VictorJuiz/thesis huggingface dataset:
Vapour-deposited hybrid metal-halide perovskite thin films for photovoltaic applications

Hybrid metal-halide perovskite semiconductors have recently shown to be excellent photoabsorbers for photovoltaic devices. There are a variety of techniques that can be used to fabricate hybrid metal-halide thin films, from facile low temperature solution processing methods to vapour deposition methods. Physical vapour deposition, in particular thermal evaporation, is one such technique, which is easily up-scalable and widely used in industry. Additionally, vapour deposition is a solvent-free technique that yields smooth, uniform and pin-hole free thin films.
This thesis reports an investigation of hybrid metal-halide perovskite, CH3NH3PbI3 (MAPbI3) thin films, and their subsequent implementation in photovoltaic devices. The initial investigation follows the formation dynamics of the MAPbI3 thin films from sequentially depositing the precursors, PbI2 and CH3NH3I (MAI), whilst keeping the substrates at 273 K. Upon heating the substrates to room temperature, the MAI diffused into the PbI2. The diffusion is tracked using Fourier transform infrared spectroscopy (FTIR) and x-ray crystal diffraction analysis. The results show that exposing the films to moist air facilitates the diffusion and aids in creating a crystalline perovskite thin film.
Dual-source vapour deposition is a technique whereupon both the precursors are evaporated at the same time, yielding a perovskite thin film immediately. An investigation on how different substrates influence the perovskite thin film, was carried out. It is found that perovskite growth on TiO2 induces regions of amorphous perovskite at the interface. The presence of amorphous perovskite explains why photovoltaic devices which have a TiO2/MAPbI3 interface show current-voltage hysteresis. However, incorporating a layer of continuous fullerene thin film on the substrate leads to the formation of crystalline perovskite at the interface, and the corresponding photovoltaic devices show no hysteresis.
MAPbI3 photovoltaic devices were carefully investigated at low-temperatures (15 K – 350 K) using Fourier transform photocurrent spectroscopy (FTPS). The photocurrent spectra of MAPbI3 devices showed that the excess PbI2 usually found in MAPbI3 thin films contributes to the photocurrent. Furthermore, a lower limit of the exciton binding energy of 9.1 meV was extracted for MAPbI3 by comparing the low temperature absorption spectra with the photocurrent spectra.
Finally, organic bulk heterojunction photovoltaic devices were investigated. The effect of UV light on the devices for 18 hours was carefully tracked using FTPS. Initially, the results show a decrease in external quantum efficiency (EQE) at high photon energies. A closer look at the EQE spectra reveal a change in the sub-band gap absorption, indicative of a change in the micromorphology of the bulk heterojunction. These findings indicate the need to find non-fullerene acceptors.

Keywords: Vapor-Deposition
Metal-Halide Perovskites
Photocurrent Spectroscopy
Low temperature
Advisor: Herz, L
Developing and applying modelling and Bayesian inference tools for developmental biology

Developmental biology allows us to answer crucial questions about how patterned, polarized cells can organize robustly and repeatably to form living tissues and organisms. Quantitative imaging data is now available that can provide deep insights into developmental processes, but to make best use of these data, we need mathematical and computational models. Linking complex mechanistic models to quantitative experimental data is challenging, but Bayesian inference provides a principled framework for this. Together, mechanistic models can guide hypothesis-driven experiments, while quantitative experimental data can inform modelling through a data- driven framework for inference and testable predictions. However, challenges remain in developing methodological tools to relate mechanistic models to data effectively. We address a number of these challenges in this thesis, and demonstrate the use of such tools in an integrative framework to address hypotheses about the robustness and points of control governing mRNA localization during development. We begin by studying the bottlenecks controlling the regulation of mRNA transport, using a fine-grained stochastic model of transport within a single cell and relate this model to experimental data. We then study this stochastic model in a more general setting and ask how experimental design, such as the sampling frequency of observations via imaging, affects parameter estimation. Summary statistics are frequently used to simplify inference for problems with high-dimensional data. It is not clear how to select and combine summary statistics for approximate Bayesian computation, although much previous work has been done in this area. We address this problem with an adaptive algorithm that aims to maximize the distance between prior and approximate posterior distributions. Finally, we return to our study of mRNA localization by developing a coarse-grained model to address questions about how robustness of mRNA localization is regulated during development. Using a simple differential equation model, we make testable predictions, which demonstrate the surprising robustness of mRNA localization, and subsequently reveal crowding as a mechanism for regulation of this process. Together, the contributions of this work are towards developing modelling and inference tools to enable quantitative hypothesis-driven study of developmental and cell biology.

