http://theses.ncl.ac.uk/jspui/handle/10443/73 2026-02-05T10:23:27Z 2026-02-05T10:23:27Z Fsadni, Miriam Helen http://theses.ncl.ac.uk/jspui/handle/10443/6667 2026-01-27T15:59:19Z 2025-01-01T00:00:00Z

Title: Computational and experimental investigations into the factors influencing hole mobility in tuneable small-molecule organic hole transporters Authors: Fsadni, Miriam Helen Abstract: Perovskite solar cells (PSC) are widening the scope of photovoltaics (PVs) to applications beyond the effective capabilities of conventional silicon-based PVs. In these devices the organic hole transport material (HTM) plays a major role in controlling the overall performance and cost of PSCs. Charge recombination at the HTM/perovskite interface remains a challenge, as is the cost of producing standard HTMs, such as spiro-OMeTAD, limiting the viability of these devices. Recently, inexpensive tuneable small-molecule organic HTMs have been developed using condensation chemistry, some of which show promising charge transport properties. A better understanding of the factors governing mobility in these materials could help us move away from the conventional trial-and-error approach and enable us to design HTMs with a higher mobility. In this thesis I explored the properties of small molecule organic HTMs relevant to charge transport. In amorphous (disordered) systems such as these, where charges are localised on energetically discrete sites (molecules), charge transport has been conceptualised as a hopping process which may be described by Miller-Abrahams and Marcus rate equations. Initial quantum mechanics calculations on single molecules revealed that the best charge transport properties were exhibited by HTMs with high dipole moments. This is rather surprising, as correlated energetic disorder has been shown to scale with the dipole moment in amorphous materials and quench mobility. This led us to look further into the effects of the size and ordering of molecular dipoles on mobility using an in-house kinetic Monte Carlo code. While it has been suggested that higher dipole moments might drive favourable self-assembly during film formation and reduce the width of energetic disorder, our simulations show that mobility is rapidly quenched even at low levels of disorder. We find that increasing the dipole moment reduces the number of energetically available hopping sites, resulting in inefficient charge percolation through the film. High levels of global order are unlikely to be achieved in solution processed thin films. However, crystal structures of HTMs reveal closely packed dimers which orient antiferroelectrically in some cases. The presence of these stable supramolecules, with a zero-dipole moment, might reduce the overall energetic disorder in an otherwise disordered film. Molecular dynamics simulations show that our systems based on high performing molecules have a greater proportion of these dimers and in kMC simulations the mobility increases sharply with the population of zero dipole dimers. While these results suggest that it may be beneficial to design molecules with a low dipole moment, polar HTMs may be better at binding to and passivating the perovskite layer, which would increase the power conversion efficiency (PCE) and long-term stability of PSCs. In addition, it has been shown that suitable alignment of dipoles generates a giant surface potential (GSP) across organic semiconductor films, which could be exploited to enhance charge extraction and transport. Based on ii our insights, we set out to control disorder in high dipole HTMs via dimer formation by developing and synthesising a series of HTMs with different H-bonding capabilities. These consisted of both symmetric and asymmetric secondary and tertiary amides, as well as a urea compound. The intermolecular interactions and charge transport properties of these molecules was investigated. Results show that molecules with highly available H-bonding sites readily form H-bonded dimers in the crystal structure and in solution, by proton NMR spectroscopy. These molecules also show favourable charge transport properties when compared to the methylated derivative which is unable to dimerise via intermolecular H-bonding. Our results indicate that it may be possible to use bonding to tune the width of the energetic disorder in high dipole amorphous HTM films. This could be achieved by the formation of short-range ordered domains with quenched dipoles, made up of closely packed favourably oriented monomers, and to a lesser extent through better ordering of dipoles in the film as a whole Description: PhD Thesis

2025-01-01T00:00:00Z Diessner, Mike http://theses.ncl.ac.uk/jspui/handle/10443/6666 2026-01-27T15:49:14Z 2025-01-01T00:00:00Z

