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Structure-Property Relationships in Anhydride-Cured Epoxy Resin
No full textAnhydride-cured epoxy after amines are among the most widely used curing agents.
Anhydride-cured epoxy resins exhibit excellent thermomechanical properties as matrices
in polymeric composite materials and are widely used in the aerospace and automotive industries. This study investigates the influence of anhydride structures and initiator choice on the cure kinetics, thermal, and mechanical properties of the cured diglycidyl
ether of bisphenol A (DGEBA).
N, N-dimethylbenzylamine (BDMA) and 1-methylimidazole (1MI) were employed as the
initiator, and the hardeners used were cis-1,2,3,6-tetrahydrophthalic anhydride (THPA),
a combination of cis and trans-hexahydro-4-methylphthalic anhydride (MHHPA) and cis-hexahydrophthalic anhydride (HHPA).
Isothermal cure kinetics data revealed that the anhydride structure and the choice of
initiator influenced the conversion rate and rate constant of the epoxy-anhydride
network. Kinetic triplet (Ea, A, and f(α)) parameters were obtained by fitting the
experimental data to the Kamal-Sourour, Sestak-Berggren, and isoconversional models.
Data on the glass transition temperature (Tg) of the different epoxy-anhydride networks
indicate that the molar mass of the alicyclic component in the diester crosslink
predominates in determining the stiffness of the chains.
ATR-FTIR analysis confirmed the formation of ester linkages in the epoxy-anhydride
networks. PALS data also revealed that the anhydride's structure influenced the average
free volume in the network.
The size of the free volume has been shown to affect the compressive modulus. An inverse relationship was observed between the flexural modulus and compressive modulus. Additionally, a positive correlation emerged between the density of the different networks and the fracture toughness results.
Characterising these epoxy-anhydride-cured networks based on curing kinetics and their
responses to both static and dynamic stresses provide insights into their potential for
demanding applications in polymer composites and in the aerospace and automotive
sectors
A Reconfigurable Robotic Fabrics Framework for Scalable Collective Behaviours of Robot Swarms
No full textModular reconfigurable robotics is advancing the development of intelligent autonomous systems. Robotic fabrics are emerging systems from this field that have shown great potential in morphing their shapes dynamically to suit many environments and to perform difficult tasks. These fabrics are formed by mechanically coupling modules of swarm robots to create different configurations. Specifically, this work focuses on the development of robotic fabrics for planar (2D) motion. Despite their numerous capabilities, existing robotic fabrics are mostly not modular, cannot self-propel, and are often limited in scalability and reconfigurability. In this thesis, a novel framework for producing deformable, scalable, and easily reconfigurable robotic fabrics using self-propelling Kilobot modules is proposed, realized, and implemented for different applications. The framework is made up of rigid holding rings and deformable
links. Based on the geometry, the deformable links are of two types: spring-based and rod-based. These components of the fabrics, which were realized through advanced additive manufacturing, support fabrics of arbitrary size and shape, and enable easy plug-and-play reconfiguration. An open-loop and a deformation-correcting controller were first implemented on four robotic fabrics configured with the two deformable links. The results demonstrated that spring-based fabrics achieved improved coordination under the deformation-correcting controller, while rod-based fabrics performed more effectively with open-loop control. This was attributed to their lower elasticity. A probabilistic controller was derived and implemented on 7 × 7 configurations. The robotic fabrics successfully turned left and right at the four specified rotation radii. They were also used to demonstrate manipulation capabilities. Finally, robotic fabrics were utilized to implement four random walk strategies for fabrics of different sizes, with up to 100 physically linked modules. Self-propelling robotic fabrics based on distributed, embodied intelligence could pave the way for novel applications, from search and rescue operations to medical uses within the human body
Linear State Estimation for Real-Time Monitoring of Active Distribution Networks
No full textThe rapid integration of distributed energy resources (DERs) and smart technologies has transformed distribution networks into active systems, creating new monitoring and control challenges. Conventional state estimation methods for transmission networks are not suited to active distribution networks (ADNs), which often feature unbalanced loads, weakly meshed structures, and diverse measurements.
This thesis develops and validates a novel linear distribution network state estimation (DNSE) framework for fast and robust state estimation and anomaly detection in ADNs. The proposed method improves computational efficiency and scalability by directly relating hybrid measurements—including real-time and delayed smart meter data—to system states using linear equations, eliminating model approximations.
Four solution methods are introduced, offering trade-offs between accuracy and computational burden. Both static least squares and dynamic Kalman filtering approaches are used, with dynamic DNSE enabling pre-estimation anomaly detection. A unified anomaly detection mechanism utilises the innovation vector and robust Mahalanobis distance to distinguish real events from measurement errors.