Keywords: Bayesian statistics
Approximate Bayesian computation
Model comparison
RNA localization
Advisor: Baker, R
Parton, R
Stress-induced extracellular vesicle and cytoophidium biology in breast cancer

Cells respond to metabolic stresses by reprogramming gene expression, degrading unneeded proteins and altering post-translational modifications of other proteins. Herein, I reported that breast cancer (BC) cells can adapt to cellular stress by modulating the protein composition and function of extracellular vesicles (EVs) and reorganizing metabolic enzymes into filamentous structures, termed cytoophidia. Glutamine is a non-essential amino acid that activates the mechanistic target of rapamycin complex 1 (mTORC1) pathway. I confirmed that luminal and basal lineages of BC cells differ in their glutamine dependencies, and that EVs may mediate intercellular glutamine symbiosis between the two cell lineages (Chapter 3). Despite the difference in sensitivity towards INK128 (an mTOR kinase inhibitor) between lung and brain metastatic clones of basal BC cells, I demonstrated that, blocking mTOR led to secretion of EVs containing proteins associated with cell migration and activation of epithelial-mesenchymal transition (EMT) (Chapter 4). I further demonstrated that the glutamine dependency was not only variable across BC subtypes but also between lung and brain metastatic clones of basal BC cells. Furthermore, my data shows that inhibition of glutamine consumption by 6-Diazo-5-oxo-L-norleucine (DON) significantly promoted higher abundance and length of glutamine-dependent cytidine triphosphate synthase 1 (CTPS1) and inosine-5’-monophosphate dehydrogenase 1 (IMPDH1) cytoophidia in brain metastatic than lung metastatic clone of basal BC cells, highlighting the variation in glutamine dependency across different metastatic clones of basal BC (Chapter 5). The assemblies of cytoophidia of CTPS and many other metabolic enzymes is a stress adaptation strategy to maintain metabolic homeostasis during nutrient starvation, as I demonstrated by conducting a genetic screen in budding yeast (Chapter 5). In summary, my work suggests that stress-induced alteration of protein composition and function of EVs and reorganization of metabolic enzymes into cytoophidia are strategies for BC cell survival in hostile microenvironment.

Advisor: Goberdhan, D
Liu, J
Mäger, I
Programming patchy particles to form complex ordered structures

This thesis explores by numerical simulations the rational use of patchy particles to control material properties, by designing particles to either assemble into or apply internal stress to a target structure. Increasingly complex and fine-tuned patchy-particle assemblies are being reported, which may have a wide range of interesting properties and thus immense value for meso-scale materials, devices, and technologies. Understanding the behaviour of patchy-particle ensembles is also useful for understanding natural systems, such as viruses and condensed matter.
I introduce a systematic, rational design scheme for a set of patchy particles to form a unique periodic structure. Torsional restrictions encoded in my patches assist assembly, by controlling second-nearest neighbour bonding. As a proof-of-concept, I demonstrated the robust assembly of five complex, extended, three-dimensional targets. In contrast, previous structures assembled from patchy particles are mostly simple, two-dimensional, and/or finite. I also considered simplified versions of the successful designs (e.g. fewer patches, no torsional interactions), finding them sufficient in some cases but not others.
I further extend the structural complexity by assembling three-dimensional dodecagonal quasicrystals, building on earlier two-dimensional work which was recently realised via DNA origami. This is the first demonstration of the self-assembly of any three-dimensional quasicrystal from patchy particles. I use two systems: unary, with wide, non-selective patches; and ternary, with narrower, selective patches. Assembly is more complicated than in two dimensions (e.g. due to de-mixing), but robust nonetheless. I characterised the structures by their diffraction patterns. I unexpectedly observed screw dislocations in the unary system (the ternary system's narrower patches disfavour defects), and in an approximant.
Finally, I deliberately introduce internal stress into a helical, tube-like structure via imperfectly aligned patches. Recently, a new mechanism of cholesteric ordering was identified (in DNA origami filaments) - solenoidal shape deformations due to mechanical stress. However, the results for my tubes were inconclusive, with no clear trend in solenoidal shape deformations being yet apparent. This was at least partially due to the tubes' stiffness, which reduced the shape fluctuations, but may also reflect the complex network of axial and lateral interactions controlling the tubes' mechanics.

