Open Research Exeter - University of Exeter
Not a member yet
41213 research outputs found
Sort by
Microwave Properties of Helical Resonators
No full textThis thesis investigates the electromagnetic resonances of helical structures at microwave frequencies through both modelling and experimentation, with a particular emphasis on the relatively unexplored higher-order modes. By systematically varying geometric parameters, especially pitch, this study reveals unique properties that emerge at many modes, as well as showing transition toward axial field characteristics as mode number is increased. The dispersion of infinitely long helical structures is characterised, demonstrating their predictive capability for lower-order modes in finite systems and their tunability through geometric tailoring.
Additionally, a more complex two-handed variant of the conventional helix, referred to as the ambihelix, is analysed in detail with a focus on its resonant behaviour. This structure supports two distinct low-order modes: one exhibiting an almost pure electric dipole characteristic and the other an almost pure magnetic dipole. Importantly, it is shown that by adjusting geometric parameters, these modes can be reordered in frequency or even tuned to occur at the same frequency which has never been shown before, offering new possibilities for resonance control.
Finally, the coupling between pairs of helices and ambihelices is investigated through both simulation and experiment. By carefully rotating these structures, near-zero coupling between closely spaced resonators can be achieved. This is experimentally validated using an innovative liquid metal 3D-printing technique for mould fabrication, demonstrating controlled coupling behaviour and the potential for achieving superdirectivity in coupled systems. Further extending this concept to infinite chains of helical elements, the study further shows that specific geometric configurations can result in near-zero group velocity, paving the way for novel wave propagation applications.</p
Coherent Control of Nitrogen Nuclear Spins via the V<sup>-</sup><sub>B</sub> -Center in Hexagonal Boron Nitride
No full textCharged boron vacancies (−) in hexagonal boron nitride (hBN) have emerged as a promising platform for quantum nanoscale sensing and imaging. While these primarily involve electron spins, nuclear spins provide an additional resource for quantum operations. This work presents a comprehensive experimental and theoretical study of the properties and coherent control of the nearest-neighbor 15N nuclear spins of −-ensembles in isotope-enriched h10B15N. Multi-nuclear spin states are selectively addressed, enabled by the state-specific nuclear spin transitions arising from spin-state mixing. We perform Rabi driving between selected state pairs, define elementary quantum gates, and measure longer than 10 s nuclear Rabi coherence times. We observe a two orders of magnitude nuclear g-factor enhancement that underpins fast nuclear spin gates. Accompanying numerical simulations provide a deep insight into the underlying mechanisms. These results establish the foundations for leveraging nuclear spins in − center-based quantum applications, particularly for extending coherence times and enhancing the sensitivity of 2D quantum sensing foils.</p
Robust Performance of Sub‐Thermophilic Anaerobic Digestion Enabled by Microbial Functional Redundancy
No full textGlobal demand for renewable energy continues to rise, highlighting the importance of renewable natural gas asa sustainable alternative to fossil fuels. Anaerobic digestion (AD) is a proven, scalable technology for producing biogas, yet its efficiency depends on stable microbial communities operating under suitable temperature regimes. This study evaluates the effect of lowering operating temperatures in pilot‐scale digesters on gas yields, digestate chemistry, and microbial dynamics. Six continuously stirred tank reactors are acclimated at 55°C before three are gradually reduced to 48°C, while three remain at 55°C. Over a 60‐day period, methane yields do not significantly differ between the two treatments, averaging 140 l CH
4
kg
−1
VS at 48°C and 125 l CH
4
kg
−1
VS at 55°C, with biomethane composition stable at 54%. Metagenomic analyses reveal higher microbial diversity and functional redundancy at 48°C, with greater representation of methanogenic taxa such as
Methanocelleus
. In contrast, communities at 55°C show reduced diversity and dominance by thermophilic taxa. These results suggest that lowering AD operating temperatures may enhance microbial resilience without compromising methane production. Reduced energy input for heating at 48°C also offers potential operational and environmental benefits, which may improve commercial viability, subject to site‐specific factors and further validation of large‐scale AD facilities.</p
FDTD Algorithms and Plotting Code for "Scattering in time-varying Drude-Lorentz models"
No full textSupplementary code for "Scattering in time-varying Drude-Lorentz models", published in Journal of Optics, 2 March 2026. DOI: https://doi.org/10.1088/2040-8986/ae4bf2Article abstract:Motivated by recent experiments, the theoretical study of wave propagation in time varying materials is of current interest. Although significant in nearly all such experiments, material dispersion is commonly neglected in theoretical studies. Yet, as we show here, understanding the precise microscopic model for the material dispersion is crucial for predicting experimental outcomes. Here we study the temporal scattering coefficients of four different time-varying Drude-Lorentz models, exploring how an incident continuous wave splits into forward and backward waves due to an abrupt change in plasma frequency. The differences in the predicted scattering are unique to time-varying media, and arise from the exact way in which the time variation appears in the various model parameters. We verify our results using a custom finite difference time domain algorithm, concluding with a discussion of the limitations that arise from using these models with an abrupt change in plasma frequency.</p
Social comparison and its association with disordered eating symptoms: A systematic review and meta-analysis.