Simulation results on IEEE test feeders demonstrate superior accuracy and speed compared to established nonlinear methods, even under challenging scenarios. This research advances DNSE for ADNs, supporting grid operators in managing DER-rich networks and enhancing operational efficiency
Graphical Summarisation of Argumentative Text
Full text linkThis work investigates the summarisation of argumentative text. Our main focus is on the generation of graphical summaries from dialogical argumentative texts such as online news comment sections.
We develop a novel type of graph structure to summarise argument, which we refer to as Argument Summary Graphs or ASGs. Our contributions are twofold. Firstly, we develop two new data resources to investigate the generation of ASGs, the Debatabase-ASG dataset (created from a curated collection of online debates), and the SENSEI-ASG dataset, developed by adding annotations to the SENSEI dataset of online news article comments.
Secondly, we investigate alternative methods for generating ASGs from both datasets using Large Language Models (LLMs). We carry out experiments on the Debatabase-ASG dataset and find that an end-to-end text-to-text method performs better than a pipeline approach. Additionally, we test how well the end-to-end approach generalises across corpora, using both of the corpora we created, and a third argument mining corpus, the Argument Annotated Essays corpus (AAEC). We find that additional fine-tuning on a monological dataset from a distinct Argument Mining task provides similar benefits to fine-tuning on a second in-genre dataset.
We also carry out two strands of work not narrowly focused on ASGs. Firstly, we investigate the prediction of Reasoning Markers, used for detection of argumentative text. We create a corpus for this task and implement multiple baselines, with the best achieving an F1 score of 0.69. Secondly, we investigate Argument Structure Parsing: the task of extracting argumentative components and their relations from text. We fine-tune several LLMs which achieve near state-of-the-art scores in the end-to-end setting. We propose a novel method of formatting the output for this task, which we find competitive with the existing state-of-the-art approach in evaluation metrics, while being significantly faster to generate
Computationally efficient signal processing of image sequences for thermal inspection of additive manufacturing
No full textThis thesis presents a unified, physics-informed framework for the spatio-temporal super-resolution of thermal imaging, motivated by the limitations of commercial cameras in monitoring dynamic additive manufacturing (AM) processes. Conventional infrared systems don't simultaneously achieve high spatial and temporal resolution, constraining their ability to capture rapid thermal transients that govern part quality.
The challenge is addressed by reformulating thermal image enhancement as a state-estimation problem grounded in the heat diffusion equation. A discretised state-space model enables the use of Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing to reconstruct high-resolution, high-frame-rate thermal fields from spatially and temporally downsampled observations. This physics-informed approach produces reconstructions that preserve sharp spatial details and transient dynamics, significantly outperforming interpolation-based methods in both accuracy and stability.
To overcome the computational burden of standard Kalman filtering, a distributed estimation architecture was developed through the Reduced-Update Partition-Based Kalman Filter (RU-PBKF) and Reduced-Update Partition-Based Smoother (RU-PBS). By partitioning the image into locally interacting subsystems, the framework exploits the inherent locality of heat diffusion to achieve scalability, reducing computational complexity while maintaining estimation fidelity.
The framework was further extended to accommodate rolling shutter imaging, using a time-varying observation model and state-augmentation strategy to correct temporal misalignment and perform temporal super-resolution. Finally, a Generalised Likelihood Ratio Test (GLRT) with a CUSUM-type accumulator was integrated for online detection, estimation, and compensation of unknown thermal inputs, enabling adaptive response to unmodelled heat events.
Comprehensive simulations demonstrate that the proposed framework achieves accurate, scalable, and robust reconstruction of transient thermal phenomena. The framework provides a foundation for real-time, physics-consistent monitoring in additive manufacturing and other thermally driven processes
Optimisation of chemical absorption for the decarbonisation of the iron and steel industry: an integrated simulation and pilot-scale investigation to benchmark capture performance from high-CO2 process emissions
Full text linkDecarbonisation of critical hard-to-abate industrial sectors such as the iron and steel industry is crucial for meeting climate targets, with chemical absorption carbon capture identified as a key transitional technology. However, its application to these industrial sectors is hindered by a significant knowledge gap: the absence of publicly available and transparent performance benchmarks using a conventional capture plant under elevated CO2 conditions that are typical of industrial process emissions.
This thesis addresses this gap through a synergistic methodology combining process simulation with pilot-scale experimentation. An initial process simulation model was developed in Aspen Plus to identify parametric trends and plant performance across a wide range of operating conditions. The results from this investigation were used to inform initial starting conditions for a targeted experimental campaign on a conventional packed bed system.
The experimental campaign established a novel performance baseline for the treatment of flue gas with CO2 concentrations ranging from 10 to 25 mol.% CO2, using a solvent of 35 wt.% monoethanolamine. The dataset was then used to calibrate and enhance the simulation model to create a high-fidelity predictive tool that forms the foundation of a digital twin for the capture plant. This was achieved using a novel Python automation framework developed to control Aspen Plus. The enhanced model was successfully used to interpolate and extrapolate performance of the capture plant, identifying optimal operating conditions beyond the experimental scope.