Keywords: Soft matter
Cholesteric phases
Patchy particles
Self-assembly
Dodecagonal
Quasicrystals
Helical nanotubes
Monte Carlo
Advisor: Doye, J
Sugars sensing and amplification using bacterial operons

Carbohydrates, especially oligosaccharides are essential molecules present in all the living organisms. To analyse and biosynthesize saccharides of high interest new techniques are required. In this project, a novel biosynthetic approach for sugar sensing and amplification is proposed. This approach is based on the use of sugar operon regulated recombinant gene expression as a detector for the presence of a specific sugar with subsequent activation of amplification circuits for the given sugar
Initially, bacterial lactose metabolism operon and β-1,4-galactosyltransferase 1 were used in the design of a self-inducing lactose amplification gene circuit. This proof-of-principle model showed that sugars can be sensed using an autoinduction loop system. However, an inevitable effect of promoter leakiness led to constant basal amplification of sugar in the autoinduction system. Consequently, this simple approach was not deemed to be practically useful for sugar sensing and additional complexity was added to the design.
Subsequently, a sucrose-lactose synthetic oscillator was designed; that relies on separating the sensing and biosynthetic parts into distinct compartments to bypass the promoter leakiness. A chromosomally encoded sucrose metabolism operon (Csc) was studied as a part of the design because it is homological to the Lac operon, it has been poorly studied, and sucrose is the cheapest commercially available sugar 1.
Finally, the first sucrose regulated recombinant plasmid for E. coli was constructed and tested, with sucrose sensing and following lactose synthesis as part of the designed sucrose-lactose oscillator allowing an initial step towards applicable sugar sensing and amplification system. Using the generated biosynthetic construct, the potential of such approaches was illustrated by studying sucrose transport into E. coli cells. The results suggest diffusion of sucrose without any active transport.

Advisor: Davis, B
Baldwin, A
NextBASS: a new low-frequency CMB foregrounds instrument

The Next Band All-Sky-Survey (NextBASS) is an instrument capable of measuring the diffuse Galactic Foregrounds continuously in frequency between 7 to 30 GHz, in both temperature and polarisation. Still currently in the design phase, NextBASS will obtain a resolution of ≤ 1◦ , using a 6m cross-Dragone telescope with two bands, a single feed operating at 7 – 15 GHz and a 33-feed array of corrugated feed horns for 15 – 30 GHz. NextBASS will achieve a raw sensitivity of ≈ 9.4µKdeg for band-1, and ≈ 1.7µKdeg for in band-2. This sensitivity estimate is based on a conceptual 5 year survey, modelled with the atmospheric conditions and a visible sky fraction mirroring those of the Llano de Chajnantor Observatory. The receiver and digital back-end technologies will use continuous wave stabilisation with a low-bit and fast sampling ADC, and digitalsignal-processing performed in an FPGA. Using single pixel forecasting simulations with Bayesian parametric fitting methods, I demonstrate that NextBASS is capable of measuring a synchrotron spectral curvature term. Furthermore, NextBASS has the potential to detect the presence of a 1% polarised AME component, in regions of sky where the AME amplitude is bright comparative to other foregrounds. NextBASS will improve constraints on the diffuse foreground parameters and help remove bias on the cosmic microwave background B-mode amplitude. This thesis focuses on the motivations behind NextBASS by assessing its impact through single pixel forecasting, along with a full proof of concept, detailing the antenna and feed horn designs and developing a receiver model. The final performance of the combined optical and receiver system is capable of achieving beam efficiencies of over 96% in a 1◦ beam, yielding TSYS ≤ 34 K for all frequencies, and a cross-polarisation of less than -35 dB across the band.

Advisor: Jones, M
Generalised networks for protein interaction analysis

Protein interaction networks (PINs) are mathematical representations of interactions between proteins within organisms. Studying their properties can give insights into biological functions and the importance of proteins, and it can therefore aid in drug- discovery. Graphs are the most common mathematical object used to represent PINs. In this thesis, we investigate generalised mathematical representations of PINs. In particular, we examine multilayer networks (MLNs) and node-weighted networks. These mathematical objects allow the construction of temporal PINs and tissue-specific PINs by combining gene-expression data with PINs. We introduce promiscuity as an information-theoretical measure of a node’s distribution of neighbours across different layers in MLNs. We examine promiscuity in synthetic networks and tissue-specific PINs and find that the vast majority of proteins are not cell-type specific. Integrating temporal gene-expression data with PINs allows us to create temporal PINs in the form of MLNs. We investigate an eigenvector-based temporal centrality in a temporal PIN of yeast during the cell cycle. We thereby examine the change of proteins’ importance over time, which reflects their activity during the cell cycle. We then discuss the detection of community structure in node-weighted networks. For synthetic networks, we show that considering node weights can alter detected community structure. We combine a human PIN with gene-expression data to construct tissue-specific PINs and investigate their community structure. Comparing the detected communities with gene-ontology information, we find some tissue-specific functions of these PINs. Overall, the case studies in this thesis suggest that MLN and node-weighted networks are suitable for the integration of protein-interaction data with other biological data sets.