Title: Bayesian optimisation for expensive physical experiments and computer simulators with application in fluid dynamics Authors: Diessner, Mike Abstract: Expensive black-box functions such as physical experiments and computer simulators are challenging to optimise as they cannot be solved analytically, and only small numbers of function evaluations are available for optimisation. This prevents use of conventional methods that rely on gradient information or larger numbers of function evaluations, requiring a specialised optimisation strategy. Bayesian optimisation is a sample-efficient strategy that represents the objective function through a surrogate model and guides the exploration of the input space with heuristics—so-called acquisition criteria—to select promising candidate points sequentially. Expensive black-box functions are a common occurrence in fluid dynamics where the underlying systems, for example the Navier-Stokes equations, can be too complex to solve explicitly and can be viewed as a black box. In addition, the expensive nature of the associated experiments and simulations makes Bayesian optimisation a prime candidate. However, the Bayesian optimisation literature is mainly geared towards statisticians and computer scientists and is potentially challenging to scrutinise and apply for non-experts. Thus, the main motivation of this thesis is to make Bayesian optimisation more accessible and answer some fundamental questions overlooked in the literature, while also developing techniques for specific challenges encountered in but not limited to fluid dynamics. This thesis studies three topics for applying Bayesian optimisation to experiments and simulators. Firstly, it investigates key choices in Bayesian optimisation empirically, such as the choice of the acquisition criterion and the number of data points used for initialisation, and applies the findings to two computer simulators with the objective of controlling air flow to maximise the skin-friction drag reduction over a flat plate—mimicking the surface of a moving vehicle such as the wing of an aeroplane. Secondly, NUBO—an open-source Python package for optimising expensive experiments and simulators aimed at practitioners of Bayesian optimisation—is presented, and its functionalities are discussed. This transparent package allows users to tailor the optimisation loop to their specific problems and supports sequential single-point, parallel multi-point and asynchronous optimisation for bounded, constrained and mixed (discrete and continuous) input parameter spaces. Lastly, problems affected by external environmental variables that cannot be controlled are investigated, and ENVBO—a novel algorithm—is introduced. ENVBO fits a global surrogate model over all controllable and environmental variables but optimises the acquisition criterion only with regard to the controllable variables while keeping the environmental variables fixed at a current measurement. Important properties of ENVBO, such as the robustness to noisy objective functions and the number of environmental variables, are studied. ENVBO is applied to a wind farm simulator to maximise energy production by (a) finding optimal positions for four wind turbines within a complex terrain with changing wind directions and (b) setting optimal derating factors of a row of five wind turbines subject to changing wind speeds. Description: PhD Thesis

2025-01-01T00:00:00Z Khan, Mir Atif Ali http://theses.ncl.ac.uk/jspui/handle/10443/6665 2026-01-23T12:14:50Z 2025-01-01T00:00:00Z

Title: mitoML: Machine Learning to Understand Mitochondrial Disease Pathology Authors: Khan, Mir Atif Ali Abstract: Mitochondria are organelles that reside in virtually every cell of the human body and provide the energy for the cells to function. OXPHOS is the main metabolic pathway through which mitochondria generate energy, it is a machinery made up of five complexes each built with sub-units of multiple proteins and molecules. Defects in OXPHOS machinery manifest as results of genetic mutations and lead to mitochondrial disease. Mitochondrial diseases are currently untreatable due to our limited understanding of their pathology. The study of mitochondrial disease pathology involves discovery of OXPHOS protein expression patterns linked to various genetic mutations. Mitochondrial disease affects high energy demanding cells like Skeletal Muscle (SM) cells (myofibres). The expression of various OXPHOS proteins in myofibres taken from SM biopsies is studied. These OXPHOS proteins in SM tissue are observed using various imaging techniques such as Imaging Mass Cytometry (IMC). IMC produces high dimensional (up to 40 channels) multiplexed pseudo-images representing spatial variation in the expression of a panel of OXPHOS proteins within a tissue, including sub-cellular variation. In previous methods good quality ‘analysable’ myofibres in these multichannel images are segmented and various statistical summaries, such as mean protein expression, are computed per myofibre. Statistical summaries of various groups of myofibres linked with different genetic mutations and a healthy control group are compared to analyse and understand the OXPHOS protein expression patterns of various mitochondrial diseases. Theses methods have a number of limitations i) profiling OXPHOS protein patterns in high dimensionality data: Due to high dimensionality multiplex data, it is not possible to classify and discover the OXPHOS protein expression pattern for four out of five groups of genetic mutations affecting mitochondria that have been studied [1] i.e. except for one group of genetic mutation the classification accuracy for all other groups was below 90%. ii) Precise segmentation and curation of myofibres: It is not possible to precisely segment and curate myofibres with existing applications without heavy manual corrections. iii) The use of statistical summaries per myofibre ignores all intra-myofibre features. There are many hypotheses [2, 3] that theorise the existence of differential features within myofibre in various mitochondrial dysfunctions. In this thesis I use Machine Learning (ML)-specifically logistic regression and XGboost, and various Deep Learning (DL) methods to address the three limitations mentioned above with the following contributions. I) Classify myofibres of mitochondrial patients affected by various genetic mutations, using explainable ML and myofibre statistical summaries. I show that using ML the classification accuracy for all five mutations exceeds 90% . I also demonstrate the use of explainable ML methods to discover the OXPHOS protein expression patterns associated with these high predictive accuracy ML models. II) Precise myofibre segmentation and curation pipeline: I developed ‘myocytoML’ a precise myofibre segmentation and curation pipeline that meets the quality of gold standard manual human annotations. This also led to the building of NCL-SM: A large dataset of more than 50k manually annotated myofibres, which is now available for public use. III) Classify myofibres of mitochondrial patients affected by various genetic mutations, using explainable DL and segmented multichannel raw images. I show that using DL the classification accuracy for all five mutations exceeds 98%. I also demonstrate the use of explainable DL methods to discover the OXPHOS protein expression patterns associated with these high predictive accuracy DL models. Description: PhD Thesis

