Theses - Chemical Engineering and Biotechnology

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Embargo
DNA Inspired Tools for Biosensor Design
Etheridge, William
This thesis endeavours to advance the field of biosensor development by introducing innovative DNA immobilisation strategies, with a primary focus on designing a new generation of biosensors utilising DNA as a biorecognition element. In Chapter 2, the exploration of a copper (II) (Cu (II)) specific DNA cleaving DNAzyme was undertaken towards the development of a sensitive Cu (II) sensor. Despite the promising utility of DNA cleaving DNAzymes, their slow kinetics limit their application in biosensors. Through studies in solution, it was discovered that the addition of polydopamine (PDA) and gold (Au) nanoparticles significantly enhances the rate of Cu (II) mediated cleavage, making this DNAzyme more suitable for biosensing applications. In Chapter 3, an electrochemical Cu (II) sensor is presented utilising the Cu (II) specific DNAzyme immobilised on the surface of a gold electrode. A self-polymerising PDA coating was employed to entrap both the DNAzyme and gold nanoparticles, resulting in a Cu (II) sensor with a low limit of detection (180 nM) and a linear range of 1 – 100 μM. This represents a significant step towards low-cost, remote environmental monitoring. Notably, the sensor can be fabricated in just 2 hours, delivers results in 15 minutes, and exhibits an excellent shelf life with a modest decay in signal over 2 months of storage under ambient conditions. Chapter 4 explores the use of an electrohydrodynamic (EHD) inkjet printer capable of depositing femtolitre volumes for the preparation of DNA microarrays. While such printers have been utilised for creating biomolecule patterns on various surfaces, few studies have investigated their impact on current DNA microarray functionalisation techniques that should render single stranded DNA covalently bound to a surface. Work presented here reveals that the femtolitre inkjet printing strategy is incompatible with the widely used silane (3-Glycidyloxypropyl)trimethoxysilane (GOPTS) surface functionalisation chemistry on both conductive (Silicon Dioxide) and non-conductive (Indium Tin Oxide) surfaces. Aside from being a lengthy protocol (over 24 hours), binding of EHD printed DNA is demonstrated to be largely mediated by non-specific adsorption due to unavoidable drying of the droplets of DNA ink. In addition, the requirement of surface blocking, wherein the remaining functional surface is rendered unreactive, is demonstrated to be required to prevent non-specific binding of the target DNA upon hybridisation. To overcome these challenges, Chapter 5 explores the creation of polymethyl methacrylate (PMMA) - azide microarrays, employing copper mediated click chemistry for the selective grafting of alkyne-modified ssDNA. This strategy results in covalent binding of DNA in 10 minutes, hybridisation with its complementary target in 2 hours, requires no blocking of the remaining surface, and permits substrate reuse, offering substantial advantages over conventional silane methods. In summary, this thesis is aimed at advancing biosensor manufacturing through the introduction of novel, easy-to-perform and useful DNA immobilisation strategies. The exploration of a Cu (II)-specific DNAzyme, enhanced by PDA and gold, has led to the development of a sensitive and rapid electrochemical Cu (II) sensor with remarkable attributes. Furthermore, the systematic investigation of an EHD inkjet printer for DNA microarray fabrication provides a comprehensive overview of the conventional techniques in microarray design, highlighting crucial factors in sensor design. The findings offer valuable insights and serve as a reference document, aiding researchers in conducting similar work and furthering the progress in this specialised domain. Finally, the development of a PMMA-azide ink and DNA attachment through click chemistry offers a valuable alternative protocol for the next generation of biosensing technologies utilising EHD printers.
Embargo
Harnessing Deep Learning with Protein Language Models to Unveil Microbial Enzyme Function in Health and Disease
Thurimella, Kiran
In microbial genomics, accurate annotations of the biological functions of enzymes are critical, as these proteins have important roles in catalysing essential biochemical reactions with high specificity and efficiency. Historically, functional annotation tools have relied on hidden Markov models (HMMs) that are built by aligning many amino acid sequences or using sequence homology tools like BLAST, which employs a pairwise alignment strategy between query and target sequences. Advancements in deep learning have significantly aided the functional annotation of proteins and comprehension of their diverse functions. Protein language models (pLMs), such as those used for structural prediction and other tasks, demonstrate remarkable capabilities in decoding the intricate amino acid language of proteins, which facilitates their functional annotation through a distinct approach compared to sequence-based alignment methods. In this body of work, I delve into the application and refinement of deep learning and classical machine learning techniques to unveil novel microbial enzymes with implications for health and disease. In the second chapter I combined pLMs with structural tools to create a new homology search system for annotating uncharacterized microbial proteins in IBD. I design several benchmarks for the evaluation of performing nearest neighbour searches to find protein homologs using the pLM embeddings. I demonstrate that the system can integrate protein structure when computing homology searches. My system uncovered potentially important enzymes in the IBD microbiome, including plsA, involved in plasmalogen biosynthesis. Additionally, I identified a remote homolog of McbB, a Pictet-Spengler enzyme, using structural search. McbB is hypothesised to synthesise ligands for GPR35, a receptor genetically linked to IBD. These discoveries, facilitated by deep learning tools, could offer new insights into IBD pathology and lead to novel therapeutic targets. In the third chapter I detail the development of CAZyLingua, the first tool that harnesses transfer learning from pLM embeddings to build a deep learning framework that facilitates the annotation of Carbohydrate Active Enzymes (CAZymes) in metagenomic datasets. I applied CAZyLingua to a paired mother/infant longitudinal dataset and revealed unannotated CAZymes linked to microbiome development during infancy. When applied to metagenomic datasets derived from patients affected by fibrosis-prone diseases such as Crohn’s disease and IgG4-related disease, CAZyLingua uncovered CAZymes associated with disease and healthy states. A CAZyme abundant in Crohn’s disease that CAZyLingua predicted to be a carbohydrate esterase was experimentally validated by demonstrating catalytic activity against acetylated manno-oligosaccharides. In the final chapter I describe connections between the gut and oral microbiomes to allergies. In particular, serine proteases (SPs) are emerging as potential allergens. This chapter uses deep learning and pre-trained pLMs to find allergenic SPs in metagenomic data. First, I develop a model to identify the key catalytic serine residue in serine hydrolases, showing how pLMs capture catalytic residues. Then, I build a deep learning framework to detect SP allergens across entire gene catalogues, using the structurally conserved catalytic triad to find potential homologs in gut and oral sites despite low sequence identity. The model predicts a putative SP allergen like a V8 protease, a known protease activated receptor-1 trigger. Notably, the model generalises beyond trained SP examples to predict a cysteine protease allergen resembling the Der f 1 dust mite allergen. This framework reveals allergens beyond those found by traditional sequence similarity, offering new targets for allergy research. This work represents a significant step towards redefining understanding of how microbial enzymes impact human health and disease. By integrating advanced deep learning, particularly pLMs, into existing functional annotation pipelines, previously hidden potential of enzymes within vast metagenomic datasets can be unlocked. This paves the way for the discovery of novel microbial enzyme functions associated with diverse health states, from IBD and chronic inflammatory diseases, to allergic responses and finally healthy physiological growth.
