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  • Xeno-pumice from Harrat Rahat: Understanding magma-crust interaction

    Garcia, Evelyn R. Garcia Paredes (2022-07-28) [Thesis]
    Advisor: Van der Zwan, Froukje M.
    Committee members: Jonsson, Sigurjon; Troll, Valentin R; van Buchem, Frans
    “Xeno-pumice” describes a pumice-like material, high in silica content and vesiculation, found as a xenolith in a more mafic rock. A xeno-pumice is an indicator of magma-crust interaction; however, the origin, nature, and processes behind this xenolith are still debated. Xeno-pumice has been described in a few places worldwide, including the Canary Islands in Spain, Indonesia, Iceland, the USA, Chile and Mexico. This thesis, for the first time, presents and analyzes the mineralogy, textural features, whole-rock geochemistry (major and trace element), and oxygen isotopes of xeno-pumice samples found in Harrat Rahat, Saudi Arabia. Harrat Rahat is a volcanic field whose last eruption was in 1256A.D. and reached the outskirts of Madinah, one of the main cities in Saudi Arabia. Harrat Rahat is characterized by a wide range of volcanic products: from basalts to trachyte. Previous studies suggested that this chemical variation has its source in the mantle and minor crustal contamination; however, the xeno-pumice samples found indicate magma-crust interaction. Thus, in this thesis, the crust-melt interaction hypothesis is addressed as a process that could modify the composition of the melt and thus the resulting volcanic products and eruptive style of the volcanic field. Indeed, the chemistry and oxygen isotope values of the studied volcanic rocks show a variation in composition, which is suggested to be the consequence of crust-melt interaction. The petrology, chemistry and oxygen isotope values suggest that the melt interacted with either the metamorphosed plutonic portion of the upper Arabian crust or with (meta-) sediments below Harrat Rahat. Finally, this thesis proved that magma-crust interaction occurred at Harrat Rahat, which has important implications for interpreting eruption mechanisms and mantle sources.
  • Establishment of 3D culture protocols for the maintenance and expansion of human pluripotent stem cell aggregates in a low scale platform and in the DASbox® Mini-Bioreactor System

    Hernandez-Bautista, Carlos Alberto (2022-07-27) [Thesis]
    Advisor: Adamo, Antonio
    Committee members: Merzaban, Jasmeen; Ibrahim, Leena Ali
    The human Embryonic Stem Cells (hESCs) and human induced Pluripotent Stem Cells (hiPSCs) have offered numerous advantages including but not limited to model diseases, high-throughput drug screening, and regenerative purposes. However, the employment of monolayer cultures has not been sufficient to mimic the in vivo stem cells niche. Thus, three-dimensional suspension cultures have helped us to advance our knowledge and ease the development of the human organs’ counterparts, commonly referred as organoids. Currently, the challenge is the generation of homogenous and reproducible human Pluripotent Stem Cell (hPSC) aggregates, the basic cellular unit to derive organoids. To date, the Ultra-Low Attachment (ULA) 6-well plates have been routinary used for the hPSC aggregates formation, which mainly relies on the inhibition of the Rho-associated kinase (ROCK) pathway resulting in the enhancement of cell survival coming from cryopreserved stocks or when passaging. However, little is known in this regard when analyzing the aggregate formation of hPSCs with two widely used compounds: RevitaCellTM Supplement and Y27632. Importantly, due to the high demand required from the regenerative medicine, I aimed to upscale the hPSC aggregates production in the DASbox® Mini-Bioreactor System. In this thesis, I established protocols for the hPSC aggregates formation by using two different types of media in two platforms being the ULA 6-well plates and the DASbox® Mini-Bioreactor System. In addition, I demonstrated that monolayer confluence cultures before single cell inoculations are paramount for the formation of bona fide hPSC aggregates in healthy and X aneuploid hiPSCs, precisely two hESCs and five hiPSCs.
  • Performance Analysis of Space-Air-Ground Integrated Networks: Stochastic Geometry Approaches

