responsible for 11 % of global deaths annually and air temperature rise within 0.5 degree C of the target pursued by Paris Agreement signatories, accurate forecasts of organic aerosol particle mass loadings are required to inform policy decisions. We will interrogate experimental results presented in our recent landmark study to investigate the mechanisms determining SOA formation in atmospheric mixtures. We will include new mechanistic chemical understanding developed from this work into a coupled model of gaseous photochemistry and aerosol formation. Detailed comparison of the model with measured gaseous and aerosol composition will enable unprecedented confidence in our understanding of the interactions that can occur in the real atmosphere. We will use the model to demonstrate the magnitude of interactions to be expected in airmasses containing natural and manmade pollutants that promises to enable reasonable mechanistic interpretation of SOA formation in the real atmosphere for the first time. What... We will develop a mechanistic quantitative representation of oxidative chemistry leading to SOA formation in realistic atmospheric mixtures including interactions between biogenic and anthropogenic precursors. We will demonstrate its predictive capability by comparison with existing and emerging experimental data and use it to evaluate the potential for SOA formation and uncertainty ranges across VOC mixtures at VOC:NOx regimes applicable to the real atmosphere. Why... Our recent study (McFiggans et al., 2019) was transformative in that it showed that the formation of particulate mass in mixtures of gaseous precursors cannot be assumed to be the sum of that formed independently from the components of the mixture. We demonstrated that this resulted from two effects: i) oxidant scavenging; competition of the precursor molecules for the available oxidant and ii) product scavenging; vapour phase interactions between oxidation products that would have otherwise reacted to form condensed particulate mass. These two effects lead to the requirement for a realistic treatment of SOA formation in mixtures in order to predict atmospheric PM loading and its effect on human health and climate. How... We have data from a large number of published and (as yet) unpublished laboratory and chamber experiments, investigating SOA formation from the oxidation of individual VOC and their mixtures. In each, we quantify the formation of highly-oxygenated organic molecules (HOM) found to be major contributors to the condensed SOA mass. The VOC include key species from the major biogenic and anthropogenic classes of SOA precursors. We will extend the benchmark mechanism for atmospheric VOC oxidation to incorporate the most recent mechanistic understanding of HOM into our chamber model of coupled photochemistry and aerosol microphysics. We will optimise simulations using this model by comparison with the experimental data and conduct further simulations to establish the critical dependencies of SOA formation in the atmosphere. Large scale air quality and climate models will need to capture these relationships to enable confidence to be placed in their predictions. We will include a simplified mechanism based on the same framework as our more detailed scheme into the EMEP regional pollution model to demonstrate the impact of interactions in atmospheric mixtures of VOC on regional PM.

Solid-state nuclear magnetic resonance (NMR) spectroscopy is arguably the most powerful technology for providing atomic-level structure and dynamics understanding of molecules and materials. The physical and life sciences communities exploit this analytical science technique extensively to address challenging issues in a wide range of systems relevant to, for example, pharmaceuticals, battery materials, catalysis and protein complexes. Importantly, the advances enabled by solid-state NMR as an analytical technique are continually increasing in line with technological progress in the development of new NMR hardware. In particular, the recent development of commercial 1.2 GHz NMR systems stands to open up exciting new directions in NMR methodological development and deliver unprecedented levels of structural, dynamic and mechanistic information. Seven 1.2 GHz NMR systems are already in operation across Europe with further systems soon to be installed in Germany and the USA. UKRI has recently invested in two such systems at the High-Field Solid-State NMR National Research Facility (NRF) at the University of Warwick, and at the Henry Wellcome Building for Biomolecular NMR Spectroscopy at the University of Birmingham. These systems are expected to be operational in the UK in 2025. The proposed project aims to optimise UKRI's substantial investment in high-field solid-state NMR spectroscopy (notably £23M in 1.2 GHz NMR) by working in partnership with fifteen internationally leading laboratories and seven industry partners. The work will focus on sharing technical and application know-how and expertise to deliver new experimental NMR methodologies and protocols, as well as new scientific insight into complex chemical systems. The project will be divided across three main classes of systems: inorganic materials, biosolids and pharmaceuticals, with researchers working in each of these fields. New experimental methodologies will be designed and investigated within the NRF itself, and also exploiting the wide range of NMR hardware and expertise available in the co-investigator team and partner institutions. As well as the main focus of ultra-high field NMR, the NRF and partner institutions will provide access to specialist NMR hardware such as very high- and low-temperature apparatus (100 - 1000 K) to enable complex structural and dynamic phenomena to be probed in greater detail. The techniques developed within the project will enable the capabilities of ultra-high field NMR to be fully realised and will lead to new atomic-level insights into systems of relevance to the wider scientific community and industrial partners. The dissemination of the research and the interaction with international academic and industry partners will help to maintain the UK's position as a world leader in solid-state NMR research.

