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Optical Wireless Communication (OWC) underwater characterization for robot communication system
Full text linkThis thesis presents a comprehensive study on the design, optimization, and
evaluation of LED-based Optical Wireless Communication (OWC) systems within
confined pipeline environments, including both air-filled (free-space) and waterfilled
conditions. The investigation begins with an experimental characterization of
light transmission in an empty PVC pipe, where line-of-sight (LOS) and multipath
reflections are assessed using multiple wavelength LEDs. Optical enhancements such
as collimating lenses and reflectors are integrated and analyzed through simulation
and measurement to quantify their effects on received power and frequency
bandwidth.
To address the thermal limitations of high-power LEDs, an optimized heatsink
structure is developed using heat transfer modeling and validated through SolidWorks
CFD simulations. Experimental results confirm that the optimized heatsink
significantly improves LED frequency response and receives optical power, with up
to a 245% enhancement observed in certain wavelengths compared to conventional
cooling solutions.
Subsequently, the thesis focuses on the challenges of underwater optical
transmission. Water-filled pipe environments are investigated through both
theoretical and experimental frameworks, incorporating Fresnel’s reflection theory,
Beer–Lambert law, and Henyey–Greenstein scattering models. A controlled testbed
is used to evaluate LED performance under various water levels, revealing the
wavelength-dependent attenuation behaviors. Simulation results using Ansys Zemax
OpticStudio corroborate experimental findings, demonstrating the impact of beam
divergence, and material interfaces.
The outcomes of this research provide critical insights into the optical,
thermal, and environmental factors influencing OWC in pipeline systems. These
findings inform the development of robust, high-efficiency communication platforms
for applications in pipeline monitoring, submerged robotics, and smart infrastructure
systems
The microbiome of disease: Staphylococcal bacteriophages and Diabetic Foot Ulcer AMR pathogens.
No full textUnderstanding Burnout in Healthcare Professionals: Risk Factors for Medical Doctors and Nurses
Full text linkOccupational burnout is a psychological response to work-related stress. It is important to understand why healthcare professionals (HCPs) are more susceptible to burnout. Current burnout interventions are beneficial in the short-term, aiding the development of coping skills to manage stressors. However, it is well-recognised that they seldom support the unique needs of individuals. Understanding the risk factors for burnout and exploring how interventions work for different groups is needed to offer effective support. This would help to consider how services can improve their response to burnout to sustain a healthy workforce.
Chapter one provides a systematic review of the literature, exploring whether factors relating to racial-ethnic identity influence how much HCPs experience burnout. Twenty observational studies were identified which examined racial-ethnic related factors and burnout at a single timepoint, of which some also assessed workplace mistreatment. The narrative synthesis indicated that the role of racial-ethnic factors is inconclusive. This aligns with the diverse experiences of underrepresented groups reported in the wider literature. Future research could explore the intersections of individual and sociocultural factors on burnout to provide more supportive and inclusive workplaces to mitigate burnout.
Chapter two reports an empirical study which examined whether different burnout subtypes could predict treatment responses to the Mind Management Skills for Life (MMSFL) programme. Pre-existing data from trials with HCPs in the National Health Service (NHS) identified 12 subtypes using the Oldenburg Burnout Inventory. Results showed subtypes did not influence outcomes; however, subgroups of professionals experienced more severe burnout at the start and end of the intervention. Based on the findings, it is not possible to personalise the MMSFL programme at this stage. Future research should confirm if findings replicate across larger and more diverse samples. Methodological issues and clinical and research implications are discussed for the systematic review and empirical study
Understanding and optimising binding mechanisms to enhance process sustainability within food granulation
Full text linkGranulation is a well-established size enlargement process in which small particles agglomerate to form larger structures called granules. Enhancing sustainability within granulation is a key focus for the food industry, particularly when processing amorphous powders. These powders are sensitive to moisture and heat due to their Glass Transition Temperature, making controlled agglomeration difficult and often leading to material waste.
Despite its significance, the sustainability of competing granulation technologies remains underexplored. This study addresses this gap by screening the sustainability of four major granulators based on material, energy, and time efficiency. The findings highlight the energy efficiency of dry granulation due to the elimination of the drying step and the high material efficiency of wet granulation, attributed to its cyclic bonding mechanism.
Among the screened technologies, the High Shear Granulator emerged as a strong candidate for further development. A novel regime map was constructed using the parameters ‘Temperature – Glass Transition Temperature’ and ‘Liquid/Solid Ratio divided by a viscosity based constant’ to define an optimal operating region where controlled agglomeration can occur, minimizing caking and material waste.