Keywords: Systems Biology
Network Science
Protein-Protein Interaction
Machine Learning
Advisor: Deane, C
Maini, P
Wray, J
Porter, M
Deep artificial neural networks applications in the biomedical sciences

Deep artificial neural networks are a family of computational models that have led to a dramatical improvement of the state of the art in several domains such as image, voice or text analysis. These methods provide a framework to model complex, non-linear interactions in large datasets, and are naturally suited to the analysis of hierarchical data such as, for instance, longitudinal data with the use of recurrent neural networks.
In the other hand, the biomedical sciences are confronted with increasingly complex statistical analysis challenges that traditional methods oftentimes fail to handle properly. As deep neural networks were able to achieve so much in so little time in the field of artificial intelligence, it only feels natural to see whether these new analysis problems can be addressed with these methods. This thesis introduces the concept of deep artificial neural networks as applications in the biomedical sciences, with both a theoretical and practical approach.

Advisor: Howard, N
Handa, A
Cross-linguistic transfer of phonological awareness in Chinese children receiving English instruction: an intervention study

The aim of the research is to investigate whether children transfer phonological awareness between languages. There is theoretical and practical significance to study the transfer of phonological awareness across languages. We can gain a better insight into how bilingual children develop phonological awareness in both languages, and how children’s phonological awareness support learning to read in different orthographies. In practice, the transfer enables bilingual children to develop phonological awareness in their first language and transfer to their second language. The learning of second language can be made easier by transferring the prior phonological knowledge across languages. In Study 1, phonological measures were developed to assess the phonological awareness of English and Cantonese in parallel. The measures were rhyme oddity, initial phoneme deletion, and final phoneme deletion. The measures were evaluated, and the results showed that the English version and the Cantonese version of each measure were comparable to each other. In Study 2, phonological awareness was hypothesized as a language-general ability across languages. It was predicted that training English phonological awareness could enhance Cantonese phonological awareness and vice versa. One hundred and sixty-two Hong Kong children at age five were randomly assigned to English phonological training, Cantonese phonological training, or an active control of print concepts. The children were tested on English and Cantonese phonological awareness before and after the training by the three phonological awareness measures developed in Study 1. Results revealed that the English training group significantly outperformed the print concepts control group in all the Cantonese measures, whereas the Cantonese training group significantly outperformed the control group in all the English measures. The results provided strong evidence that there was a bidirectional transfer of phonological awareness between English and Cantonese, suggesting that phonological awareness is a language-general ability that can transfer across languages. Practical and theoretical implications of the findings of both studies were discussed.

Keywords: Phonological training
Phonological Awareness
Cross-Language Transfer
Bilingual
Cantonese-English
Cross-Linguistic Transfer
Intervention
Advisor: Nunes, T
Multi-responsive hydrogel structures from patterned droplet networks

Responsive hydrogels that undergo controlled shape changes in response to a range of stimuli are of widespread interest for use in microscale soft robotics and biomedical devices. However, existing fabrication methods cannot easily produce patterned multi-material structures. This thesis describes a novel approach for producing 2D- and 3D-patterned, multi-material, multi-responsive hydrogels, on a μm to mm scale, templated by droplet networks. Droplet networks are assemblies of aqueous droplets formed in a lipid-containing oil, and connected by droplet interface bilayers (DIBs). Within the compartmentalised structure of a droplet network, 3D patterning of multiple types of aqueous droplet is possible. Initially, nanolitre N-isopropylacrylamide (NIPAm) pre-gel droplets were assembled into networks in a range of 2D and 3D geometries. Polymerization across the lipid bilayers then resulted in continuous poly(N-isopropylacrylamide) (PNIPAm) hydrogel structures that reversibly contracted when heated and cooled. Exploiting the ability to modulate the temperature response of PNIPAm, and form multi-material structures using droplet networks, enabled the fabrication of structures capable of non-uniform shape changes, such as curling. Subsequently, additives were incorporated into the hydrogel structures that imparted additional functionality, including lightcontrolled shape change and magnetically controlled locomotion. By utilising a mechanism for dual temperature control, this would allow the fabrication of a magnetically controlled gripper-like device. Finally, the adaptation of a 3D droplet printer to automate the fabrication of PNIPAm hydrogel structures is described. With the droplet printer, networks composed of picolitre droplets were produced, which will allow higher resolution patterning. Furthermore, automated droplet generation enables the precise placement of hundreds to thousands of droplets. Successful implementation of this technology will allow the formation of multimaterial responsive hydrogels from networks composed of thousands of droplets, patterned to a resolution of tens of microns.

Advisor: Bayley, H
Booth, M