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CD82 as a novel biomarker of senescence and its theragnostic potential
González López, Cristina
[Restricted]
Indefinitely restricted
Synthesis, Characterisation, and Applications of Phase Engineered Molybdenum Disulfide
Sami, Ismail
[Restricted]
Open Access
A dynamic knowledge graph approach to creating interoperability in smart cities: Selected case studies towards holistic flood impact assessments and district heating operations
Hofmeister, Markus; Hofmeister, Markus [0000-0002-5154-2550]
Modern cities leverage information and communication technology to enhance residents' quality of life, thereby evolving into increasingly ‘smart’ entities. Advancing digitalisation generates extensive data with the potential to address pressing environmental and social challenges, boost innovation, and provide decision support, from strategic planning to day-to-day operations. Yet, the seamless integration of ever-increasing amounts of information in heterogeneous formats and types remains challenging, leading to a landscape of fragmented solutions and data silos. This thesis proposes a solution to these problems based on a dynamic knowledge graph and demonstrates its effectiveness using two interdisciplinary case studies - the assessment of potential flood impacts with regard to the population and built environment at risk and the resource-optimised operation of a district heating network. Specifically, a dynamic knowledge graph approach is investigated to align the representation of data and models using Semantic Web technologies, enabling the connection of siloed data sources and overcoming limited automation opportunities in non-interoperable tools and smart city applications. New domain ontologies are developed to capture relevant concepts and their dependencies, and to link related information. Unlike conventional knowledge graphs, the proposed approach includes semantic descriptions of software capabilities and embeds corresponding agents as an integral part of the graph to carry out computations, rendering it inherently dynamic. The semantically connected ecosystem of knowledge, data, and computational capabilities supports graph-native provenance and dependency tracking, which ensures that instantiated updates are automatically cascaded to provide dynamic up-to-date insights at all times. A set of linked software agents is developed to continually instantiate publicly available near real-time data for a more holistic perspective on flood risk in the UK. The implications of newly raised flood warnings are directly assessed in terms of the number of people and buildings as well as total property value at risk, enabling continuously updated impact assessments as flood hazards evolve. In a second example, the potential for cross-domain automation is highlighted by integrating airborne emission dispersion modelling with the dynamic control of a district heating network. An agent-based implementation is developed to forecast the anticipated heat demand, minimise associated total generation cost, and couple it with dispersion modelling of corresponding emissions to provide insights into air quality implications of various heat sourcing strategies. The effectiveness of the approach is demonstrated based on actual historical operations data from an existing heating network of a midsize town in Germany, identifying reduction potentials of around 20% in operating costs and 40% in CO₂ emissions compared to baseline data.
Embargo
Material selection and manufacture for a polymer heart valve application
Patel, Ruhi
This thesis reports on the material selection, manufacture and testing of a polymer heart valve made entirely from block copolymer. Selection based on Ashby diagrams pointed towards styrene-based and polyurethane-based block copolymers as candidate materials for study. Styrene-based block copolymers had been well studied in their fatigue behaviour, but more insight was required into their biostability. On the other hand, polyurethane-based block copolymers are reputable for their resistance to in vivo degradation, but further study was required to understand their performance in the application. To form a holistic comparison between these materials, both their biostability and fatigue were studied. All materials were characterised for their structural, thermal and mechanical properties before and after accelerated ageing by oxidation and hydrolysis. Thermal ageing in air was also studied. Collection of this qualitative and quantitative information builds a useful database of results from which to develop an understanding of the nature of degradation in these polymers. Variations in results for ageing in different environments sheds light on the importance of selecting suitable ageing environments to simulate in vivo degradation in polymers. Studies in this thesis present the unique potential of polyisobutylene-based polyurethane for the polymer heart valve application, with excellent performance in biostability experiments and fatigue. As part of these studies, a compression moulding technique was developed to make the first prototype heart valves from this polymer. Performance testing on rapid-failure rigid post heart valve prototypes made from polyisobutylene-based polyurethane, along with a styrene-based block copolymer and Elast-Eon, a well-known commercial biomaterial, have shed light on its outstanding durability, placing it as a top contender in this material selection study. Following these findings, experimental fatigue data was applied alongside a finite element model of a polyisobutylene-based polyurethane heart valve to create a lifetime prediction curve for rigid post heart valve prototypes. With the clinical need for a prosthetic heart valve that is both effectively durable and biocompatible, the performance of BCPs reported in this thesis illuminate the path towards a polymer-based solution, highlighting the promise of polyurethane-based block copolymers and identifying factors influencing polymer degradation to provide a framework for the assessment of new candidate materials to be developed.
