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(2021) Chua, StephanieThesisImprovements in liquid lithium-ion battery electrolytes using of metal organic frameworks (MOFs) as a functional decoration on polymer membrane separators were investigated using a combination of experimental and theoretical methods. Zirconium-based MOF UiO-66 was introduced to the polymer support using the mixed matrix membrane (MMM) method. The method allowed the one-step manufacture of a robust, mechanically pliable polymer-MOF membrane composite of high MOF loading. MOF-MMMs imparted improved electrochemical behaviours such as a widened potential operating window, near-unity transference number, and increased presence of solid electrolyte interphase (SEI) components crucial to battery performance. Density functional theory (DFT) calculations were also performed to provide insights regarding electrolyte solvation in the presence of MOF. A simple dip-coating technique was utilised to modify the surface of the MOF-MMMs with polydopamine (PDA) for further improvement of the electrochemical properties. Increased transference numbers, as well as stability during rate cycling, were observed with the resulting PDA-MMM owing to the improved electrode/electrolyte interface. However, surface analyses using x-ray photoelectron spectroscopy (XPS) showed that there are reduced amounts vital SEI components compared to the original MOF-MMM support. The last section further explores the versatility of UiO-66 and tackled the preparation of gel polymer electrolytes (GPEs) decorated with UiO-66 via phase inversion technique. Using the phase inversion method, the fabricated GPE contained pores from both polymer substrate and the intrinsic pores of the 3D nanomaterial for improvement of electrochemical properties. It was demonstrated in this work that the MOF GPE is equally inert and suitable in ether or carbonate-based electrolytes. Overall, this study demonstrated the versatility of UiO-66 metal organic frameworks for use as a functional nanofiller for electrolyte membranes. With the use of inexpensive membrane fabrication methods, the composites obtained are viable for lithium-metal battery applications. Similarly, insights drawn can provide a springboard towards future study of MOF-based electrolytes.
(2022) Zhang, QiThesisAs a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing.
Service network design for emerging modes in air transport: autonomous airport inter-terminal bus shuttle and air metro(2022) Zhao, RunqingThesisEmerging modes of air transport such as autonomous airport shuttle and air taxi are potentially efficient alternatives to current transport practices such as bus and train. This thesis examines bus shuttle service within an airport and air metro as two examples of network design. Within an airport, the bus shuttle serves passengers between the terminals, train stations, parking lots, hotels, and shopping areas. Air metro is a type of pre-planned service in urban air mobility that accommodates passengers for intra- or inter-city trips. The problems are to optimise the service, and the outputs including the optimal fleet size, dispatch pattern and schedule. Based on the proposed time-space networks, the service network design problems are formulated as mixed integer linear programs. The heterogeneous multi-type bus fleet case and stochastic demand case are extended for the airport shuttle case, while a rolling horizon optimisation is adopted for the air metro case. In the autonomous airport inter-terminal bus shuttle case, a Monte Carlo simulation-based approach is proposed to solve the case with demand stochasticity, which is then further embedded into an "effective" passenger demand framework. The "effective" demand is the summation of mean demand value and a safety margin. By comparing the proposed airport shuttle service to the current one, it is found that the proposed service can save approximately 27% of the total system cost. The results for stochastic problem suggest estimating the safety margin to be 0.3675 times of the standard deviation brings the best performance. For the second case, the service network design is extended with a pilot scheduling layer and simulation is undertaken to compare the autonomous (pilot-less) and piloted service design. The results suggest that an autonomous air metro service would be preferable if the price of an autonomous aircraft is less than 1.6 times the price of a human-driven one. The results for rolling horizon optimisation suggest to confirm the actual demand at least 45 minutes prior to departure. Based on data from the Sydney (Australia) region, the thesis provides information directly relevant for the service network design of emerging modes of air transport in the city.
Rational Synthesis of Lead-Free Epitaxial BiFe0.5Cr0.5O3 Perovskite Thin Film: A Structure-Property Relationship Study for Emerging Optoelectronic Application(2021) Seyfouri, MoeinThesisMultiferroic BiFe0.5Cr0.5O3 (BFCO) in which ferroelectric and magnetic orders coexist has gained research interest owing to its potential applications, e.g., spintronic and resistive random-access memory. Moreover, multiferroics possess a narrower bandgap compared to typical ferroelectrics, extending their application to photovoltaic devices. In contrast to the conventional semiconductors, the polarization-induced electric field facilitates the photoexcited charge separation, leading to an above-bandgap photovoltage in ferroelectrics. Nevertheless, a long-standing issue is the relatively low absorption of visible light. Thus, it is essential but challenging to tune their bandgap without compromising ferroelectricity. This thesis explores structural phase transition in the epitaxial BFCO films grown on SrRuO3 buffered (001) SrTiO3 substrate via Laser Molecular Beam Epitaxy (LMBE). Reciprocal space mapping result shows strain relaxation mechanism is not solely by the formation of misfit dislocation but also by changing the crystal symmetry, transitioning from tetragonal-like to a monoclinically distorted phase as the thickness increases. The crystallographic evolution is also coupled with bandgap modulation, confirming that BFCO structure and its physical properties are strongly intertwined. Using spectroscopic ellipsometry, the slight redshift of the bandgap distinguishes the absorption process of the T-like BFCO layer from that of monoclinically distorted structure, further confirmed by spectral photocurrent measurement via conductive-atomic force microscopy. The preparation of pure phase BFCO film with a robust polarization is of paramount importance for practical application. Yet, similar to the parental bismuth ferrite, BFCO suffers from poor electrical leakage performance. We report a three-order of magnitude suppression in the leakage current for the BFCO film through judicious adjustment of the growth rate. Scanning probe microscopy (PFM, AFM and c-AFM) results reveal that both microstructure and ferroelectric properties can be tuned by lowering the growth rate, ensuing realization of the room-temperature ferroelectric polarization comparable to the ab-initio predicted value. This thesis provides a facile strategy to tailor the structure-property of epitaxial BFCO film and its functional response for emerging optoelectronic devices.
(2021) Alohali, Ruaa Tawfiq AThesisThe Arabian basin was subject to several tectonic events, including Lower Cambrian Najd rifting, the Carboniferous Hercynian Orogeny, Triassic Zagros rifting, and the Early/Cretaceous and Late/Tertiary Alpine orogenic events. These events reactivated Precambrian basement structures and affected the structural configuration of the overlying Paleozoic cover succession. In addition to a 2D seismic array and several drill well logs, a newly acquired, processed 3D seismic image of the subsurface in part of the basin covering an area of approximately 1051 km2 has been provided to improve the understanding of the regional tectonic evolution associated with these deformation events. In this study, a manual interpretation is presented of six main horizons from the Late Ordovician to the Middle Triassic. Faults and folds were also mapped to further constrain the stratigraphic and structural framework. Collectively, this data is used to build a geological model of the region and develop a timeline of geological events. Results show that a lower Paleozoic sedimentary succession between the Late Silurian to the Early Permian was subject to localised tilting, uplift, and erosion during the Carboniferous Hercynian Orogeny, forming a regional unconformity. Subsequent deposition occurred from the Paleozoic to the Mesozoic, producing a relatively thick, conformable, upper succession. The juxtaposition of the Silurian rocks and Permian formations allows a direct fluid flow between the two intervals. Seismic analysis also indicated two major fault generations. A younger NNW-striking fault set with a component of reverse, east-side-up displacement affected the Lower Triassic succession and is most likely related to the Cretaceous and Tertiary Alpine Events that reactivated the Najd fault system. These fault structures allow vertical migration that could act as conduits to form structural traps. Manual mapping of fault structures in the study area required significant time and effort. To simplify and accelerate the manual faults interpretation in the study area, a fault segmentation method was developed using a Convolutional Neural Network. This method was implemented using the 3D seismic data acquired from the Arabian Basin. The network was trained, validated, and tested with samples that included a seismic cube and fault images that were labelled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training dataset. To achieve a more robust model, the prediction results were further enhanced using postprocessing by linking discontinued segments of the same fault and thus, reducing the number of detected faults. This method improved the accuracy of the prediction results of the proposed model using the test dataset by 77.5%. Additionally, an efficient framework was introduced to correlate the predictions and the ground truth by measuring their average distance value. This technique was also applied to the F3 Netherlands survey, which showed promising results in another region with complex fault geometries. As a result of the automated technique developed here, fault detection and diagnosis were achieved efficiently with structures similar to the trained dataset and has a huge potential in improving exploration targets that are structurally controlled by faults.
Advancing understanding of development policy impacts on transboundary river basins: Integrated watershed modelling of the Lower Mekong Basin.(2021) Ly, KongmengThesisThe management of transboundary river basins across developing countries, such as the Lower Mekong River Basin (LMB), is frequently challenging given the development and conservation divergences of the basin countries. Driven by needs to sustain economic performance and reduce poverty, the LMB countries are embarking on significant land use changes in the form hydropower dams, to fulfill their energy requirements. This pathway could lead to irreversible changes to the ecosystem of the Mekong River, if not properly managed. This thesis aims to explore the potential effects of changes in land use —with a focus on current and projected hydropower operations— on the Lower Mekong River network streamflow and instream water quality. To achieve this aim, this thesis first examined the relationships between the basin land use/land cover attributes, and streamflow and instream water quality dynamics of the Mekong River, using total suspended solids and nitrate as proxies for water quality. Findings from this allowed framing challenges of integrated water management of transboundary river basins. These were used as criteria for selecting eWater’s Source modelling framework as a management tool that can support decision-making in the socio-ecological context of the LMB. Against a combination of predictive performance metrics and hydrologic signatures, the model’s application in the LMB was found to robustly simulate streamflow, TSS and nitrate time series. The model was then used for analysing four plausible future hydropower development scenarios, under extreme climate conditions and operational alternatives. This revealed that hydropower operations on either tributary or mainstream could result in annual and wet season flow reduction while increasing dry season flows compared to a baseline scenario. Conversely, hydropower operation on both tributary and mainstream could result in dry season flow reduction. Both instream TSS and nitrate loads were predicted to reduce under all three scenarios compared to the baseline. These effects were found to magnify under extreme climate conditions, but were less severe under improved operational alternatives. In the LMB where hydropower development is inevitable, findings from this thesis provide an enhanced understanding on the importance of operational alternatives as an effective transboundary cooperation and management pathway for balancing electricity generation and protection of riverine ecology, water and food security, and people livelihoods.
(2022) Cao, JunThesisThis thesis focuses on the development and applications of magnetic resonance electrical properties tomography (MREPT), which is an emerging imaging modality to noninvasively obtain the electrical properties of tissues, such as conductivity and permittivity. Chapter 2 describes the general information about human research ethics, MRI scanner, MR sequence and the method of phase-based MREPT implemented in this thesis. Chapter 3 examines the repeatability of phase-based MREPT in the brain conductivity measurement using balanced fast field echo (bFFE) and turbo spin echo (TSE) sequences, and investigate the effects of compressed SENSE, whole-head B_1 shimming and video watching during scan on the measurement precision. Chapter 4 investigates the conductivity signal in response to short-duration visual stimulus, compares the signal and functional activation pathway with that of BOLD, and tests the consistency of functional conductivity imaging (funCI) with visual stimulation across participants. Chapter 5 extends the use of functional conductivity imaging to somatosensory stimulation and trigeminal nerve stimulation to evaluate the consistency of functional conductivity activation across different types of stimuli. In addition, visual adaptation experiment is performed to test if the repetition suppression effect can be observed using funCI. Chapter 6 explores if resting state conductivity networks can be reliably constructed using resting state funCI, evaluates the consistency of persistent homology architectures, and compares the links between nodes in the whole brain. Chapter 7 investigates the feasibility of prostate conductivity imaging using MREPT, and distinctive features in the conductivity distribution between healthy participants and participants with suspected abnormalities.
(2022) Shahriari, SiroosThesisTime series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims: (1) To combine parametric and bootstrapping techniques within time series models; (2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and (3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data. The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types.
(2022) Wang, ShuangyueThesisTwo-dimensional transition metal dichalcogenide (TMD) nanocrystals (NCs) exhibit unique optical and electrocatalytic properties. However, the growth of uniform and high-quality NCs of monolayer TMD remains a challenge. Until now, most of them are synthesized via solution-based hydrothermal process or ultrasonic exfoliation method, in which the capping ligands introduced from organic solution often quench the optical and electrocatalytic properties of TMD NCs. Moreover, it is difficult to homogeneously disperse the solution-based TMD NCs on a substrate for device fabrication since the dispersed NCs can easily aggregate. Here, we put forward a novel CVD method to grow closely-spaced TMD NCs and explored the growth mechanism and attempts on the size control. Their applications acting as electrocatalysts and adhesion layer for Au film deposition have been also well displayed. Through the whole chapters of this thesis, the following aspects are highlighted: 1. MoS2 and other TMD nanocrystals have been grown on the c-plane sapphire. The surface oxygen vacancies determine the density of TMD nanocrystals. The MoS2 nanocrystals demonstrate excellent hydrogen evolution reaction and surface-enhanced Raman scattering performance owing to the abundant edges. 2. Deep insights into the growth of MoS2 nanograins have been explored. The surface step edges and lattice structures of the underlying sapphire substrates have a significant influence on the growth behaviors. The step edges could modulate the aggregation of MoS2 nanograins to form unidirectional triangular islands. The Raman spectra of MoS2 demonstrate a linear relationship with the crystal size of MoS2. 3. The orientation of sapphire substrate has an of importance effect on the critical size of MoS2 nanocrystals. The MoS2 nanocrystals have the smallest size on the r-plane sapphire, besides, the MoS2 on r-plane sapphire demonstrates the sintering-resistance feature, which is attributed to the edge-pinning effect when MoS2 edges are anchored on the sapphire surface. 4. The MoS2 nanocrystalline layer was utilized as the adhesion layer for Au film depositing on a sapphire substrate. The Au films on MoS2 displayed superior transmittance and electrical conductivity as well as outstanding thermal stability, which lay in the strong binding of Au film with MoS2 nanocrystalline layer.
(2022) Kaur, SandeepThesisAdvances in molecular biology data collection, leading to the accumulation of large amounts of diverse data, call for novel computational approaches to enable their effective analysis. This thesis explored the application of visual-analytics-driven bioinformatics approaches to four biomolecular data-driven challenges. For analysing time-series omic and multiomic data, a novel method, Minardo-Model, was developed. Minardo-Model can identify key events (e.g. phosphorylation) from such time-series data and temporally order them. To visualise the inferred order of events, two novel visualisation approaches, event maps and event sparklines, were developed. Minardo-Model was tested using two time-series datasets and in both cases, the event orderings derived by this method correlated with prior knowledge. To streamline the use of experimental 3D protein structures for analysing sequence variants, a novel method was developed and integrated into Aquaria. For variants specified in the HGVS notation, the method identifies and displays a best matching structure. Additionally, for each variant specified, all structures spanning the variant, and containing the exact variant (missense only), along with sequence features retrieved from external resources, are summarised. The developed approach was used to analyse variants in human ACE2, and SARS-CoV-2 spike, revealing novel insights. For pathogenic bacterial isolates characterised using multilevel genome typing (MGT), the MGTdb web service was developed. MGTdb, enables upload of isolates as sequence reads or extracted alleles, which are processed and assigned the MGT-identifiers. The features of MGTdb, such as interactive visualisation tools, data download and export to external software, enable epidemiological exploration in the context of the local or global database of isolates. The usability of MGTdb was successfully demonstrated through three case studies. For identifying insertion sequences (IS) from short-read sequencing data, a novel method, WiIS, was developed. WiIS was tested on Bordetella pertussis isolates, for which both short-read (test data) and long-read sequences (ground truth) were available - WiIS was found to have high precision and recall. It also outperformed other published tools in identifying IS in B. pertussis genomes. The novel bioinformatics methods developed in this thesis enable novel analysis of a wide variety of data thus providing insight into various biomolecular processes.
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