    Tian, Yu (2022-07-27) [Dissertation]
    Advisor: Alouini, Mohamed-Slim
    Committee members: Al-Naffouri, Tareq Y.; Gao, Xin; Wong, Kai-Kit
    In the vision of 6th generation (6G) communication systems, the space-air-ground integrated network (SAGIN) leverages space/aerial platforms at various altitudes, such as satellites (S), high altitude platforms (HAPs), unmanned aerial vehicles (UAVs), and tethered balloons, to provide large-capacity and ubiquitous global connectivity. In this dissertation, by using stochastic geometry, we analyze the performance of SAGIN from three aspects: satellite-UAV down-link network, the space-aerial up-link and the aerial-ground up-link networks. Firstly, we investigate the down-link performance of a dual-hop cooperative satellite-UAVs communication system in which S sends information to a group of UAV cluster headers (CHs) and the CHs decode-and-forward the received signals to the UAVs uniformly distributed around them within a specific distance. The positions of the CHs follow the three-dimensional (3D) Matérn hard-core point processes type-II. We study the coverage probabilities (CPs) of the S-CH, the CH-UAV, and the end-to-end links of this network. Secondly, we analyze the up-link performance of a satellite-aerial communication system including a geostationary S, a target aircraft (TA), and a set of interference aircraft (IA). Specifically, TA sends signals to S and IA generate interference. Considering the trajectory, hierarchy, and safety distance of the aircraft's flight routes, we propose a 3D stacked Poisson line hardcore point process (PLHCP) to describe the aerial locations of IA. We also propose two approximations, namely, equi-dense model and discretization model, to overcome the intractability of the stacked PLHCP and study the up-link CP of this system. Moreover, we investigate the CP of the aviation use case with predefined flight altitudes. At last, we utilize PLHCP to model the locations of vehicles on the roads and study the performance of vehicular ad-hoc networks (VANETs) by equi-dense and discretization models. To this end, the cellular vehicle-to-infrastructure network is characterized in terms of distances distribution, association probabilities, and CP. We further extend the system to 3D cellular vehicle-to-UAV communications where the receiver is a UAV heaving in the sky and investigate its CP. Numerical results and Monte Carlo simulations are presented to validate the correctness and accuracy of our derived mathematical models for these aforementioned communication systems.
  • Evaluative screening of kinetic models for simulating the performances of oxidative coupling of methane catalysts

    Gobouri, Abdullah (2022-07-27) [Thesis]
    Advisor: Castaño, Pedro
    Committee members: Ruiz-Martinez, Javier; Hauser, Charlotte
    In this work, multiple kinetic models have been screened as potential candidates for simulating the performances of three oxidative coupling of methane (OCM) catalysts. Two of the proposed models were subjected to testing and optimization. The types of models screened covered both kinetic and microkinetic type models, i.e., radical omitting and radical considering. Some of the models only accounted for catalytic heterogeneous pathways, while others have expanded on the homogeneous gas-phase mechanism of the OCM reaction. The optimization process was carried out in MATLAB® R2020a using an error minimization tool. The range of experimental conditions examined was as follows: 740–800◦C, 100 kPa, 2–4 CH4/O2 ratio, 1–6 gcat h molC –1 spacetime. The results show successful optimization of both models as well as discrepancies in terms of their performances in predicting experimentally obtained values of CH4 and O2 conversions, as well as selectivities towards COx and C2+ products. While a kinetic model served as an easy option to optimize, it expressed limits in terms of achievable performance, mainly failing to simulate experimental runs conducted at low spacetimes. A microkinetic model on the other hand, managed to simulate all experimental conditions, with less accuracy towards COx species and much greater computational demand.
  • Sars-Cov-2 Intra-Host Evolution in Immunocompromised Patients for the Emergence of Variants of Concerns, Including Omicron.