Embargo
From local and bulk structure-flow correlations to macro-scale transport properties with MRI: applications using deformable porous media and deep learning
Avrantinis, Nikolaos
This thesis investigates the relationship between structure and flow in complex porous media using nuclear magnetic resonance (NMR)/magnetic resonance imaging (MRI) and machine learning (ML) techniques. The evolving interactions between the solid framework and the local flow fields of deformable model porous systems were probed by both spatially and non-spatially resolved NMR methods and linked to bulk transport properties such as permeability and dispersion. Three-dimensional (3D) spatially resolved flow MRI measurements combined with machine learning techniques, namely a feedforward artificial neural network (ANN), were employed to predict the absolute permeability of 21 rock samples of various lithology. To investigate structure-flow correlations in deformable model porous media, a custom-made bead pack with a moving piston was developed to allow loading and unloading of the constituent deformable particles. 3D MRI velocimetry was used to probe the evolving local hydrodynamics during loading conditions of a system composed of 8 mm diameter silicone spheres. After high compression stress a redistribution of flow was observed, with the fluid being distributed more evenly among the dominant flow channels. This phenomenon was linked to the slower rate of permeability decline at high stresses compared to lower stresses, which is often observed in rock samples. Permeability hysteresis and its relationships with irreversible structural and local transport phenomena were investigated. These studies were performed during unloading conditions of a previously compressed system using 3D spatially resolved MRI velocity measurements. It was argued that the degree to which fluid is distributed evenly between the dominant flow channels affects the evolution of permeability hysteresis. The evolution of bulk transport and structural properties was investigated during loading of a deformable model porous system (composed of 1.26 mm diameter expanded polystyrene spheres) by conducting non-spatially resolved NMR propagator experiments. It was found that dispersion increased with decreasing permeability and that it almost doubled with strain due to long-range heterogeneities. Macro-scale heterogeneities introduced by differential compaction were examined using one-dimensional (1D) spatially resolved MRI propagator measurements. It was also shown that strain intensifies the power law dependence of dispersion on Péclet number. Further, NMR flow diffraction phenomena revealed the evolution of structural properties with strain. A novel technique combining 3D spatially resolved MRI propagator measurements and a feedforward ANN was used as a means for predicting the absolute permeability of 21 rock samples of various lithology. The first three moments of each per-voxel propagator were utilised for training the ANN as to develop associations between the training data and the corresponding permeability values. A correlation coefficient (R) of 0.943 between the predicted and the actual permeability values, was achieved, demonstrating the link between the spatially resolved propagator measurement and absolute permeability.
Embargo
Developing NMR/MRI Techniques for Characterisation of Reactive Transport in Porous Media
Fraboni, Francesco
Reactive flow in porous media is a complex transport phenomenon which involves the simultaneous flow and reaction of a fluid in a porous medium and plays a critical role in improving our understanding of carbon sequestration. When CO₂ is introduced into a storage site, which might be a hosting rock formation, high temperatures and pressures result in the production of a supercritical phase, which can then react with the existing brine to generate carbonic acid. This process is defined as reactive flow and may lead to both dissolution and/or precipitation. Major effort has been invested in researching the former since it may induce significant structural changes in the formation and has previously been examined for acid stimulation treatments in the oil and gas industry. Various dissolving regimes may be encountered depending on the reservoir characteristics and the physical conditions of the hosted fluid, ranging from uniform to *wormholing*, which is characterised by the formation of dissolution channels where the majority of the fluid transfers to. X-ray microtomography is widely adopted to study this kind of systems, since it can provide information about the rock structure resolved at the micron-scale. This is usually coupled with numerical simulations in order to predict the behaviour of the reactive fluid. Nuclear Magnetic Resonance (NMR) methods are a powerful tool for characterising reactive flow because, in addition to imaging, they can give a range of information such as transport characteristics and pore-size distributions. This work attempts to characterise dissolution in a reactive flow system using NMR techniques, which is accomplished in a variety of ways. First an innovative experimental protocol is defined to dynamically study the dissolution of carbonate rocks of various heterogeneities at the core-scale, allowing for the detection of major structural changes as well as temporally-resolved direct measurements of the system transport properties via the use of spatially resolved propagators. This was accomplished through optimisation of Compressed Sensing methods, a set of image reconstruction algorithms that allow for faster acquisition times using under-sampling strategies. Second, this work presents the development of novel experimental techniques based on PFG-NMR to directly measure reactive flow system’s flow field, especially in dominant wormhole regimes which are challenging to study due to extremely heterogeneous range of velocities experienced. These approaches were used to demonstrate flow mechanisms that were not previously experimentally verified and to provide experimental validation on reactive flow modelling. Moreover, it has been possible to study transport properties in the form of velocity maps and propagators at different dissolution regimes as well as at different scales with the acquisition, for the first time, of high-resolution co-registered MRI/µ/CT flow maps. Structure-flow correlations were used in particular to quantify the primary changes in the flow field during the dissolution process, and both Ketton and Estaillades experienced a focusing of velocities into high-flowing channels with subsequent stagnation of the other regions. Finally, the developed NMR/MRI techniques are used to study reactive flow in realistic systems. This is achieved by applying chemical selectivity to assess transport properties of multiphase reactive flow systems and by working towards the development of a rig to study the behaviour of CO₂ flowing through rock core plugs.