2025-01-01T00:00:00Z Jamei, Rouzbeh http://theses.ncl.ac.uk/jspui/handle/10443/6663 2026-01-23T12:03:39Z 2025-01-01T00:00:00Z

Title: Mini-TORBED Technology for Carbon Capture Adsorbent Screening Authors: Jamei, Rouzbeh Abstract: Carbon capture (CC) via fluidized bed reactors presents a promising avenue for mitigating CO2 emissions across the energy, industrial, and transportation sectors. This research focuses on developing and evaluating a small-scale and efficient CO2 capture screening platform employing a 3D-printed toroidal fluidized bed (TORBED) reactor. A commercial sorbent, based on branched polyethyleneimine (BPEI), was screened for capturing CO2 from artificial flue gas streams under a range of conditions. The adsorption screening experiments involved the introduction of various N2/ CO2 ratios into the TORBED reactor, and breakthrough curves were collected under different operating conditions, including CO2 volume fractions, BPEI bed loads, gas flow rates, and temperatures. In the hydrodynamic study, three potential industrial materials (RTI, Sasol, and Casale materials) were screened for compatibility with the TORBED reactor. The 'desirable flow regime' was quantified through methods such as visual observations, pressure drop analysis, and standard deviation analysis of pressure drop measurements, which provided insights into particle formations, flow stability, and uniform fluidization. Key results indicated that the RTI material exhibited optimal flow regimes with minimal pressure drop and high stability, making it the most suitable candidate for further adsorption and desorption studies. This comprehensive approach ensured the selection of an effective sorbent and optimal operating conditions for the TORBED reactor, contributing to advancements in carbon capture technology. In adsorption screening experiments, artificial flue gas streams comprising various N2/CO2 ratios were introduced into the TORBED reactor. Breakthrough curves were collected under different operating conditions, including CO2 volume fractions (ranging from 2 to 20 vol%), BPEI bed loads (1–2.5 g), gas flow rates (20–35 L/min), and temperatures (40–70 °C). The breakthrough curves provided insights into the sorption behaviour of BPEI under different conditions, facilitating the characterization of its adsorption capacity and kinetics. A maximum sorbent capacity of 2.64 ± 0.06 mmol/g was measured within experiment durations lasting no longer than 10 seconds. This rapid data collection rate highlights the potential for high throughput screening. Moreover, precise temperature control within the TORBED effectively minimized the influence of heat of adsorption on kinetics. Desorption, a critical aspect of CC, was then studied given its importance in the overall process and lack of relative attention compared to adsorption in the wider literature. The desorption characteristics of the commercial BPEI adsorbent were also investigated using breakthrough experiments, with a focus on studying the influence of heat transfer effects. Experimental results revealed that higher desorption temperature (110 °C), shorter preheating time (achieved with a gas flow rate of 25 L/min), and elevated CO2 concentrations during adsorption (20 vol%) improved the desorption efficiency significantly (defined as CO2 desorbed compared to the adsorbed amount). Kinetic modelling plays a crucial role in understanding and optimizing adsorption and desorption processes. Upon analysis of the cumulative uptake curves extracted from the breakthrough data, it was found that the fractional order kinetic model best matches the behaviour of the BPEI adsorbent compared to the pseudo-1st order and pseudo-2nd order models. This implies that both physisorption and chemisorption processes are responsible for the binding of the CO2 with the BPEI surface. This work reinforced by two published papers in the Chemical Engineering Journal—provides fundamental insights and practical solutions that directly contribute to more efficient, flexible, and economically viable CCS processes. 1. Jamei et al. (2023, Chem. Eng. J. 451:138405) demonstrated rapid and intensified screening of a branched polyethyleneimine (BPEI) adsorbent, achieving breakthrough measurements in a matter of seconds. This unprecedented speed of data collection allows for the rapid assessment of multiple sorbents and conditions, ultimately reducing the time and resources required for sorbent selection and optimization. 2. Jamei et al. (2024, Chem. Eng. J., 1385894724070591) addressed challenges related to small-scale Temperature Swing Adsorption (TSA) in CCS. The study showed that by tuning temperature profiles and flow regimes within the TORBED reactor, it is possible to enhance sorbent regeneration efficiency. In summary, this research highlights the potential use of small-scale TORBED technology for screening CC materials to advance carbon capture more generally. By investigating adsorption and desorption characteristics and employing kinetic modelling, this study offers valuable insights for example optimising desirable flow regime to uniform fluidisation of sorbents in entire bed area for enhancing the efficiency of CO2 capture and mitigating industrial emissions. Keywords: TORBED, Adsorption, swirling, carbon capture, Fluidisation, BPEI Description: PhD Thesis

2025-01-01T00:00:00Z