    Bantan, Azari I. (2022-07-21) [Thesis]
    Advisor: Gojobori, Takashi
    Committee members: Mineta, Katsuhiko; Pain, Arnab
    Unexpected high mutations detected in new emerging variants of concern (VOCs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), especially in the case of omicron, raises concerns and efforts to understand their evolutionary trajectory. Several hypotheses have been discussed in literature to conceptualize the source of their emergence, including intra-host viral evolution in immunocompromised patients. These patients grant opportunities for the emergence of new variants through a persisting virus winning against host immunity, and selection for viral mutations driven by treatment interventions. VOCs have in common high mutation rate exceeding the average rate of 1-2 mutations per month. Not many studies have investigated the evolutionary rate of SARS-CoV-2 in immunocompromised candidates. Therefore, the purpose of this study is to reveal potential mechanisms underlying the emergence of VOCs by exploring substitution rate of SARS-CoV-2 genomes from surveyed COVID-19 immunocompromised patient’s studies. First, SARS-CoV-2 genome sequences were collected at sequential time series throughout host infection, which were reported in the previous studies. Filtration criteria was applied to reanalyze patients with prolonged infection documented for ≥ 2 months, and comprehensive sequenced samples for ≥ 6 time points. Then, phylogenetic analysis was conducted using Nextclade (, followed by mutation rate analysis using two substantial similar approaches to calculate the rate in i) substitutions per month and ii) substitutions per site (per year). The mutation tendency of SARS-CoV-2 in immunocompromised hosts was compared to reported VOCs, particularly to omicron. The highest observed mutation rate accounted for approximately 2.2 mutations per month, which is higher than the average rate. High mutation rate was due to prolonged infection and selection pressure by treatment interventions (i.e., convalescent plasma and antibodies). Here, higher rate of intra-host viral evolution in immunocompromised patients is detected, potentially leading to the emergence of VOC. Hence, this research highlights the need for sequencing efforts in high-risk individuals, updating treatment strategies along with further analysis on adaptive mutants pronounced due to intra-host evolution. Together, such findings provide an ultimate synergy for future public health guidelines and infection control measures.
  • High-Performance Scientific Applications Using Mixed Precision and Low-Rank Approximation Powered by Task-based Runtime Systems

    Alomairy, Rabab M. (2022-07-20) [Dissertation]
    Advisor: Keyes, David E.
    Committee members: Moshkov, Mikhail; Hadwiger, Markus; Ltaief, Hatem
    To leverage the extreme parallelism of emerging architectures, so that scientific applications can fulfill their high fidelity and multi-physics potential while sustaining high efficiency relative to the limiting resource, numerical algorithms must be redesigned. Algorithmic redesign is capable of shifting the limiting resource, for example from memory or communication to arithmetic capacity. The benefit of algorithmic redesign expands greatly when introducing a tunable tradeoff between accuracy and resources. Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and to reduce their execution time. Two “universes” of algorithmic innovations have emerged to improve computations by orders of magnitude in capacity and runtime. Each introduces a hierarchy, of rank or precision. Tile Low-Rank approximation replaces blocks of dense operator with those of low rank. Mixed precision approximation, increasingly well supported by contemporary hardware, replaces blocks of high with low precision. Herein, we design new high-performance direct solvers based on the synergism of TLR and mixed precision. Since adapting to data sparsity leads to heterogeneous workloads, we rely on task-based runtime systems to orchestrate the scheduling of fine-grained kernels onto computational resources. We first demonstrate how TLR permits to accelerate acoustic scattering and mesh deformation simulations. Our solvers outperform the state-of-art libraries by up to an order of magnitude. Then, we demonstrate the impact of enabling mixed precision in bioinformatics context. Mixed precision enhances the performance up to three-fold speedup. To facilitate the adoption of task-based runtime systems, we introduce the AL4SAN library to provide a common API for the expression and queueing of tasks across multiple dynamic runtime systems. This library handles a variety of workloads at a low overhead, while increasing user productivity. AL4SAN enables interoperability by switching runtimes at runtime, which permits to achieve a twofold speedup on a task-based generalized symmetric eigenvalue solver.
  • De novo genome-scale prediction of protein-protein interaction networks using ontology-based background knowledge