Open Access
Multiscale modelling of CO₂ Electroreduction on Cu - based gas diffusion electrodes (GDEs)
Adesina, Oluwatomi Peace
The work presented in this thesis addresses the challenges of upscaling microscopic models such as microkinetic model to mesoscopic models (mass transport models) for CO₂ reduction reaction (CO₂RR). It also bridges the gap between mass transport model and molecular modelling through a multiscale modelling approach of the discussed electrochemical system. CO₂RR is a promising carbon utilisation technology which converts or recycles CO₂ into value-added hydrocarbons and fuels, powered by water and renewable energy (solar and wind). Gas diffusion electrodes (GDEs) are an important component of the electrochemical cell employed to conduct this reaction. Compared to other electrocatalysts used for CO₂RR, copper electrocatalysts have the advantage of producing several products in a single electrolytic process. CO₂RR on Cu electrodes is a very complex reaction for which the entirety of the mechanistic reaction pathways to achievable products on the electrodes are still being studied till date. This is exacerbated by two parasitic reactions (hydrogen evolution reaction and homogeneous bicarbonate reaction) which compete and inhibit the high current density of valuable products like ethylene, ethanol, propanol etc. Hydrogen gas will naturally evolve in a water rich environment requiring only two electron transfers. On the other hand, bicarbonate reaction will be favoured and compete with important products like ethylene since they both thrive in a high pH regime. The favorability of homogeneous bicarbonate reaction over CO₂RR is as a result of the non-requirement of any electron transfer for the same reaction. This makes them susceptible to occurring at any point in the system as long as CO₂ and participating ions like OH¯ ions are readily available. Therefore, conducting the CO₂RR on GDEs requires accurately capturing species mass transport and kinetic profiles of the participating species which would enable the optimisation of CO₂RR on the GDE assembly. The outcome of the thesis is in two portions: One is the upscaling of microkinetic model that carries the kinetic signature of CO₂RR on a well-studied Cu facet: Cu(100), to a GDE mass transport model. Here, it was found that due to the complexity of the reaction and multiple kinetic regions observed, the kinetic profile defined by the Butler-Volmer’s exchange current density (i₀) and transfer coefficient (α) is inadequate in expressing the true kinetics of CO₂RR. A new methodology for directly integrating icrokinetic model to mass transport model is developed by employing a well-calibrated microkinetic model of the reaction on Cu(100) facets. This eliminates the oversimplification of CO₂RR kinetics. The impact of local pH, CO₂ concentration and applied voltage on product selectivity can be observed as documented in experimental work in literature. This work also reveals certain limitations with existing experimental work on CO₂RR that warrant exploration in future mechanistic studies, particularly as the move towards commercial applications of CO₂RR gains momentum. The microkinetic model also proves to carry richer information of the reaction kinetics which when combined with mass transport models will lead to better understanding of the reaction microenvironment and serve as an important step to optimising GDE performance for commercial applications. In the second part of the thesis, Cu catalyst-ionomer interaction is investigated. Nafion®, a perfluorosulfonate ionomer, has been proposed in literature, as a shielding material against homogeneous bicarbonates reaction. Due to the residing negative sulfonate ions which deprotonates in water, Nafion® is able to repel the negative bicarbonate ion, thus tuning the reaction towards CO₂RR. This appears to be a potential resolution for addressing bicarbonate reactions and their competition with CO₂RR particularly in the presence of abundant CO₂. This hypothesis is tested by modelling a one dimensional planar electrode with a Nafion® layer and conducting DFT calculations on the molecular structure of Nafion®. Important parameters such as the thickness of Nafion® layer in the thick film regime (200 – 1100 nm), the ionic strength of supporting aqueous electrolyte and the applied voltage were investigated to model the concentration profile of participating species within the Nafion® layer. It was observed that the bicarbonate ion concentration and local pH decrease with decreasing thickness which makes thinner films better at mitigating bicarbonates at the cost of a relatively lower pH. There is also a breakdown of Donnan exclusion effects with higher ionic strength electrolytes which allows diffusion of both co-ions and counter-ions, promoting the bicarbonate reactions. Further, an important relationship between the pKₐ of Nafion® and the local dielectric constant was found. It was observed that the pKₐ of Nafion® generally increases as the local dielectric constant of the reaction medium decreases. This is primarily due to the accumulation of K⁺ and production of low dielectric constant products such as ethanol, albeit their concentrations are low on planar Cu electrodes. Lastly, the lower permittivity of the reaction medium weakens the exclusion forces and changes the morphology of Nafion® which ultimately impacts its ability to mitigate homogeneous bicarbonate reactions. The findings from this study indicate that employing Nafion as a protective barrier to deter homogeneous bicarbonate formation is a provisional remedy, emphasising the need for a more robust/resilient solution. Perhaps, the solution proposed earlier in literature that involves a tandem GDE arrangements for which one GDE reduces CO₂ to CO using Ag electrocatalysts and the other GDE, comprising Cu electrocatalysts, further reduces the produced CO to hydrocarbons is still a better option at the expense of higher energy requirement from running two separate electrochemical cells. This approach mitigates the bicarbonate curse and address the intricate challenge of tuning the reaction microenvironment to favour desired products and mitigate the bicarbonate curse.
Embargo
Energy recovery from urine
Asiain Mira, Ruben
Removal of nitrogen compounds in wastewater represents more than 10% of the total electrical demand of the integral water cycle. However, more than 80% of the nitrogen in wastewater comes from urine, where it is highly concentrated in the form of urea (20000 mg L-1). Urea contains a significant amount of hydrogen in its structure which, if recovered, makes urea a potential source of green energy. This thesis demonstrates a novel approach for the energy recovery from urea present in urine at the production source, using decentralised wastewater treatment systems. A new process has been developed in this thesis based on the integration of three steps. In the first step, adsorption is used to recover urea from urea, overcoming the energy limitations of thermal treatments applied to big water volumes. In the second step, thermal treatment is used to desorb the urea, achieving the regeneration of the adsorbent and the production of ammonia. Finally, in the third step, ammonia is used as hydrogen storage molecule to catalytically produce hydrogen on demand. The adsorption of urea is evaluated using activated carbon, determining that urea adsorbs due to physical interactions with i) delocalised π electrons of the pristine surface of the carbon and ii) carboxyl functional groups. The adsorption of urea is reduced when working with real urine due to the presence of organic compounds with affinity for activated carbon that interferes with the adsorption of urea. Thermal treatment of adsorbed urea leads to desorption of urea and regeneration of activated carbon showing a stable urea adsorption capacity during 4 consecutive adsorption/desorption cycles. Simultaneously, ammonia is produced with a 50 – 60 % yield, which is coupled with an ammonia decomposition catalyst to obtain hydrogen. Pilot trials are developed and installed in relevant environments as conventional and waterless urinals, where a social analysis shows a good acceptance towards the solution and pointed some aspects for improving. Energy analysis shows a positive balance due to the combination of the hydrogen produced and the savings in the traditional nitrogen removal. Furthermore, economic analysis indicates that the direct use of ammonia to produce electricity or fertilisers can be a competitive alternative to the obtention of hydrogen.