High Shear Granulation was also used to create layered granule microstructures. Compared to standard granules with randomly distributed components, these layered granules demonstrated greater resistance to humidity-induced caking, colour change, and shrinkage during storage.
Additionally, this study evaluated Dry Twin Screw Granulation for the agglomeration of amorphous food powders. This led to the development of the first-ever regime map for the process, which captured key operational behaviours including granulation, barrel blocking, and extrusion. Dry Twin Screw Granulation exhibited strong potential based on sustainability metrics, positioning it as a viable future alternative to the more established techniques.
Ultimately, this study takes a multifaceted approach to advancing sustainable granulation by identifying key factors contributing to efficiency, process optimisation to minimize material waste, and developing next-generation granulation technologies
Unravelling age-associated skin microbiome dynamics: from community profiling to strain-level analysis
No full textUltra-thin gold and its applications in biomedical sensing
No full textGold nanomaterials can be engineered into various shapes, which strongly influence their optical and catalytic behaviour. This thesis focuses on quasi‑one‑dimensional gold nanotapes (AuNT) and compares them with conventional gold nanoparticles and other morphological variants. Lower‑dimensional gold nanostructures with atomic‑level thickness, including AuNT, were synthesised using a simple aqueous‑based wet‑chemical soft‑template method. Among these, AuNT was shown to possess unique plasmonic properties and high surface reactivity.
To explore their practical use, AuNT were embedded into inkjet‑printed poly(vinyl alcohol) hydrogels, creating reusable catalytic platforms. These printed AuNT hydrogels degraded pollutants, such as 4‑nitrophenol, more rapidly and enabled phenol oxidation under mild conditions. Their printed mesh structure improved accessibility of active sites, offering higher catalytic efficiency and consistent reusability.
AuNT also demonstrated strong enzyme‑like activity, outperforming natural peroxidase in standard colourimetric and fluorometric assays. When integrated into a glucose‑sensing system, they achieved a detection limit of 9.5 µM, showing promise for low‑cost diagnostic applications.
Overall, this work establishes gold nanotapes as a versatile class of nanomaterials with applications in water purification, biosensing, and catalytic technologies, and presents inkjet printing as a scalable route for developing practical nanozyme‑based devices
Surface-Immobilized pH-sensitive DNA Triplexes
No full textDynamic DNA machines leverage base-pairing specificity and environmental sensitivity to reversibly switch between conformational states. A prominent example is pH-sensitive DNA nanoswitches, actuated by proton-mediated Hoogsteen interactions in triplex-forming domains. To date, studies of pH-sensitive DNA nanoswitches have largely focused on DNA machines that are freely diffusing in the solution phase. For many applications such as biosensing and DNA data processing and archival, it is advantageous to integrate these dynamic DNA machines with solid-state devices, requiring immobilization on surfaces. This thesis explores the dynamics of pH-sensitive DNA triplexes immobilized as dense 2D monolayers (∼ 10^12 molecules/cm^2) on gold using thiol chemistry. Switching dynamics on-surface was monitored using quartz crystal microbalance with dissipation (QCM-D), while complementary single-molecule Förster resonance energy transfer (smFRET) studies confirmed solution-phase pH-responsivity. In solution, a 5-base loop triplex showed reversible switching with a pKa of 7.83 ± 0.03 (SD) and thermodynamic analysis indicated enthalpy-driven triplex formation. Following surface-immobilization, QCM-D confirmed that the DNA triplex switch retained functionality, switching between conformations with a pKa of 8.02 ± 0.03 (SD) and that the switching was repeatable and reversible over 20 pH cycles. Kinetic analysis revealed pH-induced opening of the immobilised triplex switch followed a first order exponential process while closing was a second-order exponential, in agreement with previous literature, with time constants of 20–90 s. Variations in loop length (from five bases-5B to twelve bases- 12B) showed that longer loops modified both the pKa and the kinetics of triplex closing, with the 12B construct exhibiting faster, first-order kinetics. The kinetics of loop opening were also shown to be influenced by the presence of mismatches within the triplex forming domain, with mismatches at the extremities resulting in faster opening rates. Finally, loop-complementary strands were seen to bind selectively to open triplexes, preventing reclosure and highlighting competition between Hoogsteen and Watson–Crick interactions. This study advances understanding of immobilized DNA triplex dynamics and supports the development of hybrid bioelectronic devices for sensing, logic, and data storage
Patchy Feminism: Young Chinese Women’s Perceptions and Practices of Feminism
No full textThis thesis focuses on young Chinese women’s perceptions and practices of feminism. It is contextualised within contemporary Chinese society, where feminism is gaining popularity as in the West, yet simultaneously faces unique restrictions driven by gendered norms, censorship, and nationalism-infused anti-feminism. This thesis is a qualitative study that draws on data collected through 40 online semi-structured interviews with young Chinese women aged 18–35 who engage in feminist discussions on social media. By using the theories of postfeminism, the theories of becoming and Chinese feminism, this thesis offers the concept of ‘patchy feminism’ to describe the entanglement between Chinese feminism and the very patriarchal structure it criticises
Generative models for unsupervised anomaly detection in temporal data
Full text linkLine-scan imagery has become a core sensing modality in automated railway inspection, enabling high-resolution, continuous monitoring of track structures. Unlike conventional 2D images, line-scan images are constructed row-by-row as the camera moves along the track, forming temporally ordered sequences where the vertical axis encodes time and the horizontal axis represents spatial cross-sections. This spatiotemporal structure introduces unique challenges for anomaly detection, including subtle structural deviations, periodic object patterns, and high-frequency, non-informative background textures.