    Niu, Kexin (2022-07-18) [Thesis]
    Advisor: Hoehndorf, Robert
    Committee members: Inal, Sahika; Moshkov, Mikhail
    Proteins and their function play one of the most essential roles in various biological processes. The study of PPI is of considerable importance. PPI network data are of great scientific value, however, they are incomplete and experimental identification is time and money consuming. Available computational methods perform well on model organisms’ PPI prediction but perform poorly for a novel organism. Due to the incompleteness of interaction data, it is challenging to train a model for a novel organism. Also, millions to billions of interactions need to be verified which is extremely compute-intensive. We aim to improve the performance of predicting whether a pair of proteins will interact, with only two sequences as input. And also efficiently predict a PPI network with a proteome of sequences as input. We hypothesize that information about cellular locations where proteins are active and proteins' 3D structures can help us to significantly improve predict performance. To overcome the lack of experimental data, we use predicted structures by AlphaFold2 and cellular locations by DeepGoPlus. We believe that proteins belonging to disjoint biological components have very little chance to interact. We manually choose several disjoint pairs and further confirmed it by experimental PPI. We generate new no-interaction pairs with disjoint classes to update the D-SCRIPT dataset. As result, the AUPR has improved by 10% compared to the D-SCRIPT dataset. Besides, we pre-filter the negatives instead of enumerating all the potential PPI for de-novo PPI network prediction. For E.coli, we can pass around a million negative interactions. To combine the structure and sequence information, we generate a graph for each protein. A graph convolution network using Self-Attention Graph Pooling in Siamese architecture is used to learn these graphs for PPI prediction. In this way, we can improve around 20% in AUPR compared to our baseline model D-SCRIPT.
  • Water Effect on the Methanol to Olefin Conversion over SSZ-13 Catalyst with an Operando Spectroscopy

    Alsindi, Mohammed (2022-07-18) [Thesis]
    Advisor: Ruiz-Martinez, Javier
    Committee members: Pinnau, Ingo; gomez-cabrero, David
    For more than 50 years, the methanol to olefins (MTO) reaction remains to be a hot topic within the catalysis community. The recent discoveries about it and the industrial implementation made it even receive more attention. The best way this process can be used is by hydrogenation of CO2 to make methanol and undergo after that the MTO reaction. This method will save energy, be more environmentally friendly, and be sustainable, but it requires advancement regarding carbon capture. The purpose of this paper is to understand the effect of water on SSZ-13 commercial zeolite when it is used for MTO reaction by a combination of gas chromatography (GC) analysis and operando UV – vis spectroscopy. It was observed that water with a ratio of 2:1 methanol to water would increase the lifetime of the catalyst from 3 h to 6.5 h, and the ratio of 1:1 would increase it to 9 h. However, a higher amount of water hadn’t been analyzed, but theoretically, it would cause dealumination to the zeolite invoking a different type of deactivation. This increase in catalyst lifetime was first due to the competitive adsorption between water and methanol; leading to a lower methanol reactivity toward methoxide formation. Second, because of the competition between water and propylene, it resulted in a longer induction period and a delay in the formation of hydrocarbon pool. Hence, less coke was formed from the reaction and more species were able to diffuse into the inner pores. Also, it was observed that ethylene selectivity increased with the addition of water to the feed. The UV-vis analysis proved the longer induction period and showed the formation of more species due to that. The deactivating materials were identified to be polyaromatic carbocation and phenanthrene, while the main activating species was methylated naphthalene carbocation. In addition, multiple characterization techniques, such as nitrogen physisorption, ammonia TPD, and SEM, were performed to understand the nature of the catalyst. It was found that it has weak and strong Bronsted acid sites, BET surface area of 665.7 m2/g, and crystal size of about 0.5 – 2 µm.
  • Benthic Habitat Mapping of Thuwal’s Reefs Using High-Resolution Acoustic Technologies and Imaging Data

    Watts, Marta A. Ezeta (2022-07-14) [Thesis]
    Advisor: Benzoni, Francesca
    Committee members: Berumen, Michael L.; Volker, Vahrenkamp
    Remote sensing studies based on satellite and aerial imagery have improved our understanding of the morphology and distribution of several shallow reefs along the Red Sea Arabian coast and of the benthic assemblages associated to them (Bruckner et al., 2011; Bruckner et al., 2012; Rowlands et al., 2016). However, data concerning the deeper benthic assemblages' composition and spatial distribution in the central Red Sea are still missing. Using high-resolution acoustic technology and an underwater remotely operated vehicle (ROV), we aim to map, describe, and classify the reefs found in Thuwal's coastal area, filling the information gap by producing the first benthic habitat map of this area and making progress towards the evaluation of shallow and upper mesophotic benthic resources in the Saudi Arabian Red Sea. High-resolution acoustic data was collected using a multibeam echosounder system, which generated a bathymetric model. Based on this, the seafloor features were classified into 12 morphotypes following a visual assessment. Based on the morphotypes classification, 28 sites were visually selected for ground-truthing data acquisition and characterization of the substrate and benthic assemblages using a remotely operated vehicle equipped with an ultra-short baseline (USBL) positioning system. With the information obtained from the bathymetry data and the ROV video transects, a Top-Down approach in which we analyzed, categorized, and classified the data was used to create Thuwal's reefs benthic habitat map in which 23 different benthic habitat types were identified. This research uncovered previously poorly studied reef morphologies in the Red Sea and their associated benthic assemblages. Moreover, this work will help improve the understanding of the spatial distribution of benthic communities located on Thuwal's reefs, giving a baseline with the potential to provide fundamental information that can be used for mapping, management, conservation, and future research at other Red Sea reef sites in Saudi Arabia.
  • Fast, Robust, Iterative Riemann Solvers for the Shallow Water and Euler Equations