Embargo
Development of adsorbent materials for phosphate recovery
Avena Maia, Marina
Phosphate, one of the primary components in fertilizers, is a finite resource in current scarcity, mainly obtained by the mining of phosphate rocks. In 2014, this mineral was declared as one of the 34 critical resources in the European Union. Phosphate rocks are geographically concentrated but needed for the growth of plants and mammals. The latter ones excrete phosphate in urine, with a concentration between 260 and 900 mg/L. Current wastewater treatment plants receive a wide range of stream sources, with a phosphate inlet concentration up to only 20 mg/L. Current treatments focus on the removal of phosphate and not its recovery to avoid eutrophication, complying with standard discharge regulations. Removal of phosphate through chemical precipitation in plants consumes 49 MJ/Kg P. This thesis demonstrates a novel approach for the recovery and reuse of phosphate, focused on its extraction from decentralized wastewater systems (i.e. no-mix toilets) with recovery at the production point. For the application of this approach, an integrated process is developed focused on the synthesis of adsorbent materials for phosphate selective adsorption, its recovery through multiple adsorption and desorption cycles, followed by its reuse for fertilizer production. Overall, this thesis demonstrates the efficient recovery and reusability of phosphate from aqueous solutions with high phosphorous initial contents (PO₄-P) to simulate the concentration of streams from decentralized wastewater systems and from urine streams. This work evaluates the synthesis of ZnO-based and layered metal hydroxide adsorbent materials for phosphate recovery. Layered metal hydroxides are analysed due to their metal composition tunability and positively-charged surfaces. In addition, a novel adsorbent synthesis approach was evaluated through the synthesis of ZnO-composite materials via atomic layer deposition. This synthesis technique produced ZnO clusters on the substrate’s surface, increasing the atom-efficiency in the adsorption process. Furthermore, through the adsorption mechanism investigation, it was observed that the presence of metallic species in the phosphate adsorbent materials increased the overall positive surface charge, resulting in the enhancement of the adsorption capacity via electrostatic attraction. The phosphate species were also adsorbed through chemisorption via surface complexation. Lastly, as a proof-of-concept investigation, this phosphate recovery approach was also successfully implemented using synthetic urine and human urine streams, where phosphate was removed not only by adsorption but also by chemical precipitation.
Open Access
An investigation of heat stable milk chocolate by addition of glycerol
Holian, Jennifer
Glycerol added to milk chocolate (made via the crumb method), in small amounts, can create a heat stable chocolate. Milk chocolate (MC) is a dense suspension consisting of cocoa solids, sugar and milk powder dispersed in a continuous fat phase of cocoa butter and milk fat. Heat stable milk chocolate (HSMC) is chocolate that does not flow at temperatures between 35-40 °C, when traditional chocolates do. This thesis aims to determine the mechanism of heat stability in solid milk chocolate as afforded by the inclusion of small amounts of glycerol. Chocolate sales in warmer climates is growing exponentially. HSMC is required by these consumers to combat negative effects of premature melting such as “bloom” and misshapen bars that are too soft to handle. When glycerol was added to chocolate it did not completely lose its hardness (i.e. soften) at high temperatures. From indentation experiments, a linear relationship existed between bulk yield stress and the square of the volume of glycerol present in the chocolates. Adding glycerol to chocolate increased its viscosity, which raises the risk of an increase in difficulty pumping and mixing in a factory setting. As determined by DSC and XRD experiments, addition of glycerol did not affect melting or crystallisation properties of the fat phase. The fat phase in HSMC melted over the same temperature range as MC. Tempering chocolate remained the biggest influence on crystal structure of the fat phase, and subsequently its melting temperature range. Adding glycerol before or after tempering did not change this. NMR tests showed that no chemical reactions occurred when glycerol was mixed with the fat blend. This was further demonstrated when HSMC continued to exhibit “bloom” after cooling from melted states, as expected with MC. This is an undesirable effect for the consumer. The reason for induced heat stability was not related to changing the fat phase to remain solid at higher temperatures. The fat in HSMC was melting but the chocolate was not flowing. This indicated there was a structure holding the fat phase in place. By removing fat from HSMC by submerging in hexane, it was confirmed that addition of glycerol to chocolate formed a cage-like structure of solids linked together throughout the entire system. HSMC retains the same heat stability when no lecithin is present. This indicates an interaction between the glycerol and lecithin is not needed to cause heat stability (it remains essential for favourable flow properties). Therefore, glycerol interacting purely with the surface of crumb must be the root cause of the cage structure and heat stability in chocolate. Glycerol did not prefer to wet surfaces of ingredients in chocolate when it was surrounded by the fat blend. However, it weakly wetted sugar surfaces. Through indentation testing on chocolates made with different proportions of sugar, crumb and cocoa powder, it was found that sugar played an important role in the interaction with glycerol. When very small amounts (<0.5%) of glycerol were added, severe agglomeration was seen in chocolates with only sugar solids. Glycerol’s interaction with sugar is the reason for the cage-like structure that imparts heat stability to the chocolate. Glycerol HSMC needs to have sugar in the recipe, so it won’t be applicable for calorie reduced, diabetic chocolates where sugar has been replaced with bulk polyols. Overall, sugar has a large role to play in this cage being formed and deserves further research to confirm if this is due to sugars becoming ‘sticky’ and agglomerating or capillary force induced liquid bridges. The structure of this thesis is as follows: chapter 1 introduces chocolate and its manufacture, chapter 2 reviews relevant academic literature and patents and discusses the current state of knowledge of mechanisms for HSMCs. Four chapters then follow containing experimental results and discussion. The first of these presents physical properties of HSMC and compares them with MC. Three chapters then investigate possible mechanisms for observed heat stability in relation to glycerol’s effect on the ingredients in chocolate: fat blend (chapter 4), lecithin (chapter 5) and solids (chapter 6). The last chapter will discuss the projects conclusions and findings that are relevant to heat stable chocolate manufacture on an industrial scale.