The novel baseline performance dataset can be used to compare the capture effectiveness of alternative and advanced process configurations in the treatment of heavy industry process emissions. In addition, the methodologies outlined in this thesis can serve as a guide for the development, validation, and enhancement of representative process simulations of other chemical absorption systems. This work ultimately seeks to accelerate the deployment of carbon capture for industrial decarbonisation
Developing a preclinical-grade chemical into a potential therapy against malignant brain cancer
No full textGB is the most common primary brain tumour in adults, with the current standard of care involving surgical resection with concurrent chemoradiotherapy. Despite current efforts exploring targeted therapies, immunotherapies and drug delivery systems to bypass the blood-brain barrier, clinical outcomes remain poor, underscoring the need for a novel therapeutic strategy.
GB cells exhibit metabolic adaptation, simultaneously upregulating glycolysis and OXPHOS, which presents a vulnerability. Previous work by Polson et al. (2018) identified the chaperone HSPD1 as a potential target given its role in regulating metabolism, and their small molecule KHS101 showing selective GB targeting.
This study identified and characterised 21 analogues of KHS101 for exploration of a new frontrunner compound and an additional analogue where KHS101 is conjugated to TPP to explore KHS101 ‘on-target’ effects with mitochondrial HSPD1 in patient-derived GB cells, including a non-cancerous control cell line.
An NADH-based HSPD1:HSPE1 refolding assay was utilised to demonstrate the compound's ability to affect HSPD1:HSPE1 activity. However, lacking sensitivity and sufficient consistency, an image-based multi-parametric assay was employed. This assay assessed six parameters measuring three phenotypes: autophagy and cytoplasmic degradation (metabolic dysfunction), vacuolised area of the cytoplasm and number of vacuoles (vacuolisation) and number of stress fibres and nuclear-to-cytoplasmic ratio (stress). Among the 21 KHS101 analogues, SCBT-21 emerged as the leading compound, demonstrating strong metabolic exhaustion and selectivity in GB and NP cells.
Further in vitro analysis revealed that SCBT-21 had a pronounced effect on cell viability of recurrent GB models over primary GB models, reflected in their IC₅₀ values. Additionally, SCBT-21-treated cell cycle indicator carrying GB cell models demonstrated a G1 cell cycle arrest in a small ‘resistant’ population of cells in GB models, while no such effect was observed in the NP model. In vivo studies using syngeneic mouse models and a pharmacokinetic (PK) study demonstrated SCBT-21 was able to penetrate the blood-brain barrier and accumulate in the brain.
To confirm KHS101 targets the mitochondria, a mitochondrial-targeted analogue (KHS101TPP) was synthesised to bypass the cytoplasm and cytoplasmic proteins (e.g., TACC3 and cytoplasmic HSPD1), showing enhanced potency across multiple assays, with the suggestion that KHS101 is targeting the mitochondria.
Overall, SCBT-21 represents an analogue with significant advancement over KHS101, with pronounced metabolic exhaustion and favourable pharmacokinetics in vivo. SCBT-21 lays the foundation for future optimisation, including SAR refinement and dosage studies towards an effective therapeutic agent against GB
The electronic and magnetic properties of thin film Fe₃Sn₂
Full text linkThis thesis is an exploration of the growth and properties of thin film Fe₃Sn₂.
Through precise stoichiometric deposition, this frustrated kagome ferromagnet
can be grown with minimal impurities and intergrowths from the other Fe-Sn
intermetallic alloys. The frustrated spin texture within the Fe₃Sn₂, and the reorientation these spins undergo with changes of field and temperature, have been predicted to produce the spin frustration needed to stabilise magnetic skyrmions.
These skyrmions in turn have potential applications in new architectures for
magnetic data storage and the development of neuromorphic computing. Beyond
this, the electronic band structure of Fe₃Sn₂ is predicted to have many novel prop-
erties such as flat bands and Dirac-points within tantalising reach of the Fermi
surface. The resulting contribution to the anomalous Hall effect as well as unique
magnetoresistance curves could also feature in future spintronic devices in order
to improve the energy efficiency of our already highly optimised computation
methods.
To begin, high quality 80 nm to 100 nm epitaxial films of Fe₃Sn₂ are fabricated
through sputter deposition on to heated single crystal sapphire substrates with
a seed layers of epitaxial Pt. Using X-ray diffraction these films were found to
be strongly orientated in the (001) direction with large 30 nm grain sizes. Scan-
ning transmission electron microscopy, combined with a custom phase matching
technique, allows for spatial maps of these thin films to be produced covering
100s of nm of the film’s cross section and with spatial resolution of 3 nm by 3 nm.