No full textOBJECTIVE: Social comparison has been widely implicated in the etiology and maintenance of body dissatisfaction and eating disorders. At the same time, however, the magnitude of this relationship remains unclear, with existing studies varying widely in methodology, measurement, and sample characteristics. METHOD: To address this gap, we conducted a systematic literature review and meta-analysis (PROSPERO ID: CRD42024626732) to estimate the overall effect size of the association between social comparison and disordered eating symptoms and examine key moderators that may influence this relationship. RESULTS: Searches of databases (PubMed/Medline, Web of Science, PsycINFO, Scopus, ProQuest, ETHoS, MedRxiv, and PsyArXiv) identified 305 studies comprising 383 distinct samples, with a total of 126,702 participants included. The methodological quality of the included studies was assessed. A multi-level meta-analysis found a significant overall medium effect size, Zr = 0.43, p </p
Biodiversity Impact of China's Power System Transition From a Life Cycle Perspective
No full textBiodiversity loss is a critical but underexplored dimension of global low-carbon energy transitions, particularly in the context of China’s power sector. China’s power sector is undergoing an in-depth decarbonization, which could shape both the local and global environment. To uncover the biodiversity consequences of China’s power system decarbonization, this thesis develops a spatially explicit life cycle assessment (LCA) model to evaluate the biodiversity impacts of China’s power system by applying three mainstream LCIA methods. The model covers the 2020 baseline and two 2050 scenarios—NDC (National Determined Contribution) and PEAK30—and integrates a province-level inventory with scenario-based technology deployment. A stepwise approach was adopted: the model robustness was first validated using indicators of environmental footprints such as Greenhouse Gas Emissions (GHG), Particulate Matter Formation, Freshwater Eutrophication, Mineral Resource Use, and Land Transformation, then subsequently applied to biodiversity impact assessment.
The environmental footprint results of 2020 indicate that coal power dominated climate and air pollution impacts, while hydropower and wind contributed significantly to land and mineral pressures. Environmental footprint results of future scenarios reveal substantial co-benefits and trade-offs: decarbonization reduces climate- and pollution-related burdens by up to 90%, yet land transformation and mineral demand increase markedly—by up to 4–6 times—driven mainly by biomass expansion and large-scale renewable deployment.
Biodiversity impacts assessment has further confirmed a fundamental biodiversity trade-off: although decarbonization can successfully alleviate global warming and pollution-related biodiversity impacts, it also exacerbates the land use-driven biodiversity loss, bringing uncertainty to the cumulative biodiversity impacts. This trade-off primarily stems from significant biomass feedstock cultivation. Technology choices strongly influence outcomes: wind, solar, and nuclear offer far lower per-kWh biodiversity impacts than biomass with CCS. Current LCIA methods, however, underrepresent site and species-specific biodiversity impacts. These findings highlight the necessity to integrate biodiversity metrics into energy planning, regulating land-intensive technologies and develop more comprehensive assessment tools that also capture wider aspects and site-specific biodiversity impacts.</p
Stress and strain in magma‐mush reservoirs: implications for reservoir failure and magma propagation
No full textInvestigating the mechanical stability and failure of magma reservoirs following magma supply is critical for volcanic hazard assessment. While magma reservoirs were traditionally modeled as melt-filled cavities, they are now more often described as crystal mushes where melt flows and is stored in porous networks. Little attention has been devoted to stress changes within and outside magma-mush reservoirs, which ultimately dictate their failure and the transport of magma toward the surface. Here, we address this gap by developing Finite-Element numerical models of magma supply in gravitationally loaded, poroelastic magma reservoirs embedded in an elastic crust. We explore the stress changes and volumetric strain rate within the reservoirs during and after magma supply, and perform sensitivity tests on different poroelastic and magma supply parameters. Contrarily to melt-dominated, static models, we find that regions where failure is promoted, both within and around the mush, evolve through time and are localized in the surroundings of the magma injection site. However, melt diffusion eventually leads to failure being promoted at the top of the reservoir. We also highlight how pore pressure compensates gravity-induced compressive stresses, so that smaller magma overpressures are required to reach failure-likely conditions. Finally, we find that tensile stresses due to magma supply within the mush itself may be large and lead to diffuse mush failure or destabilization. Further modeling developments, combining our approach with fracture propagation, and better constrained magma and mush properties will improve our understanding of reservoir stability and how volcanic eruptions are triggered.Plain Language SummaryUnderstanding how magma reservoirs rupture is key to predicting volcanic eruptions. When new magma enters a reservoir, it increases pressure and stress in the surrounding rocks, potentially causing cracks by which magma might reach the surface. Traditionally, magma reservoirs were conceived as liquid-filled cavities, but they are now more often described as mushy regions where magma flows through a network of crystals. Our study uses numerical models to simulate what happens when magma is added to this type of reservoir. We look at stress and strain rate changes, as they help identify when and where cracks may form. Our models also account for the effect of gravity. Unlike static models of fluid-filled reservoirs, our results show that failure zones shift over time as magma diffuses through the mush, initially expanding near the magma entry area, then migrating upward. Magma supply can induce large tensile stresses that may break the mush; their magnitudes, however, are controlled by the mush, magma, and melt supply parameters. We also discuss how our models might be integrated with existing models of magma propagation. Future advances in both models and knowledge of the properties of mushy reservoirs will help us understand and better forecast volcanic eruptions.</p