    Muñoz-Moncayo, Carlos (2022-07-12) [Thesis]
    Advisor: Ketcheson, David I.
    Committee members: Tzavaras, Athanasios; Truscott, T. T.
    Riemann problems are of prime importance in computational fluid dynamics simulations using finite elements or finite volumes discretizations. In some applications, billions of Riemann problems might need to be solved in a single simulation, therefore it is important to have reliable and computationally efficient algorithms to do so. Given the nonlinearity of the flux function in most systems considered in practice, to obtain an exact solution for the Riemann problem explicitly is often not possible, and iterative solvers are required. However, because of issues found with existing iterative solvers like lack of convergence and high computational cost, their use is avoided and approximate solvers are preferred. In this thesis work, motivated by the advances in computer hardware and algorithms in the last years, we revisit the possibility of using iterative solvers to compute the exact solution for Riemann problems. In particular, we focus on the development, implementation, and performance comparison of iterative Riemann solvers for the shallow water and Euler equations. In a one-dimensional homogeneous framework for these systems, we consider several initial guesses and iterative methods for the computation of the Riemann solution. We find that efficient and reliable iterative solvers can be obtained by using recent estimates on the Riemann solution to modify and combine well-known methods. Finally, we consider the application of these solvers in finite volume simulations using the wave propagation algorithms implemented in Clawpack.
  • Estimation of Mercury Injection Capillary Pressure (MICP) from the Nuclear Magnetic Resonance (NMR) exponential decay with the Machine Learning (ML) Neural Network (NN) approach

    Ugolkov, Evgeny A. (2022-07-09) [Thesis]
    Advisor: Hoteit, Hussein
    Committee members: Santamarina, Carlos; Ahmed, Shehab
    Information about the capillary pressure has a wide range of applications in the petroleum industry, such as an estimation of irreducible water saturation, calculation of formation absolute permeability, determination of hydrocarbon-water contact and the thickness of the transition zone, evaluation of the seal capacity, and an estimation of relative permeability. All the listed parameters in the combination with petrophysical features, pressures, and fluid properties allow us to evaluate the economic viability of the well or the field overall. For this reason, capillary pressure curves are of great importance for petroleum engineers working on any stage of the field development: starting from exploration and finishing with production stages. Nowadays, capillary pressure experiments are provided either in the lab on the plugs of the rocks, either in the well on the certain stop points with the formation tester tools on the wire or tubes. Core extraction and formation testing are both laborious, expensive, and complicated processes since the newly-drilled well remain in the risky uncased condition during these operations, and for this reason, usually the listed works are provided in the exploration wells only. Afterward, the properties obtained from the exploration wells are assumed to be the same for the extraction or any other kinds of wells. Therefore, these days petroleum engineers have limited access to the capillary pressure curves: the modern tests are provided on the limited points of formation in the limited number of wells. An extension of capillary pressure measurements in the continuous mode for every well will dramatically expand the abilities of modern formation evaluation and significantly improve the field operation management by reducing the degree of uncertainty in the decision-making processes. This work is the first step toward continuous capillary pressure evaluation. Here we describe the procedure of finding the correlation between the results of the lab Nuclear Magnetic Resonance (NMR) experiment and lab Mercury Injection Capillary Pressure (MICP) measurements. Both experiments were provided on the 9 core plugs of the sandstone. Afterward, a Machine Learning (ML) algorithm was applied to generate additional samples of the porous media with different petrophysical properties representing the variations of the real cores of available sandstones. Overall, 405 additional digital rock models were generated. Thereafter, the digital simulations of MICP and NMR experiments were provided on the generated database of digital rocks. All the simulations were corrected for limited resolution of the CT scan. Based on the created database of experiments, we implemented a ML algorithm that found a correlation between the NMR echo data and MICP capillary pressure curves. Obtained correlation allows to calculate capillary pressure curve from the NMR echo data. Since NMR logging may be implemented in every well in the continuous mode, an extension of the created technique provides an opportunity for continuous estimation of capillary pressure for the whole logging interval. This extension is planned as future work.
  • An Experimental Investigation of Soot Formation in Laminar Inverse and Normal Diffusion Flames at Elevated Pressure