This large scale crystal characterisation confirms the ability to control the phase
content of the ferromagnetic Fe₃Sn₂ and the antiferromagnetic FeSn through
the growth process. The ratio of power fed to the Fe and Sn magnetron guns
alone, as opposed to temperature or growth rate, is found to be the determining
factor that changes the resulting film composition, due to the precise control of
growth rate that can achieved from each gun, that can then in turn lead to specific
stoichiometries in the resulting films.
The highly pure, with over 97% crystalline content, Fe₃Sn₂ films were found
to have saturation magnetisation of 777 emu/cm³ ± 9 emu/cm³ and very soft
coercivity of 1.5 mT ± 0.2 mT. Analysis of the magnetic properties of the resulting
films showed expected reduction in saturation magnetisation with increasing
FeSn intergrowth, but an unexpected minima behaviour in the coercivity of highly mixed Fe₃Sn₂ and FeSn film with changing temperature, indicating a strong
ferromagnetic-antiferromagnetic coupling. Fitting to the Bloch-3/2 law allows a spin wave stiffness of (4.1 ± 0.2) × 10⁻⁴⁰ J/m² to be extracted. Further high temperature measurements reveal that a temperature of 750 K results in the Fe₃Sn₂ irreversibly breaking down, which in turn causes the appearance of a hitherto unseen magnetic transition at 120 K in the ZFC/FC measurements.
The electronic properties of ultra-thin 5nm thick Fe₃Sn₂ films are also explored, with surprisingly no orbital contribution to the anomalous Hall effect observed despite being strongly suggested in the literature. Instead, a carrier type change-over is confirmed by the change of sign of the ordinary Hall effect at 75 K. This critical temperature was found to correspond closely to the point of spin reorientation and an observed change in the scaling of the anomalous Hall effect. Along with this, a linear negative magnetoresistance that is observed to have its gradient decrease with increasing temperature is observed, with currently no established theoretical explanation with potential applications of such consistent linear magnetoresistance being part of the detector system in a high field magnetic sensor
The Quantum Backflow Phenomenon
Full text linkIt has been known since at least 1969, that there exist quantum states of a free
particle on the line with purely positive momentum that exhibit probability transfer
towards the negative half-line. This phenomenon, the quantum backflow effect, is
the subject of this thesis. The largest amount of backflow any state can exhibit over
a single time interval, the Bracken–Melloy constant, was initially calculated to be
cBM ≈ 0.04.
In the first half of this thesis, we consider the question of how much quantum
backflow states can exhibit over disjoint time intervals. This is done by formulating
the question in terms of the spectra of bounded operators. When considering more
than one disjoint time interval, we discuss a new phenomenon where a state exhibits
more probability transfer in the same direction as its momentum than any classical
state. We call this effect quantum overflow. Given a number M of disjoint time
intervals, we give bounds on the maximum amount of backflow and overflow a state
can exhibit over any M many intervals. The limiting cases in which two disjoint
intervals merge into one is particularly studied. Finally, we show plots of the time
t = 0 momentum space wavefunction of states exhibiting multiple quantum backflow
and quantum overflow.
The second half of the thesis contains a detailed numerical investigation into the
single backflow problem. Based on previous conjectures, we construct the maximum
backflow state from a given set of normalised states closely resembling the sinc
function. The Bracken—Melloy constant is then bounded from below by the largest
solution to a generalised eigenvalue problem. Using a multi-step numerical procedure,
we solve a high precision large dense generalised eigenvalue problem. From this
we obtain new numerical approximation to the backflow state and the best known
rigorous lower bound of the Bracken—Melloy constant
Two theorems on sums over zeros of the Riemann zeta function.
Full text linkThis journal-style thesis presents two new results concerning discrete moments of derivatives of the Riemann zeta function.
In Chapter2 we establish a generalisation of the Landau-Gonek Theorem, in particular proving asymptotics uniform in and for \begin{equation*}
S(X,T)=\sum_{T<\Im(\rho)\le 2T}\chi(\rho)X^{\rho},
\end{equation*}
where are the non-trivial zeta zeros and is the factor from the functional equation . This allows one to evaluate sums of approximate functional equations evaluated at the non-trivial zeta zeros, and as a consequence of this we are able to provide a new proof of the Generalised Shanks conjecture.\\
In Chapter 3 we consider sums of the form These sums were first considered by Gonek in 1984, whereby a leading asymptotic was established. We extend this to a full asymptotic by establishing all of the lower order terms in the asymptotic expansion. As a corollary we recover a 2008 theorem due to Milinovich which provides a full asymptotic for , and we go further by establishing the full asymptotic for for all positive integers . Our theorem is entirely unconditional, but we provide sharper bounds on the assumption of the Riemann Hypothesis
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