Open Access
On-demand production of deoxynucleotides from genomic DNA
Bird, Anna; Bird, Anna [0000-0003-0354-8824]
There is a burgeoning of use of nucleic acid amplification testing (NAAT) diagnostics and a need for their associated reagents, especially in less resourced environments. NAATs are highly sensitive and specific assays, applicable in the detection of infectious and many noncommunicable diseases, and thus are crucial for disease management globally. The high cost of this technology, relative to traditional immunoassays and culture-based methods, has slowed the adoption of NAATs in low- and middle-income countries. This thesis explores a novel synthesis method to produce a key NAAT reagent, namely the 2'-deoxynucleoside 5'-triphosphate (dNTPs) building blocks of DNA, in a manner that can be integrated into a NAAT workflow. A unique top-down approach to produce dNTP monomers from a genomic DNA polymer is investigated, contrary to traditional bottom-up synthesis methods, which rely on inaccessible precursor molecules. Readily available and renewable bacterial genomic DNA provides a crude starting material to generate monophosphorylated deoxynucleotides (dNMPs) by the digestion of DNA with nuclease enzymes. The dNMPs are then phosphorylated to dNTPs by a set of in-house recombinantly produced kinase enzymes, and the produced dNTPs directly applied in NAAT, without the need for additional purification steps. In-house synthesised dNTPs were able to amplify PCR fragments up to 7.5 kb in length and showed successful specific detection of *Plasmodium malariae* and *Plasmodium ovale* malaria target genes, demonstrating its potential utility for diagnostics applications. Biocatalysis offers many advantages over chemical synthesis, but most importantly these biocatalysts can easily be regenerated in low-resource settings, building on the previous work for local enzyme production by the Cambridge Analytical Biotechnology Lab. Furthermore, the biocatalysts can be immobilised to silica particles and implemented in a reusable bioreactor device. The silica immobilised kinase enzymes showed reusability up to twelve times over two months, when stored at 4⁰C. Overall, this work has demonstrated a pathway to generate deoxynucleotides from genomic DNA, and investigated an immobilised enzyme milli-bioreactor as a proof-of-principle for how the technology can be translated into practice and integrated with NAAT in low-resource research and diagnostics labs.
Embargo
Enhancing Special Core Analysis Insights into Wettability through the Application of Magnetic Resonance
Smith, Sean
Energy demand is determined from a balance of affordability, sustainability and security. The oil and gas exploration and production industry has shifted focus to try and optimise production from existing hydrocarbon fields without substantially increasing emissions. Special core analysis aims to aid optimisation of improved/enhanced oil recovery (IOR/EOR) fluids to increase hydrocarbon production from existing fields by altering the wettability of the system. Wettability is the preference of a fluid to adhere to the surface of a rock core whilst in the presence of another immiscible fluid and can be quantified through special core analysis. This thesis aims to increase understanding of special core analysis protocol through the development and application of magnetic resonance (MR) methods and apply them to an IOR/EOR wettability altering corefloods. This was achieved by developing low-field techniques including the constant gradient *z*-*T*₁-*T*₂ and *z*-D-*T*₂ and high-field 3D imaging of *T*₁ and *T*₂. The new methods were applied to a series of corefloods to ensure that they were quantitative. The previously established 2D *T*₁-*T*₂ correlation was quantitively benchmarked to the industry standard wettability measurement, the Amott-Harvey test. The first *in situ* monitoring of the ageing process was also conducted on crude oil samples. The findings indicate that the newly developed methods aligned quantitatively with the volumes produced that were determined through both existing MR techniques and gravimetric measurements obtained during corefloods. In addition, the methods provided increased data on systems through spatially-resolved *T*₁/*T*₂ (a measure of the strength of surface interactions) and spatially-resolved diffusion on the 2 MHz spectrometer when relaxation data did not give sufficient phase-separation. Benchmarking the 2D *T*₁-*T*₂ to the Amott-Harvey experiment for neutral- and oil-wet cores gave strong results and showed there was a correlation with the resultant wettability indices – for the oil-wet sample, oil *T*₁/*T*₂ was higher throughout and for the neutral-wet sample they were similar. For the crude oil experiments, clear evidence of common mechanisms occurring to all the rock types and fluids were seen, with macroscopic displacement dominating the early phase of the primary drainage experiments before microscopic displacement as irreducible water saturation is reached. During low-salinity IOR flooding, MR measurements provided evidence for wettability alteration without having to conduct a full Amott-Harvey experiment. This thesis has furthered core analysis protocol and understanding through the development of new techniques and applying them quantitively to complex corefloods to increase understanding of multiphase displacement mechanisms.
Open Access
Enzyme-Linked Electrochemical Detection of Urinary Sarcosine
McCarthy, John
Biomarker-based diagnostics are crucial for the early detection of prostate cancer (PCa). One such biomarker that may be useful in PCa diagnostics is urinary sarcosine (N-methylglycine). However, its utility is confounded by several factors: the small differences in metabolite concentration that separate healthy (~ 0.2 µM) and sick (~ 5.0 µM) patient populations, the difficulties associated with detection of these low concentrations in complex matrix (i.e. urine), and the cumbersome methods that have typically been used for detection (i.e. HPLC). To address these problems, this thesis sought to develop a first-generation enzyme-linked electrochemical biosensor (EnzL-ECBS) to detect sarcosine at clinically relevant concentrations for PCa diagnosis in human urine. Combining a silica-immobilised chimeric monomeric sarcosine oxidase (mSOx) biorecognition element with a static platinum disc macroelectrode transducer created an easy-to-use, low-cost alternative for chronocoulometric sarcosine detection in static samples of PBS and artificial urine matrix (AUM). However, initial EnzL-ECBS designs suffered severe sensitivity limitations. Characterisation studies demonstrated that sarcosine detection was obstructed by the impact of several AUM components (uric, citric, lactic acids and CaCl₂) on biochemical and electrochemical transduction, along with the acidic pH (which could be induced or bolstered by AUM acids) and insensitivity of the initial macroelectrode design towards static H₂O₂ samples. Attempts to engineer kinetically enhanced biorecognition elements from different mSOx and glycine oxidase constructs failed to generate viable alternatives. However, kinetic enhancements to the initial mSOx-bound silica were artificially imbued by increasing the concentration and duration of its exposure to the sarcosine-containing sample. Exchanging the initial static macroelectrode cell design with either static microelectrodes or dynamic flow cell designs provided significant improvements to peroxide sensitivity in both PBS and AUM. Combining sample pretreatment methods, such as pH titration to pH ~ 8 and exposure to Amberlite™ exchange resins bolstered sarcosine detection by enhancing sarcosine transduction while simultaneously reducing the signature of background interferents in a statistically significant manner. These actions generated sensitivity to sarcosine at concentrations as low as 0.25 µM in PBS and AUM, aided in large part by a ~ 71% reduction in electrochemical interference of pretreated AUM samples compared to their untreated counterparts. While further research is needed, this work establishes a new platform for the detection of sarcosine at concentrations considered clinically relevant for prostate cancer detection in both PBS and AUM.