Problem-Specific Quantum Machine Learning and Quantum Algorithms: Data Embeddings, Symmetries and Structure
No full textDevelopments in physics, mathematics, computer science and engineering have contributed to the rise of the field of quantum computing over the last few decades. Quantum devices are becoming more powerful and more reliable than ever, poised to disrupt a variety of areas within both research and industry. Despite this promise, there are many challenges that need to be overcome before quantum computers become a widespread practical tool. One of the keys to unlocking the potential of quantum computing is the development of quantum algorithms which boast an advantage over classical protocols for solving problems. This thesis presents some original approaches to tackling this task. Specifically, I present several quantum machine learning and algorithmic procedures, some adapted from established protocols, others novel. Throughout the thesis, I introduce and make use of several state-of-the-art quantum subroutines, including linear combinations of unitaries, quantum singular value transformation, and several techniques for ground state preparation. Our algorithms are all motivated by specific problems, interesting from a theoretical and practical point of view, including tasks from quantum chemistry, function and image classification, and graph analysis. Our general algorithm design process is based upon three foundation stones: data embeddings, symmetries and structure. Through theoretical investigations, simulations and scaling analysis, we demonstrate the utility of our protocols, paving incremental yet meaningful steps toward the realisation of practical quantum advantage.</p
The effect of timescale separation on the tipping window for chaotically forced systems
No full textTipping behavior can occur when an equilibrium of a dynamical system loses 7 stability in response to a slowly varying parameter crossing a bifurcation threshold, 8 or where noise drives a system from one attractor to another, or some combination of 9 these effects. Similar behavior can be expected when a multistable system is forced 10 by a chaotic deterministic system rather than by noise. In this context, the chaotic 11 tipping window was recently introduced and investigated for discrete-time dynamics. 12 In this paper, we find tipping windows for continuous-time nonlinear systems forced 13 by chaos. We characterize the tipping window in terms of forcing by unstable 14 periodic orbits of the chaos, and we show how the location and structure of this 15 window depend on the relative timescales between the forcing and the responding 16 system. We illustrate this by finding tipping windows for two examples of coupled 17 bistable ODEs forced with chaos. Additionally, we describe the dynamic tipping 18 window in the setting of a changing system parameter.</p
Advancing single cell microbiology and bacteriophage-bacteria interactions using microfluidics and image-based deep learning
No full textThe rapid rise of antimicrobial resistance (AMR) poses a critical global health threat, demanding innovative approaches for diagnosing infections and developing effective therapies. Conventional culture-based assays are often slow, labour-intensive, and limited in their ability to capture the heterogeneous responses of individual bacteria to antibiotics or bacteriophages. This thesis addresses these challenges by developing integrated, label-free, droplet-based microfluidic platforms coupled with advanced deep learning algorithms for studying bacteriophage-host interactions towards high-throughput single-cell microbiology.
First, microfluidic devices featuring tailored droplet trapping arrays were designed and fabricated using soft lithography. These platforms enabled the long-term culture and monitoring of individual bacterial microcolonies under controlled microenvironments, supporting time-resolved quantification of bacterial growth, morphological changes, and phenotypic responses during bacteriophage–host interactions. By miniaturising cultivation volumes into picolitre droplets, bacterial–phage interactions were accelerated and parallelised, reducing assay times from days to hours. Complementary computational pipelines were established to automate droplet detection, segmentation, and single-cell morphological analysis using deep convolutional neural networks, thereby eliminating reliance on fluorescent labels and subjective manual inspection. These tools were applied to investigate clinically relevant individual and polymicrobial–phage interactions, with a focus on Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus. Morphology-based phenotyping revealed dynamic lytic and non-lytic responses at the single-cell level, including transient states such as spheroplast-like morphotypes that may contribute to phage persistence.
Together, the methodological and analytical advances presented in this work establish a scalable, high-resolution framework for studying complex microbial communities and their viral predators. Beyond fundamental microbiology, these approaches hold translational potential for rapid antimicrobial susceptibility testing and personalised phage therapy, offering a path toward more effective interventions against drug-resistant infections.</p