    Alsheikh, Ibrahim (2022-07-07) [Thesis]
    Advisor: Roberts, William L.
    Committee members: Sarathy, Mani; Hoteit, Hussein
    Hydrogen production from autothermal reforming (ATR) with Carbon Capture Utilization and Storage (CCUS) is gaining traction as prospect for a blue hydrogen economy. ATR is susceptible to catalyst poisoning and degradation from soot formation, which decrease H2 yield. In this work soot formation was examined thoroughly in conditions close to ATR, using an oxygen rich inverse diffusion flame (IDF) burner at elevated pressure. Normal diffusion flames (NDF) were also investigated against the same conditions to ultimately be compared alongside IDF. In NDF, soot formation and oxidation happen simultaneously, while in IDF soot oxidation is ignorable. Primary fuel was CO2 diluted methane, and the oxidizer stream has a 70%-by-mol O2 (30% N2) concentration. OH* chemiluminescence was used to find flame height against key parameters, PAH concentration and soot volume fraction were captured using Laser Induced Fluorescence (LIF) and Laser Induced Incandescence (LII) respectively. Key findings in this work were the dissimilarities between IDF and NDF against pressure and the effects of varying flame constituents on flame height in IDF.
  • Mean-Field Games on Networks and Wardrop Equilibria

    Saleh, Fatimah H. Al (2022-07-06) [Dissertation]
    Advisor: Gomes, Diogo A.
    Committee members: Markowich, Peter A.; Santamarina, Carlos; Di Fazio, Giuseppe
    This thesis consists of three main parts. In the first part, we discuss first-order stationary mean-field games (MFGs) on networks. We derive the mathematical formulation of first-order MFGs on networks with particular emphasis on the conditions at the vertices both for the Hamilton-Jacobi equation and for the transport equation. Then, we review the current method, which, for the stationary case, allows us to convert the MFG into a system of algebraic equations and inequalities. Finally, we discuss in more detail the travel cost and its properties. In the second part, we discuss the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary MFGs. First, we build the Wardrop model on networks. Second, we show how to convert the MFG model into a Wardrop model. Next, we recover the MFG solution from the Wardrop solution. Finally, we study the calibration of MFGs with Wardrop travel cost problems. In the third part, we explain the algorithm for solving the algebraic system associated with the MFG numerically, then, we present some examples and numerical results.
  • Biophysical Characterization of the BIRD Complex and their Mode of Interaction

    Wang, Luyao (2022-07-06) [Thesis]
    Advisor: Arold, Stefan T.
    Committee members: Habuchi, Satoshi; Blilou, Ikram
    In Arabidopsis thaliana, the development and the defense system are precisely controlled by some proteins to allocate energy and resources as needed. JASMONATE-ZIM domain 3 protein is the repressor of the jasmonic acid defense pathway. JACKDAW (JKD), SHORTSHOOT (SHR), and SCARECROW (SCR) bind together to form the BIRD complex, which regulates root patterning. The transcription factor Teosinte branched1/Cycloidea/Proliferating cell factor 14 (TCP14) also regulates plant development. Recent data shows that JAZ3 and TCP14 interact with JKD and may form a ternary complex, which reveals the study of the five proteins mentioned above may help to understand how defense signals are interpreted during plant growth. The interactions of these five proteins provide a theoretical base to maximize plant fitness and increase crop yield. Using protein purification, microscale thermophoresis, isothermal titration calorimetry, negative staining, X-ray crystallography in this project, we identified JKD interacted with JAZ3, and JKD interacted with TCP14, but they could not form a ternary complex in vitro; SHR/SCR interacted with JAZ3. Those binding results suggests TCP14 and SHR/SCR may have very similar binding site of JKD, and JAZ3 may guide the degradation of the BIRD complex. In structural studies, we resolved the 2D class average that showed the outline of the BIRD complex and it potentially helped to identify how JKD bound to DNA. We also determined the crystal structure of the TCP14 domain, which was an intertwined dimer that possibly uses arginine residues in the N terminus to interact with DNA. These interaction and structure studies of the five proteins provide the basis to understand how defense signals affect plant development.
  • Predicting the future high-risk SARS-CoV-2 variants with deep learning