Biomass burning (BB) and wildfires release huge quantities of particulates and trace gases into the atmosphere in amounts highly variable in space and time. Plume rise means these that under certain conditions these emissions can be injected into the atmosphere at heights far above the Earth surface, enhancing their long-range transport and altering their atmospheric chemistry, radiative budget, and air quality effects. Results from past project show that UK air quality can be signficantly affected by long-range transport of smoke from European and Russian wildires, and smoke from fires in Canada can be detected in air samples at DEFRA monitoring stations in e.g. Mace Head. Near real-time (NRT) atmospheric modelling and forecasting schemes aiming to realistically represent these aspects of the Earth system must include a high temporal resolution, non-retrospective source of BB emissions information - which generally comes from satellite Earth Obervation data. However, as discussed above, a fires smoke plumes buoyancy characteristics can strongly influence its atmospheric impact, and this is increasingly realised to be an important term to represent when modelling the long-range effects of wildfire smoke emissions. However, a lack of a priori information and, until recently, a directly-related EO observable, has meant that parameterisation of smoke plume injection height has received far less attention than has estimating the magnitude and variability of the smoke emissions. This KE Project will exploit the findings from two successful NERC research projects to provide major improvements to the current (ad hoc) prescription of wildfire smoke plume injection height in the prototype GMES UK/European atmospheric monitoring and forecasting scheme (the 'GMES Atmospheric Core Service', which is based on the world-leading integrated forecast system (IFS) of ECMWF in the UK and which is being desiged to provide the public, policy makers and downstream organisations with access to state-of-the-art atmospheric chemistry monitoring and forecasting data. The GACS serves a broad community of users, for example those involved in environmental policy development and policing, those delivering downstream services related to the health community (warning of increased asthma incidence during air pollution episodes), and those aiming to reduce public exposure to air pollution. We will work with Project Partners developing the GACS to exploit the research on plume height rise developed in NE/E016863/1 and the EO data processing procedures developed in NE/H00419X/1 to provide a much more realistic representation of smoke injection height in the GACS system; one that takes account of both fire and atmospheric characteristics such that the atmospheric transport of these emissions, including to the UK, can be better represented. The Project Partners are ECMWF, who lead GACS development in the UK and who operate the global model within which the plume rise scheme will be embedded, and Jülich Research Centre who are experts in the chemistry and transport of smoke emissions and who are a main partner in the GACS development. The KCL Environmental Research Group (KCL-ERG) are a 'down-stream' user of global atmospheric model output, funded by UK Government to provide regional air quality (AQ) monitoring and modelling, and this KE project will support them in starting to use the enhanced GACS outputs in their UK regional and London-wide AQ modelling schemes, in particular to take better take account of smoke-polluted air that is known to move into the UK from e.g. eastern Europe or western Russia, and which at present causes enhanced discrepancies between the AQ models and measurements (see DEFRA letter of support). All model outputs incorporating the new scheme will be made available freely through the GMES GACS system interface http://www.gmes-atmosphere.eu/ and for the UK region throught the online public interface www.londonair.org.uk/

Modern society is becoming increasingly reliant on digital data, yet most data is stored on magnetic hard disk drives that consume large amounts of energy and are limited in reliability. As data centres and volumes of servers grow it is becoming necessary to explore more efficient future digital storage technologies. Domain wall (DW) memory is a type of solid-state magnetic random-access memory that controls the motion and position of magnetic domains along a nano-scale magnetic track, i.e., racetrack (RT) memory. The magnetic moments of DWs are driven by transferring spin angular momentum from electrons in an applied current pulse. The position of the DWs can also be controlled by including defects along the RT that hold the DWs in place between current pulses. Conventional RT memories can vastly improve their storage density and connectivity if they expand into three-dimensional (3D) RT systems. However, this makes their fabrication and understanding the behaviour of DWs very challenging due to reduced access. The aim of this project is to use advanced electron microscopy techniques to construct 3D RT memories that provide direct, nano-scale analysis of their chemistry, structure and DW motion under operando conditions (current pulsing and heating). This will allow effective engineering of their operation, taking the functional performance of 3D RTs into a brand-new realm of understanding. Through optimising the composition, geometrical design and current pulse parameters of the 3D RTs we can address the key issue of consistent, power-efficient control of DWs motion in complex 3D nanomagnetic arrays. The results will not only lead to high impact publications and conference presentations, but also provide a wealth of information for expanding the field of spintronics into advanced nanomagnetic systems with complex 3D geometries.