To address these challenges, this thesis proposes two complementary generative frameworks for unsupervised anomaly detection in railway line-scan imagery. The first is a VAE-GAN model trained to predict future segments of track sequences based on temporal continuation. By learning normal structural dynamics, the model can identify deviations that violate expected continuity. A confidence-aware auxiliary decoder assigns pixel-wise certainty scores to guide the anomaly score, improving robustness against stochastic background textures such as ballast.
The second approach leverages Stable Diffusion as a high-fidelity reconstruction model. By fine-tuning a pretrained latent diffusion inpainting model using LoRA (Low-Rank Adaptation), we adapt the generative process to grayscale, line-scan railway images. To localize anomalies, we introduce a segmentation-guided pipeline using FastSAM to detect object-level differences between original and inpainted images. Regions exhibiting significant structural changes, as measured by Intersection over Union (IoU), are flagged as anomalous.
Both methods are evaluated on synthetic and real-world datasets, demonstrating their effectiveness in detecting structural faults under minimal supervision. The thesis concludes by comparing the two approaches, discussing their respective advantages, and proposing future directions for hybrid modeling. Overall, this work highlights the potential of generative modeling in addressing the unique demands of industrial inspection tasks involving line-scan imagery
Studies on the Calibration and Optimisation of Detector Response in Liquid Argon Time Projection Chambers for Neutrino Experiments
Full text linkNeutrino physics has advanced to the point where all neutrino oscillation parameters have been determined, with current generation neutrino experiments probing new discoveries in the field such as the sterile neutrino. This is a fourth neutrino flavour that only couples to gravity, and is theorised to be the cause of previously measured anomalies in neutrino oscillation physics. The Short-Baseline Neutrino program will perform high resolution νµ and νe oscillation measurements from the Booster Neutrino Beam (BNB), utilising ICARUS and the Short-Baseline Near Detector (SBND). SBND is a Liquid Argon Time Projection Chamber (LArTPC), placed 110 m from the BNB target, allowing for high precision neutrino-argon cross-section measurements. This thesis presents a methodology characterising the timing drift and resolution of cosmic-ray taggers before their installation around the SBND cryostat, allowing for full cosmic-ray background mitigation. Also featured is a procedure for validating the detector response modelling algorithms used in SBND software, comparing the expected response to average waveforms reconstructed from Monte Carlo event samples and SBND data. SBND is expected to exhibit ambiguities in event reconstruction due to using planes of sense wires to read out charge, motivating a pixelated charge readout for future LArTPC experiments, such as the DUNE near detector. The University of Sheffield provides an R&D experiment for this technology, named STEEL. Scintillation data from a 22-day data run of STEEL is used to calculate singlet, triplet and intermediate decay constants as τ1 = (12.4 ± 0.8) ns, τ3 = (0.715 ± 0.009) µs and τi = (69.6 ± 4.5) ns respectively, with pixel data used to determine the electron lifetime to be τe = (0.491 ± 0.006) µs. Measurements of τ3 and τe leads to average oxygen impurity density calculations of [O2]γ = (1.48 ± 0.04) ppm and [O2]e = (1.42 ± 0.01) ppm respectively
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