Novel Synthetic Magnetic Materials Optimised for RF and Microwave Applications

Understanding and controlling the dynamic magnetisation behaviour of magnetic systems is fundamental to current and future technological applications. This thesis examines the underlying physics and explores the potential of magnetic thin-film systems for high frequency applications. Particular focus was directed towards exploiting interfacial phenomena, to enhance the frequency bandwidth response, and lithographic patterning to control the resonant frequency. The design, development and functional capabilities of a purpose built integrated system to probe the dynamic response of magnetic thin-film systems as a function of applied magnetic field, excitation frequency (up to 15~GHz) and sample orientation is detailed. Sample motion, with respect to the measurement transmission line, was achieved with $5\mu$m vertical motion precision and an angular step precision of 1.8$^\circ$. Physical mechanisms and parameters involved in damping were investigated on multilayers combining ferromagnetic (Co, CoFeB) and heavy metal layers (Pt, Ru) to quantify interfacial spin-transport. Enhanced interfacial transparency, leading to higher damping, is observed for more closely matched FM/HM crystal structures. Including a thickness-dependent spin-diffusion length gives a bulk value of $\lambda_\mathrm{s}=9.4\pm 0.7$~nm for Pt. The propagation of spin current into Pt was shown to be suppressed beyond a nominal SiO$_2$ insulating barrier of 2~nm corresponding to the formation of a continuous SiO$_2$ layers, from x-ray reflectivity analysis. The role of the induced moment in Pt with respect to interfacial damping was explored using synchrotron radiation. The nature of the induced moment was first explored by investigating the alignment of the Pt moment across the transition-metal-rare-earth ferrimagnetic magnetisation compensation transition. It was shown that the moment aligns with the transition-metal regardless of the dominant sub-lattice. The induced moment was correlated with the magnetic damping in CoFe/Pt and NiFe/Pt systems with Au and Cu spacer layers. The relation between an induced moment and enhanced damping highlights the role of $d - d$ hybridisation. Photolithography was used to pattern materials to enhance the effective magnetic field. An almost linear relation was observed between the aspect ratio and the induced anisotropy field, and hence the resonant frequency. This relation was used to produce tessellated patterns with varying aspect ratio, that demonstrated an isotropic, broadband and field-free dynamic magnetisation response

Optimising network interactions through device agnostic models

Physically implemented neural networks hold the potential to achieve the performance of deep learning models by exploiting the innate physical properties of devices as computational tools. This exploration of physical processes for computation requires to also consider their intrinsic dynamics, which can serve as valuable resources to process information. However, existing computational methods are unable to extend the success of deep learning techniques to parameters influencing device dynamics, which often lack a precise mathematical description. In this work, we formulate a universal framework to optimise interactions with dynamic physical systems in a fully data-driven fashion. The framework adopts neural stochastic differential equations as differentiable digital twins, effectively capturing both deterministic and stochastic behaviours of devices. Employing differentiation through the trained models provides the essential mathematical estimates for optimizing a physical neural network, harnessing the intrinsic temporal computation abilities of its physical nodes. To accurately model real devices' behaviours, we formulated neural-SDE variants that can operate under a variety of experimental settings. Our work demonstrates the framework's applicability through simulations and physical implementations of interacting dynamic devices, while highlighting the importance of accurately capturing system stochasticity for the successful deployment of a physically defined neural network

A perspective on physical reservoir computing with nanomagnetic devices

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used

Baseline natural killer and T cell populations correlation with virologic outcome after regimen simplification to atazanavir/ritonavir alone (ACTG 5201)

Objectives: Simplified maintenance therapy with ritonavir-boosted atazanavir (ATV/r) provides an alternative treatment option for HIV-1 infection that spares nucleoside analogs (NRTI) for future use and decreased toxicity. We hypothesized that the level of immune activation (IA) and recovery of lymphocyte populations could influence virologic outcomes after regimen simplification. Methods: Thirty-four participants with virologic suppression ≥48 weeks on antiretroviral therapy (2 NRTI plus protease inhibitor) were switched to ATV/r alone in the context of the ACTG 5201 clinical trial. Flow cytometric analyses were performed on PBMC isolated from 25 patients with available samples, of which 24 had lymphocyte recovery sufficient for this study. Assessments included enumeration of T-cells (CD4/CD8), natural killer (NK) (CD3+CD56 +CD16+) cells and cell-associated markers (HLA-DR, CD's 38/69/94/95/158/279). Results: Eight of the 24 patients had at least one plasma HIV-1 RNA level (VL) <50 copies/mL during the study. NK cell levels below the group median of 7.1% at study entry were associated with development of VL <50 copies/mL following simplification by regression and survival analyses (p = 0.043 and 0.023), with an odds ratio of 10.3 (95% CI: 1.92-55.3). Simplification was associated with transient increases in naïve and CD25+ CD4+ T-cells, and had no impact on IA levels. Conclusions: Lower NK cell levels prior to regimen simplification were predictive of virologic rebound after discontinuation of nucleoside analogs. Regimen simplification did not have a sustained impact on markers of IA or T lymphocyte populations in 48 weeks of clinical monitoring. Trial Registration: ClinicalTrials.gov NCT00084019

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

British Romanticism and the Global Climate

As a result of developments in the meteorological and geological sciences, the Romantic period saw the gradual emergence of attempts to understand the climate as a dynamic global system that could potentially be affected by human activity. This chapter examines textual responses to climate disruption cause by the Laki eruption of 1783 and the Tambora eruption of 1815. During the Laki haze, writers such as Horace Walpole, Gilbert White, and William Cowper found in Milton a powerful way of understanding the entanglements of culture and climate at a time of national and global crisis. Apocalyptic discourse continued to resonate during the Tambora crisis, as is evident in eyewitness accounts of the eruption, in the utopian predictions of John Barrow and Eleanor Anne Porden, and in the grim speculations of Byron’s ‘Darkness’. Romantic writing offers a powerful analogue for thinking about climate change in the Anthropocene

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant