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  • Towards Improved Rechargeable Zinc Ion Batteries: Design Strategies for Vanadium-Based Cathodes and Zinc Metal Anodes

    Guo, Jing (2021-12-21) [Dissertation]
    Advisor: Alshareef, Husam N.
    Committee members: Da Costa, Pedro M. F. J.; Ooi, Boon S.; Fan, Hongjin
    The need for renewable energy is increasing as a result of global warming and other environmental challenges. Renewable energy systems are intermittent in nature and require energy storage solutions. Lithium-ion batteries are the first choice for storing electrical energy due to their high energy density, long cycle life, and small size. However, their widespread use in grid-scale applications is limited by high cost, low lithium resources, and security issues. Among the various options, the rechargeable zinc ion water battery has the advantages of high economic efficiency, high safety, and environmental friendliness, and there are great expectations for energy storage on a network scale. Inspired by these benefits, people have put a lot of effort into developing and manufacturing zinc-based energy storage devices. As the main component of zinc ion battery, the cathode material plays an important role in the storage / release of zinc ions during insertion and extraction. Vanadium-based materials are attracting attention due to their various oxidation states, diverse structures, and abundant natural resources. However, the details of suitable cathode materials and Zn2+ storage mechanism for rechargeable zinc ion battery are not yet fully understood. In this thesis, firstly, the prepared zinc pyrovanadate delivers good zinc ion storage properties owing to its open-framework crystal structure and multiple oxidation states. Mechanistic details of the Zn-storage mechanism in zinc pyrovanadate were also elucidated. Then, a calcium vanadium oxide bronze with expanding cavity size, smaller molecular weight, and higher electrical conductivity are proposed to deeply understand the impact of the crystal structure on battery performance. To improve the stability of the cathode in rechargeable zinc ion battery, an artificial solid electrolyte interphase strategy has been proposed by inducing an ultrathin HfO2 layer via the Atomic layer deposition method, which effectively alleviates the dissolution of active material. Finally, a nitrogen-doped 3D laser scribed graphene with a large surface area and uniform distribution of nucleation sites has been used as the interlayer to control Zn nucleation behavior and suppress Zn dendrite growth, which brings new possibilities for the practical rechargeable zinc ion battery.
  • Investigation of the Long-Term Operational Stability of Perovskite/Silicon Tandem Solar Cells

    Aljamaan, Faisal (2021-12-14) [Thesis]
    Advisor: De Wolf, Stefaan
    Committee members: Laquai, Frédéric; Lanza, Mario; Ooi, Boon S.
    With the global energy demand projected to grow rapidly, it is imperative to divest from traditional greenhouse gas-based power production toward renewable energy sources such as solar. In recent years, solar photovoltaics (PV) hold a large share among renewables sources. Currently, the market is dominated by crystalline silicon solar cells due to their low levelized cost of energy (LCOE) values. However, to sustain this progress, the power conversion efficiency of PV devices must be further improved since tiny costs cut from the other expenses is difficult. On the other hand, the margin for the PCE improvement in c-Si technology is also quite limited since the technology is approaching its practical limits. At this stage, coupling c-Si devices with another efficient solar cell in tandem configuration is a promising way to overcome this challenge. Perovskite solar cells (PSCs) represent a breakthrough solar technology to enable this target due to their proven high efficiency and potential cost-effectiveness. Whereas perovskite/silicon tandem solar cells are promising, their operational stabilities are still a significant concern for market entry. Here, the degradation mechanism of n-i-p perovskite/Si tandem solar cells was investigated. Thermal stability tests have shown severe degradation in such tandem devices. On the other hand, tandem devices were relatively stable when placed in a humidity cabinet with 25% relative humidity (RH). Conversely, temperature degraded devices showed cracks all over the perovskite surface and rupture in the top electrode after 1000 hrs at 85 oC. Additionally, silver iodide formation was depicted in XRD and XPS analysis. To enhance the stability, methods to reduce the hysteresis were studied. First, potassium chloride (KCl) was applied as a passivation agent to the electron transport layer (ETL) to reduce surface defects. Second, 2D passivation was applied to reduce trap density and enhance the crystallinity of the perovskite film. Finally, organic molecules were placed between the hole transport layer (HTL) and metal-oxide interface as interlayers to prevent diffusion of metal oxide to the HTL and accumulation of the dopant at the metal-oxide interface. After passivation and interface layers, stability enhanced but further improvement is still required.
  • Poly-Silicon Passivating Contacts for Crystalline Silicon Solar Cells

    Alzahrani, Areej A (2021-12-14) [Dissertation]
    Advisor: De Wolf, Stefaan
    Committee members: Laquai, Frédéric; Ooi, Boon S.; Isabella, Olindo
    Passivating-contact technologies fabricated from polycrystalline-silicon (poly-Si) are increasingly considered by the crystalline silicon (c-S) PV industry to be key enablers towards record performance. This is largely thanks to their ability to provide excellent carrier collection and surface passivation, while being compatible with industrial scale production. Poly-Si based passivating contacts consist of a stack of an ultrathin silicon oxide (SiOx) film on the surface of crystalline silicon (c-Si), covered by a doped silicon film. Thin films of SiOx can be grown by several different methods: chemically, thermally, or via UV-ozone exposure. However, each of these methods presents challenges towards industrial implementation. Here, we report an alternative method to grow SiOx films using an in-situ plasma process, where we subsequently deposit the doped poly-Si layer in the same process chamber by plasma enhanced chemical vapor deposition (PECVD). This process presents several advantages, such as ease of fabrication, inherently single-side oxide growth and poly-Si deposition, and the combined deposition in one chamber, lowering capital expenditure. Subsequently, we studied the structure of the SiOx films and the doped poly-Si(p+) capping layers using X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS) in order to determine the films’ elemental composition, and the band alignment at the semiconductor/oxide interfaces. A less p-type polysilicon was observed grown on top of a wet SiOx/c-Si with the origin tentatively attributed to depletion of the boron dopant via pin holes evidenced by AFM. A surface photo-voltage (SPV) was observed by XPS under in-situ light bias (AM 1.5) and a representation of the band alignment of the c-Si/SiOx/p-polysilicon under illumination is derived. The SPV was attributed to the photo accumulation of holes at the p-polysilicon and a splitting of quasi-fermi levels with its magnitude correlated to the device measured iVoc . Finally, a valuable application for this contact technology is the integration of silicon with perovskite solar cells, in the so-called monolithic tandem configuration. This approach is very promising to develop a new generation of PV with unmatched performances. Here, poly-Si contacts offer a variety of advantages, thanks to their broader material selection and to the stability at high processing temperature.
  • Localized Heating in Membrane Distillation for Performance Enhancement

    Mustakeem, Mustakeem (2021-12) [Dissertation]
    Advisor: Ghaffour, NorEddine
    Committee members: Sarathy , S. Mani; Mishra, Himanshu; Warsinger, David
    Membrane distillation (MD) is an emerging technology capable of treating high-saline feeds and operating with low-grade heat energy. However, commercial implementation of MD is limited by so-called temperature polarization, which is the deviation in the temperature at the feed-membrane interface with respect to the bulk fluid. This work presents solutions to alleviate temperature polarization in MD by employing a localized heating concept to deliver heat at the vicinity of the feed-membrane interface. This can be realized in multiple ways, including Joule heating, photothermal heating, electromagnetic induction heating, and nanofluid heating. In the first experiment, a Joule heating concept was implemented and tested, and the results showed a 45% increase in permeate flux and a 57% decrease in specific energy consumption. This concept was further improved by implementing a new dead-end MD configuration, which led to a 132% increase in the gained output ratio. In addition, the accumulation of foulants on the membrane surface could be successfully controlled by intermittent flushing of feedwater. Three-dimensional CFD calculations of conjugate heat transfer revealed a more uniform heat transfer and temperature gradient across the membrane due to the increased feedwater temperature over a larger membrane area. In another approach, a photothermal MD concept was used to heat the feed water locally. A 2-D photothermal material, MXene, recently known for its photothermal property, was used to coat commercial MD membranes. The coated membranes were evaluated under one-sun illumination to yield a permeate flux of 0.77 kg.m$^{−2}$h$^{−1}$ with a photothermal efficiency of 65.3% for a feed concentration of 0.36 g.L$^{−1}$. The system can produce around 6 liters of water per day per square meter of membrane. An energy analysis was also performed to compare the efficiency of various energy sources. Considering the sun as a primary energy source, the performance of different heating modes was compared in terms of performance and scale-up opportunities. Overall this work demonstrates that the application of localized heating will enable the scale-up and the use of renewable energy sources to make the MD process more efficient and sustainable.
  • Polycyclic Aromatic Hydrocarbons and Soot Particle Formation in the Combustion Process

    Shao, Can (2021-12) [Dissertation]
    Advisor: Sarathy, Mani
    Committee members: Roberts, William L.; Dally, Bassam; Gascon, Jorge; Yuan, Xuan
    The threat to the environment and human health posed by the emission of soot particles and their precursors during the combustion process has attracted widespread attention for some time. Generation of soot particles includes the precursor’s formation, particle nucleation, and the growth and oxidation of soot particles; these processes are experimentally and numerically studied in this dissertation. Fuel composition is one of the most important parameters in the study of the combustion emissions. In the first portion of this research, quantified soot precursors were detected in a jet stirred reactor and a flow reactor of several gasoline surrogates, which covered various fuel compositions and different MON numbers. A kinetic model was made to capture the polycyclic aromatic formations and help to clarify the chemistry behind them. Major reaction pathways were discussed, as well as the role of important intermediate species, such as acetylene, and resonantly stabilized radicals like allyl, propargyl, cyclopentadienyl, and benzyl in the formation of polycyclic aromatic hydrocarbons. In the second section, a Fourier-transform ion cyclotron resonance mass spectrometry was first used to probe the chemical constituents of soot particles. By examining the soot particle generated in the early stage of nucleation, some information about the nucleation process was gained. The aromatics in the infant soot particles were all peri-condensed, of a size and shape easily linked by Van der Waals forces to form aromatic dimers and bigger clusters under the specified flame conditions. Compositions in the mature soot particles indicated that soot particles grow through the carbonization process. As a hydrogen carrier, ammonia was considered a good additive for controlling soot formation. In the third portion of this work, chemical effects of ammonia on soot formation were studied. Ammonia can suppress soot formation by reducing the precursor’s formation. Chemical kinetic analysis revealed that C-N species generated in ethylene-ammonia flames removed carbon from participating in soot precursor formation, thereby reducing soot formation, however, high concentrations of toxic hydrogen cyanide may be formed, which warrants further investigation.
  • Eikonal Solution Using Physics-Informed Neural Networks for Global Seismic Travel Time Modelling

    Taufik, Mohammad Hasyim (2021-12) [Thesis]
    Advisor: Alkhalifah, Tariq Ali
    Committee members: Peter, Daniel; Ravasi, Matteo
    Being able to determine how much time it takes for a seismic wave to travel from one point to another is essential in geophysics. One can achieve this goal under the asymptotic ray assumption and end up with the so-called Eikonal equation. The equation finds itself to be beneficial across science and engineering. In geophysics, especially the global seismology field, the solution of this equation is primarily used to perform travel time tomography and earthquake relocation application. In this research I propose a novel scheme to solve the Eikonal equation under two main objectives in mind: being able to compute more accurate first-arrival travel time using Three-dimensional (3-D) velocity model and also being as efficient as the standard procedure. The proposed method is using a physics-informed neural network (PINN). The forward problem is formulated such that the physical equation is the driving component of the minimization of the objective function. The velocity model used on this research is the second generation of the three-dimensional global adjoint tomographic model, GLAD-M25, to account for anelastic behaviour of the Earth. From the numerical tests, I observed one unique feature in using PINNs to solve the Eikonal equation. I demonstrate that I can use a velocity model which has incomplete velocity information in it and still able to model accurately in some regions the travel time. The results show that the proposed method achieves a significant improvement on the velocity validation and more importantly, is able to calculate the first-arrival travel time using a full three-dimensional global tomographic model (GLAD-M25). The validation process is done by comparing the input velocity data with the recovered velocity from the modelled travel time. The residuals for all depth is below -1 to 1 % error and the recovered velocity and input data are align with a cosine similarity value around 0.999. The main limitation pertaining to the first iteration model proposed on this research is its training cost. For each epoch, given the large number of batches, the training takes around 52.383 minutes. However, once the model is trained, the inference process is comparable to a standard Eikonal solver.
  • Production of Linear Alpha Olefins via Heterogeneous Metal-OrganicFramework (MOF) Catalysts

    Alalouni, Mohammed R. (2021-12) [Dissertation]
    Advisor: Han, Yu
    Committee members: Pinnau, Ingo; Castaño, Pedro; Huang, Kuo-Wei; Yan, Ning
    Linear Alpha Olefins (LAOs) are one of the most important commodities in the chemical industry, which are currently mainly produced via homogenous catalytic processes. Heterogeneous catalysts have always been desirable from an industrial viewpoint due to their advantages of low operation cost, ease of separation, and catalyst reusability. However, the development of highly active, selective, and stable heterogeneous catalysts for the production of LAOs has been a challenge throughout the last 60 years. In this dissertation, we designed and prepared a series of heterogeneous catalysts by incorporating structural moieties of homogenous benchmark catalysts into metal-organic-frameworks (MOFs), aiming to provide a feasible solution to this long-standing challenge. First, we reviewed the background and state of the art of this field and put forward the main objectives of our research. Then, we thoroughly discussed a novel heterogeneous catalyst (Ni-ZIF-8) that we developed for ethylene dimerization to produce 1-butene, focusing on its designed principle, detailed characterizations, catalytic performance evaluation, and reaction mechanisms. Ni-ZIF-8 exhibits an average ethylene turnover frequency greater than 1,000,000 h$^{-1}$ (1-butene selectivity >85%), far exceeding the activities of previously reported heterogeneous and many homogenous catalysts under similar conditions. Compared with homogenous nickel catalysts, Ni-ZIF-8 has significantly higher stability and showed constant activity during four hours of continuous reaction for at least two reaction cycles. The combination of isotopic labeling studies and Density Functional Theory calculations demonstrated that ethylene dimerization on Ni-ZIF-8 follows the Cossee-Arlman mechanism, and that the full exposure and square-planer coordination of the nickel sites account for the observed high activity. After that, we further optimized the Ni-ZIF-8 catalytic system from the perspective of practical applications. We achieved double productivity of 1-butene by optimizing the synthetic conditions and explored its usability and performances under solvent-free conditions. Then, we extended our catalyst design concept to prepare heterogeneous catalysts comprising other metals and MOFs, which provided a suitable platform for studying the effects of the metallic center and coordination environment on the catalytic production of LAOs. Finally, we gave our perspectives on the further development of heterogeneous catalysts for the production of LAOs.
  • Effects of Pharmacotherapy, Neurodevelopment, Sex and Structural Asymmetry on Regional Intrinsic Homotopic Connectivity in Youths with Attention Deficit Hyperactivity Disorder.

    Homoud, Zainab (2021-12) [Thesis]
    Advisor: Ombao, Hernando
    Committee members: Hauser, Charlotte; Al-Naffouri, Tareq Y.
    Functional magnetic resonance imaging studies have long demonstrated a high degree of correlated activity between the left and right hemispheres of the brain. Interregional correlations between the time series of each brain voxel or region and its homotopic pair have recently been identified by methods such as homotopic resting-state functional connectivity (H-RSFC). However, little is known about whether interhemispheric regions in patients with Attention-deficit/hyperactivity disorder (ADHD) are functionally abnormal. The aim of this thesis is to examine the association between H-RSFC and medication status, age, sex, and volumetric asymmetry index (AI). In our approach, region-based activity was obtained using three different methods. To test for associations, two linear mixed-effects models were used. Across results, H-RSFC variation was found in subcortical regions and portions of cortical regions. In addition, changes in functional connectivity were found to be linked with structural asymmetry in two cortical regions. More importantly, shifting in homotopic functional activation was found as a result of medication intake in youths with ADHD. These findings demonstrate the utility of homotopic resting-state functional connectivity for measuring differences among pharmacotherapy intake, gender, neurodevelopment, and structural asymmetry.
  • A Bayesian Approach to D2D Proximity Estimation using Radio CSI Measurements

    Bezerra, Lucas (2021-12) [Thesis]
    Advisor: Al-Naffouri, Tareq Y.
    Committee members: Alouini, Mohamed-Slim; Ombao, Hernando; Bader, Ahmed
    Channel State Information (CSI) refers to a set of measurements used to characterize a radio communication link. Radio infrastructure collects CSI and derives useful metrics that indicate changes to modulation and coding to be made to improve the link performance (e.g. throughput, reliability). The CSI, however, has a wider potential use. It contains an environment-specific signature that can be used to extract information about users’ position and activity. In our work, we explore the problem of proximity estimation, which consists of identifying how close a pair of devices are to each other. By assuming that Cellular Base Stations (BSs) are distributed spatially according to a Poisson Point Process (PPP), and that the channel is under Rayleigh fading, we were able to probabilistically model radio measurements and use Bayesian inference to estimate the separation between two devices given their measurements only. We first explore a shadowless channel model, then we investigate how spatially-correlated shadowing can prove useful for estimation. For both cases, Bayesian estimators are proposed and tested through simulations. We also perform experiments and evaluate how well the estimators fit to actual data.
  • Deep Learning Action Anticipation for Real-time Control of Water Valves: Wudu use case

    Felemban, Abdulwahab A. (2021-12) [Thesis]
    Advisor: Al-Naffouri, Tareq Y.
    Committee members: Ghanem, Bernard; Elhoseiny, Mohamed H.; Bader, Ahmed; Masood, Mudassir
    Human-machine interaction could support many daily activities in making it more convenient. The development of smart devices has flourished the underlying smart systems that process smart and personalized control of devices. The first step in controlling any device is observation; through understanding the surrounding environment and human activity, a smart system can physically control a device. Human activity recognition (HAR) is essential in many smart applications such as self-driving cars, human-robot interaction, and automatic systems such as infrared (IR) taps. For human-centric systems, there are some requirements to perform a physical task in real-time. For human-machine interactions, the anticipation of human actions is essential. IR taps have delay limitations because of the proximity sensor that signals the solenoid valve only when the user’s hands are exactly below the tap. The hardware and electronics delay causes inconvenience in use and water waste. In this thesis, an alternative control based on deep learning action anticipation is proposed. Humans interact with taps for various tasks such as washing hands, face, brushing teeth, just to name a few. We focus on a small subset of these activities. Specifically, we focus on the activities carried out sequentially during an Islamic cleansing ritual called Wudu. Skeleton modality is widely used in HAR because of having abstract information that is scale-invariant and robust against imagery variances. We used depth cameras to obtain accurate 3D human skeletons of users performing Wudu. The sequences were manually annotated with ten atomic action classes. This thesis investigated the use of different Deep Learning networks with architectures optimized for real-time action anticipation. The proposed methods were mainly based on the Spatial-Temporal Graph Convolutional Network. With further improvements, we proposed a Gated Recurrent Unit (GRU) model with Spatial-Temporal Graph Convolution Network (ST-GCN) backbone to extract local temporal features. The GRU process the local temporal latent features sequentially to predict future actions. The proposed models scored 94.14% recall on binary classification to turn on and off the water tap. And higher than 81.58-89.08% recall on multiclass classification.
  • Characterizing the chemical contaminants diversity and toxic potential of untreated hospital wastewater

    Baasher, Fras (2021-12) [Thesis]
    Advisor: Hong, Pei-Ying
    Committee members: Saikaly, Pascal; Mahfouz, Magdy M.
    This study characterizes 21 wastewater samples collected from Al-Amal hospital between the period of 12 April till 8 July 2020. Al Almal is a hospital that provides drug addiction and psychological treatment to patients. Using solid-phase extraction and liquid chromatography with tandem mass spectrometry (LC-MS/MS), chemical contaminants profiles in these wastewater samples were determined in a non-targeted manner. These chemicals were then individually analyzed in an in-silico manner by checking against databases and literature to determine if they were mutagenic. By determining the proportion of mutagenic chemicals against the non-mutagenic ones, we aim to determine if untreated hospital wastewater may potentially negatively impact the downstream municipal biological wastewater treatment process. It was determined that 64% of the identified chemicals were not tested for their mutagenic effect, and hence no prior information is available in the literature and databases. Instead, we further performed in-vitro mutagenicity tests using Ames test to determine if the wastewater sample, with all of its chemical constituents, would be mutagenic. Ames test results showed that majority of the samples were non-mutagenic except for 1 sample that imposed a mutagenic effect on Salmonella enterica serovar Typhimurium TA98 and 3 samples with mutagenic effect on TA100. In addition, 1 sample showed a toxic effect on TA100. However, in all 5 instances, these samples only imposed a mutagenic and toxic effect at high concentrations of > 10x. The findings in this study suggest that a specialty hospital like Al Amal does not contribute substantially to mutagenic wastewater streams to the municipal sewer, and hence unlikely to significantly perturb the downstream biological treatment processes. However, there may still be a need to consider ad-hoc contributions of mutagenic and/or toxic wastewater streams from the hospitals.
  • Signal Processing and Optimization Techniques for High Accuracy Indoor Localization, Tracking, and Attitude Determination

    AlSharif, Mohammed H. (2021-12) [Dissertation]
    Advisor: Al-Naffouri, Tareq Y.
    Committee members: Al-Naffouri, Tareq Y.; Shihada, Basem; Laleg-Kirati, Taous-Meriem; Park, Shinkyu; Lohan, Elena S.
    High-accuracy indoor localization and tracking systems are essential for many modern applications and technologies. However, accurate location estimation of mov- ing targets remains challenging. Various factors can degrade the estimation accuracy, including the Doppler effect, interference, and high noise. This thesis addresses the challenges of indoor localization and tracking systems and proposes several solutions. Using a novel signal design, which we named Differential Zadoff-Chu, we developed al- gorithms that accurately estimate the distances of static and moving targets, even un- der random Doppler shifts. We then developed a high-resolution multi-target ranging algorithm that estimates the ranges to targets at proximity based on the Levenberg- Marquardt algorithm. These ranging algorithms require a line of sight (LOS) between the transmitter and the receiver. Therefore, we designed an algorithm to classify re- ceived signals as LOS and non-LOS by exploiting a room’s geometry. Transforming distances into a 2D or 3D location and orientation requires solving an optimization problem. We propose using three nodes arranged as an isosceles triangle to deter- mine the position and orientation of a target. Utilizing the geometry of the isosceles triangle, we developed a highly accurate location and orientation estimation algo- rithm by solving a constrained optimization problem. Finally, we propose a Kalman filter to improve the tracking accuracy of moving targets even under non-LOS condi- tions. This filter fuses the position and orientation estimated using our Riemannian localization algorithm with the position and orientation estimated using an inertial measurement unit (IMU) to obtain a more accurate estimate of a moving target’s position and orientation. We validated the proposed algorithms via numerical simu- lations and real experiments using low-cost ultrasound hardware. The results showed that the proposed algorithms outperformed current state-of-the-art in accuracy and complexity.
  • High Speed Imaging of Splashing by Fuel Droplet Impacts inside Combustion Engine

    Aldawood, Hussain (2021-12) [Thesis]
    Advisor: Thoroddsen, Sigurdur T
    Committee members: Magnotti, Gaetano; Ng, Kim Choon
    The impact of fuel drops on the walls of combustion chambers is unavoidable in direct-injection automotive engines. These drop-solid interactions can lead to splashing of the lubrication oil, its dilution or removal, which can damage the piston or the liner from dewetting. This can also cause irregular and inferior combustion or soot formation. Understanding the drop-splashing dynamics is therefore important, especially as modern IC engines are being down-sized to achieve higher thermal efficiency. Typical cylinders of IC engines contain metal liners on their walls, which have fine azimuthal grooves to support the lubricating oil as the piston moves inside the cylinder. In this thesis we study how these grooves affect the deposition or splashing of impacting diesel drops, while the solid surface is kept dry without the lubricating oil. For these experiments we use sections of actual cylinder liners and apply high-speed video imaging to capture the details of the drop impacts. The first set of experiments used normal impacts on horizontal substrates. These experiments include a range of drop sizes and impact velocities, to identify impact conditions in Reynolds and Weber number space where the transition from deposition to splashing occurs. We also study the maximum radial spreading factor of the impact lamella, finding about 8% larger spreading along the grooves than perpendicular to them. In the second set of experiments we look at the impact on inclined substrates, where the inclination angle is between 30o–60o. This produces strong asymmetry in the maximum spreading, with the tangential velocity governing the maximum radial motion. The inclined impacts change the splashing threshold, requiring larger impact velocities for splashing. The splashing threshold deviates quantitatively from earlier theories, but shows the same qualitative trends. Furthermore, a new splashing mechanism is observed, where the impact forms a prominent ejecta crown from the downstream edge. This crown ruptures first from the grooves at the sides and subsequently the capillarity detaches the downstream levitated liquid sheet from the substrate generating a myriad of splashed droplets. Preliminary observations with impacts on wet substrates show much stronger crown-formation from the lubricating oil film, with potential for dewetting.
  • Understanding biological motions with improved resolution and accuracy by NMR

    Kharchenko, Vladlena (2021-12) [Dissertation]
    Advisor: Jaremko, Lukasz
    Committee members: Hamdan, Samir; Fischle, Wolfgang; Hirt, Heribert; Cierpicki, Tomasz
    Biological motion constitutes a key and indispensable element of all biomolecules, as dynamics tightly link spatial architecture with function. Several computational and experimental techniques have been developed to study biomolecular dynamics. Nevertheless, few label-free and atomic or sub-atomic resolution techniques are able to capture biological motions at close to native conditions. Indeed, the only label-free technique giving atomic level access to dynamics from picoseconds down to seconds is nuclear magnetic resonance (NMR) spectroscopy. In this dissertation, I identify the imperfections and inaccuracies accompanying the routine and well-accepted methods of probing protein dynamics via 15N spin relaxation NMR measurements. Subsequently, I propose and develop solutions and experimental approaches to overcome the limitations and eliminate artefacts. The routine procedures applying heavy water as an internal locking standard lead to artifacts in every type of relaxation rate of 15N amides due to reaction with exchangeable deuterons. The deviations from correct values are most pronounced for highly dynamic and exposed protein fragments. I introduce a novel set of directly detected 15N spin relaxation experiments yielding an unprecedent resolution resolving the signal overlap, although of lower sensitivity. I propose a more accurate. Finally, I present how the 15N spin relaxation techniques and improved routines can be applied to understand biological processes that cannot be described without monitoring molecular motions. Using the example of human BTB domains, which are directly linked to human cancer, I demonstrate the ability to detect cryptic binding sites on the surfaces of proteins. The cryptic binding site was verified by a comprehensive NMR-monitored fragment-based screening that revealed a hit-rate only for MIZ1BTB, which was the only protein displaying slow segmental motions. I also managed to track subtle and biologically-relevant dynamic modulations of an exposed H3 histone tail affected by H1 histones or other histone variants. Enhancement of H3 tail dynamics led to increased H3K36 methylation, while restriction of motions resulted in the opposite effect. These observed correlations unequivocally support the essential role of molecular mobility in biological functions.
  • Corrosion of Buried Metals: Soil Texture and Pore Fluid Saturation