Open Access
Algorithmic Approaches for Context-Informed Reaction Prediction
Wigh, Daniel; Wigh, Daniel [0000-0002-0494-643X]
Understanding the chemical reactivity of small organic molecules is one of the key challenges in chemistry and can unlock unimaginable value in the pharmaceutical industry and beyond by accelerating molecular synthesis. Computational approaches are becoming increasingly important, and an ecosystem of tools for understanding chemical reactions is under development with the ultimate aim of accelerating lab-based workflows. This thesis makes contributions across this ecosystem of computational tools, including knowledge representation, data preparation, and model development. The first chapter discusses the representation of molecules in detail, laying the foundation for all the following chapters. The discussion of data representation continues with a showcase of how the Unified Data Model (UDM) can be used as a language for closed-loop optimisation. UDM is a standard for storage of chemical data, much like the Open Reaction Database (ORD). The ORD contains millions of reactions, and to prepare machine learning datasets from these reactions, I present the Python package ORDerly. Datasets generated by ORDerly were used in a subsequent chapter to experiment with novel model architectures exploiting the hierarchical nature of chemical reaction classes. The final two chapters discuss how a deeper understanding of the mechanistic details of a reaction can accelerate optimisation using multi-task Bayesian optimisation and make out-of-sample predictions on the rate protodeboronation, the primary degradation pathway of boronic acids. This thesis touches on numerous topics, all with the ultimate goal of developing computational tools to accelerate synthesis.
Embargo
Active Learning-Closed-Loop Optimisation for Organic Chemistry and Formulations Research
Pomberger, Alexander; Pomberger, Alexander [0000-0003-2267-7090]
The discipline of chemistry emerged in the 18th century and ever since then has been a major driver of todayʼs technologies, leading to associated comfort and resilience of the human species. Organic chemistry particularly aims to deliver tailored molecules that fulfil tasks for various purposes such as medication to treat diseases, agricultural chemicals to ensure sufficient nutrition or monomers to manufacture materials. Finding optimal methods and pathways toward target molecules is a task that has been intuition-guided for years, requiring expert chemists to select chemical process parameters based on experience. This thesis focuses on strategies to automate chemical reactions and conditions optimisation by employing active learning-driven closed-loop optimisation and merging algorithmic efforts with robotic technologies. First, I explored the effect of different machine-readable representations on closed- loop optimisation performance for an organic reaction towards a small molecule. Chemical descriptors with increasing information content were calculated ranging from generic OHE to bespoke and expensive DFT-derived descriptors. Moreover, the balance between descriptor complexity and dataset size for initialising optimisation was investigated. Based on the study, complex descriptors did not outperform simple OHE representation and it was shown that larger initial datasets delivered better performance than smaller initial datasets containing highly informative descriptors. Second, I investigated the efficiency of an active learning algorithm for the adjustment of the pH value within aqueous multi-buffered poly-protic buffer systems. Those solutions have tremendous importance in biological systems and are hard to model mathematically. By applying a data-driven optimisation I was able to model the pH response of the system after acid/base addition and control a developed liquid-handling robotic platform capable of conducting the experiments. Eventually, I managed to demonstrate that transfer learning, a method where the model leveraged prior data from a similar task to the target task, could increase efficiency up to 40%. Finally, I aimed to combine the learnings from the first two projects and investigate the feasibility of employing transfer learning to closed-loop chemical reaction optimisation workflows. In detail, an XGB model was trained on source data from the Pistachio online database with the aim to boost performance of a target task which required finding the ideal reaction parameters. While I learned that the weighting of the data as well as the exclusion of the low-quality source data (after a specified number of iterations) matters for the performance of the active learning, the overall benefits so far did not justify the added work effort and require further investigation.
Controlled Access
Computational Techniques for Studying Nanoporous Materials
Rampal, Nakul
The central goal of this thesis has been the development of new computational techniques to accelerate the discovery and characterization of nanoporous materials. Chaper 1 introduces the field of nanoporous materials/reticular chemistry, provides a brief history of molecular simulation in reticular chemistry, and finally, discusses some key challenges that need to be addressed from a computational perspective. Chapter 2 introduces the objectives of this thesis including the organization of this thesis, and some of the important questions this thesis aims to answer. Chapter 3 begins with a detailed discussion on the theory behind GCMC simulations, including the partition function and the different moves in a GCMC simulation and their associated probabilities. Next, the chapter goes into detail of how the potential energy function *U* is calculated, i.e. force fields, including it’s two main contributing terms, (i) the bonded potential, *U_bonded*, and (ii) the nonbonded potential, *U_nonbonded*. Finally, the theory behind the calculation of different geometric properties like the accessible surface area, largest cavity diameter (LCD), and the pore limiting diameter (PLD) are discussed. Chapter 4 introduces our recent advances in HTS to rapidly screen *in silico* the adsorption properties of hundreds of MOFs for CO/N₂ separations. Our approach involves the use of a multi-scale toolbox combining high-throughput molecular simulations, data mining and advanced visualization, as well as process system modeling, backed up by experimental validation. Chapter 5 extends the high-throughput screening approach introduced in the previous chapter to rapidly screen the properties of not hundreds, but thousands of MOFs for H₂ storage. We also discuss how principal component analysis (PCA) can be used to extract meaningful insights from the vast amount of data generated from such screening studies. We validate our screening approach by synthesizing and evaluating the performance of the selected MOF (HKUST-1) in its monolithic form. Chapter 6 begins with an introduction to Small Angle X-ray Scattering (SAXS) and lattice gas models. Next, we introduce the concept of a monolith, and show experimentally the existence of interparticle mesopores - inaccessible from powders - that push final adsorption capacities above levels expected for single crystals. Finally, we show how lattice-gas models in combination with GCMC simulations can be used to accurately capture the monolithic structure across both the microporous and mesoporous range enabling the robust future predictions of the adsorption characteristics of monolithic materials. Chapter 7 begins with a derivation of the BET equation from first principles. We follow this up with a discussion on how the BET equation can be used to calculate the BET area, i.e. BET method, and some state-of-the-art problems with this method. Finally, we introduce an algorithmic approach called BETSI that addresses some of these problems. Chapter 8 summarizes the key results of this thesis and provides some context on the future outlook and challenges in this field.