    Castro, Gloria M. (2021-12) [Dissertation]
    Advisor: Santamarina, Carlos
    Committee members: Alshareef, Husam N.; Burns, Susan; Ahmed, Shehab
    The corrosion of buried metals affects geosystems that range from pipelines and nuclear waste disposal to reinforced concrete and archeology. Associated costs exceed 1 trillion dollars per year worldwide, yet current classification methods for soil corrosivity have limited predictive capacity. This study -triggered by the recent development of the Revised Soil Classification System RSCS- seeks to identify the critical soil and environment properties that can improve the prediction of buried metal corrosion. The experimental studies conducted as part of this research recognize the inherently electro-chemo-transport coupled nature of buried metal corrosion, and places emphasis on phenomena that have been inadequately captured in previous studies, such as the effect of soil texture and fines plasticity, partial saturation and moisture cycles, and conditions in Sabkha environments. The comprehensive experimental program involves detailed protocols for specimen preparation, advanced visualization (X-ray micro-CT), corrosion residual characterization (XRD), and detailed image analyses of extracted coupons. Experiments include both laboratory mixtures and a wide range of field specimens gathered throughout Saudi Arabia; furthermore, field observations expand soil assessment to native environmental conditions. Theoretical analyses based on mass conservation and electrochemical phenomena complement the experimental study. Experimental and analytical results lead to new soil corrosivity assessment guidelines. Results show the relevance of the sediment pore fluid saturation, sediment texture, air and water connectivity, active corroding areas, the effect of environmental cycles on buried metal corrosion and evolving backfill contamination.
  • Integral Methods for Versatile Fluid Simulation

    Huang, Libo (2021-11-30) [Dissertation]
    Advisor: Michels, Dominik L.
    Committee members: Pottmann, Helmut; Heidrich, Wolfgang; Batty, Christopher
    Physical simulations of natural phenomena usually boil down to solving an ordinary or partial differential equation system. Partial differential equation systems can be formulated either in differential form or in integral form. This dissertation explores integral methods for the simulation of magnetic fluids, so-called ferrofluids, and the surface of the vast ocean. The first two parts of this dissertation aim to contribute to the development of accurate and efficient methods for simulating ferrofluids on the macroscopic (in the order of millimeters) scale. The magnetic nature of these fluids imposes challenges for the simulation. The two most important challenges are to first model the influence of ferrofluids on surrounding magnetic fields and second the influence of magnetic forces on the fluids’ dynamics. To tackle these challenges, two Lagrangian simulation methods have been proposed. The first method discretizes the magnetic substance as clusters of particles carrying radial basis functions and applies magnetic forces between these particles. This is a mesh-free method suitable for particle-based fluid simulation frameworks such as smoothed-particle hydrodynamics. The second method follows another direction, only discretizing the fluid’s surface as triangles and vertices. A surface-based simulation for the fluid part is employed, and a boundary element method is utilized for the magnetic part. The magnetic forces are added as gradients of the magnetic energy defined on the fluid’s surface. The second approach has to solve significantly fewer unknowns in the underlying equations, and uses a more accurate surface tension model compared to the radial basis function approach. The proposed methods are able to reproduce a series of characteristic phenomena of magnetic fluids, both qualitatively and in some cases even quantitatively which leads to a better understanding of such kind of materials. The boundary element method employed in the second part shows advantages beyond ferrofluids. In the third part of this thesis, a boundary element method is coupled with a particle-based fluid simulator for ocean simulation. The wavy motion of the ocean is simulated using large triangle meshes, while water splashes are simulated using particles. This approach is much more efficient in terms of computation time and memory consumption.
  • Towards Affective Vision and Language

    Haydarov, Kilichbek (2021-11-30) [Thesis]
    Advisor: Elhoseiny, Mohamed
    Committee members: Wonka, Peter; Michels, Dominik
    Developing intelligent systems that can recognize and express human affects is essential to bridge the gap between human and artificial intelligence. This thesis explores the creative and emotional frontiers of artificial intelligence. Specifically, in this thesis, we investigate the relation between the affective impact of visual stimuli and natural language by collecting and analyzing a new dataset called ArtEmis. Furthermore, capitalizing on this dataset, we demonstrate affective AI models that can emotionally talk about artwork and generate them given their affective descriptions. In text-to-image generation task, we present HyperCGAN: a conceptually simple and general approach for text-to-image synthesis that uses hypernetworks to condition a GAN model on text. In our setting, the generator and the discriminator weights are controlled by their corresponding hypernetworks, which modulate weight parameters based on the provided text query. We explore different mechanisms to modulate the layers depending on the underlying architecture of a target network and the structure of the conditioning variable.
  • Computational Challenges in Sampling and Representation of Uncertain Reaction Kinetics in Large Dimensions

    Almohammadi, Saja M. (2021-11-29) [Dissertation]
    Advisor: Knio, Omar
    Committee members: Hoteit, Ibrahim; Farooq, Aamir; Alexanderian, Alen
    This work focuses on the construction of functional representations in high-dimensional spaces.Attention is focused on the modeling of ignition phenomena using detailed kinetics, and on the ignition delay time as the primary quantity of interest (QoI). An iso-octane air mixture is first considered, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log-uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was first generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parametrizing the uncertain rates. We explored two avenues, namely sparse regression (SR) using LASSO, and a coordinate transform (CT) approach. Preconditioned variants of both approaches were also considered, namely using the logarithm of the ignition delay time as QoI. A global sensitivity analysis is performed using the representations constructed by SR and CT. Next, the tangent linear approximation is developed to estimate the sensitivity of the ignition delay time with respect to individual rate parameters in a detailed chemical mechanism. Attention is focused on a gas mixture reacting under adiabatic, constant-volume conditions. The approach is based on integrating the linearized system of equations governing the evolution of the partial derivatives of the state vector with respect to individual random variables, and a linearized approximation is developed to relate the ignition delay sensitivity to the scaled partial derivatives of temperature. In particular, the computations indicate that for detailed reaction mechanisms the TLA leads to robust local sensitivity predictions at a computational cost that is order-of-magnitude smaller than that incurred by finite-difference approaches based on one-at-a-time rate parameters perturbations. In the last part, we explore the potential of utilizing TLA-based sensitivities to identify active subspace and to construct suitable representations. Performance is assessed based contrasting experiences with CT-based machinery developed earlier.
  • Domain-Aware Continual Zero-Shot Learning

    Yi, Kai (2021-11-29) [Thesis]
    Advisor: Elhoseiny, Mohamed
    Committee members: Wonka, Peter; Ghanem, Bernard; Michels, Dominik
    We introduce Domain Aware Continual Zero-Shot Learning (DACZSL), the task of visually recognizing images of unseen categories in unseen domains sequentially. We created DACZSL on top of the DomainNet dataset by dividing it into a sequence of tasks, where classes are incrementally provided on seen domains during training and evaluation is conducted on unseen domains for both seen and unseen classes. We also proposed a novel Domain-Invariant CZSL Network (DIN), which outperforms state-of-the-art baseline models that we adapted to DACZSL setting. We adopt a structure-based approach to alleviate forgetting knowledge from previous tasks with a small per-task private network in addition to a global shared network. To encourage the private network to capture the domain and task-specific representation, we train our model with a novel adversarial knowledge disentanglement setting to make our global network task-invariant and domain-invariant over all the tasks. Our method also learns a class-wise learnable prompt to obtain better class-level text representation, which is used to represent side information to enable zero-shot prediction of future unseen classes. Our code and benchmarks are made available at https://zero-shot-learning.github.io/daczsl.
  • Control and Estimation for Partial Differential Equations and Extension to Fractional Systems

    Ghaffour, Lilia (2021-11-29) [Dissertation]
    Advisor: Laleg-Kirati, Taous-Meriem
    Committee members: Gomes, Diogo A.; Knio, Omar; Sun, Shuyu; Diagne, Mamadou
    Partial differential equations (PDEs) are used to describe multi-dimensional physical phenomena. However, some of these phenomena are described by a more general class of systems called fractional systems. Indeed, fractional calculus has emerged as a new tool for modeling complex phenomena thanks to the memory and hereditary properties of fraction derivatives. In this thesis, we explore a class of controllers and estimators that respond to some control and estimation challenges for both PDE and FPDE. We first propose a backstepping controller for the flow control of a first-order hyperbolic PDE modeling the heat transfer in parabolic solar collectors. While backstepping is a well-established method for boundary controlled PDEs, the process is less straightforward for in-domain controllers. One of the main contributions of this thesis is the development of a new integral transformation-based control algorithm for the study of reference tracking problems and observer designs for fractional PDEs using the extended backstepping approach. The main challenge consists of the proof of stability of the fractional target system, which utilizes either an alternative Lyapunov method for time FPDE or a fundamental solution for the error system for reference tracking, and observer design of space FPDE. Examples of applications involving reference tracking of FPDEs are gas production in fractured media and solute transport in porous media. The designed controllers, require knowledge of some system’s parameters or the state. However, these quantities may be not measurable, especially, for space-evolving PDEs. Therefore, we propose a non-asymptotic and robust estimation algorithm based on the so-called modulating functions. Unlike the observers-based methods, the proposed algorithm has the advantage that it converges in a finite time. This algorithm is extended for the state estimation of linear and non-linear PDEs with general non-linearity. This algorithm is also used for the estimation of parameters and disturbances for FPDEs. This thesis aims to design an integral transformation-based algorithm for the control and estimation of PDEs and FDEs. This transformation is defined through a suitably designed function that transforms the identification problem into an algebraic system for non-asymptotic estimation purposes. It also maps unstable systems to stable systems to achieve control goals.

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