Embargo
Halide Perovskite and Metal-Organic Framework Composites for X-ray Detection
Salway, Hayden
Our ability to detect X-ray radiation and generate detailed images on internal structures in a non-destructive manner, has had profound impacts on all of our lives. Whether that is through medical imaging, allowing vital diagnosis of tumours and visualisation of broken bones; through security imaging, a vital resource for international security; manufacturing and food processing control, ensuring the food we eat is safe to eat, free from harmful objects; to scientific research, unearthing foundational principles, contributing to the progression of society. X-ray detectors are omnipresent in all our lives, contributing to our safety, health, and the progress of science and technology. To expand the potential of this crucial technology, a new generation of X-ray detector materials is required to overcome current limitations and expand performance beyond that currently achievable. In this thesis, two families of materials with unique properties, halide perovskites and metal-organic frameworks are brought together for the first time, in a sol-gel processable, monolithic manner and utilised to advance the development of novel X-ray detector materials. Utilising the exceptional optoelectronic properties of halide perovskites, and benefitting from the enhanced stability, processability, and potential for functionalisation provided by metal-organic frameworks, a new class of X-ray detector materials are formed. Concurrently, these materials are comprehensively characterised using a suite of structural and photophysical techniques to provide mechanistic insights into their formation and photophysical processes. These crucial insights enabled the refinement and optimisation of synthesis protocols and choice of building-blocks, to enhance stability beyond previously achievable in comparable composites, and develop optimised monolithic perovskite@MOF composites tailored towards X-ray detector applications. Overall, this thesis utilises the unique properties of perovskites and metal-organic frameworks to develop, robust, stable, and scalable X-ray detectors with outstanding promise to overcome limitations of stand-alone perovskites and current detector materials. By synergistically combining two materials, detectors with new multifunctionality are possible. We show monolithic perovskite@MOFs can play a key role in future X-ray detector devices, beyond their encapsulation and stabilisation properties, contributing to the efficient transport of X-ray stimulated charges and limiting ion migration. This work opens an array of applications and contributes to bringing perovskite-based X-ray detectors closer to commercialisation.
Open Access
Development and application of optical microscopy tools for the study of axon guidance
Wunderlich, Lucia; Wunderlich, Lucia [0000-0001-7200-1713]
During their development, neurons extend axons towards their target cells, where they branch to establish connections. The navigation of axons relies on the presence of chemical cues sensed through guidance cue receptors. Upon receptor activation, intracellular signalling pathways are initiated, one of which induces local protein synthesis (LPS), a key process to enable rapid navigational responses to guidance cues. Impairments of axon guidance and LPS are associated with several neurological disorders. In this thesis, state-of-the-art optical microscopy-based tools were developed to improve the efficiency and versatility of commonly used methods for studying axon guidance both *in vitro* and *in vivo*. Furthermore, imaging-based studies were performed on selected guidance cue receptors to investigate their regulation of cue-induced LPS. For the experimental investigations, *Xenopus laevis* retinal ganglion cells (RGCs) were utilised as a model system, enabling comprehensive studies on isolated outgrowing axons both *in vitro* and *in vivo*. Initially, an imaging method was established to examine intricate axonal structures within the highly complex physiological environment. To achieve this, expansion microscopy (ExM) was combined with light sheet fluorescence microscopy (LSFM) to visualise RGC branching *in vivo*. By tracing individual axons, this technique offers a valuable tool for studying cue-dependent arborisation within the brain. Then, ExM and structured illumination microscopy (SIM) were applied in a study aimed at investigating how guidance cue receptors facilitate cue-dependent responses through changes in specific mRNA translation. A mechanism was explored that involves the direct interaction of the guidance cue receptors deleted in colorectal cancer (DCC) and neuropilin-1 (Nrp1) with the translation machinery. This interaction was found to be mediated through RNA binding proteins (RBPs), enabling a receptor-specific mRNA subset to be rapidly and locally translated in response to cue stimulation. Further investigations focused on the cue-induced intracellular transport dynamics of DCC within the endosomal system. During these studies, DCC was observed to colocalise with ribonucleoprotein (RNP) granules on endosomes, suggesting a model in which DCC facilitates the association of RNP granules with endosomes through its affinity to RBPs. Finally, an optical system was developed to enhance the throughput of commonly employed assays for stimulating axons and enabling directed axonal outgrowth *in vitro*. This system employs surface-immobilisation of guidance cues and adhesion proteins. Protocols based on the principle of light-induced molecular adsorption of proteins (LIMAP) were established to guide outgrowing RGC axons towards their physiological target tissue. In summary, this work describes the development of highly sophisticated tools designed to facilitate the study of axon guidance both *in vitro* and *in vivo*. Additionally, valuable insights were gained into the cue-induced mechanisms that initiate LPS through guidance cue receptors. These advancements hold great potential for enhancing our comprehension of axon guidance and its implications for neurological disorders.

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