    Chen, NingNing (2022-07-04) [Thesis]
    Advisor: Gao, Xin
    Committee members: Gojobori, Takashi; Wang, Di
    SARS-CoV-2 has plagued the world since 2019 with continuously emergence of new variants, resulting in repeated waves of outbreak. Although the countermeasures like vaccination campaign has taken worldwide, the sophisticated virus mutated to escape immune system, threatening the public health. To win the race with the virus and ultimately end the pandemic, we have to take one step ahead to predict how the SARSCoV-2 might evolve and defeat it at the beginning of a new wave. Hence, we proposed a deep learning based framework to first build a deep learning model to shape the fitness landscape of the virus and then use genetic algorithm to predict the high-risk variants that might appear in the future. By combining pre-trained protein language model and structure modeling, the model is trained in a supervised way, predicting the viral transmissibility and antibodies escape ability to eight antibodies simultaneously. The prevenient virus evolution trajectory can be largely recovered by our model with high correlation to their sampling time. Novel mutations predicted by our model show high antibody escape through in silico simulation and overlapped with the mutations developed in prevenient infected patients. Overall, our scheme can provide insights into the evolution of SARS-CoV-2 and hopefully guide the development of vaccination and increase the preparedness.
  • Topological Spin Textures in Emerging Layered Magnets

    Zhang, Chenhui (2022-07-03) [Dissertation]
    Advisor: Zhang, Xixiang
    Committee members: Zhou, Shengqiang; Li, Xiaohang; Lanza, Mario
    In the past few years, magnetic two-dimensional (2D) materials and topological spin textures have become two of the hottest topics in the fields of material science and spintronics. Creating topological spin textures in 2D magnets naturally becomes very attractive, because one may take advantages of both to construct novel spintronic devices. In this dissertation, we investigated the structural and magnetic properties of several emerging 2D magnets and successfully observed topological spin textures in some of them. First, we synthesized quasi-van der Waals (vdW) ferromagnet Fe0.26TaS2. Its critical behavior was systematically studied by measuring the magnetization around the ferromagnetic to paramagnetic phase transition temperature. The results reveal that the spin coupling inside Fe0.26TaS2 crystal is of the three-dimensional Heisenberg type with long-range magnetic interaction and that the exchange interaction decays with distance as J(r) ~ r −4.71. Second, we synthesized vdW ferromagnet Fe5−δGeTe2 crystals and observed dipole skyrmions with unconventional helicity polarization in the nanoflake samples. We demonstrated that the short-range order of Fe split sites in Fe5−δGeTe2 results in a localized Dzyaloshinskii–Moriya interaction contribution, which breaks the degeneracy of the opposite helicities and leads to the helicity polarization. Moreover, the topological spin textures can persist up to nearly room temperature (~273 K). This work provides new insights into the skyrmion topology in 2D materials and reveals great potentials of Fe5−δGeTe2 for spintronic applications. Third, we successfully synthesized Cr1/3TaS2 single crystals, a quasi-vdW helimagnet. We found that one-dimensional nontrivial magnetic solitons can be created below its Curie temperature. The coupling of the strong spin–orbit interaction from TaS2 and the chiral arrangement of the magnetic Cr ions evoke a robust Dzyaloshinskii-Moriya interaction. A magnetic helix having a short spatial period of ~25 nm was observed in Cr1/3TaS2 via Lorentz transmission electron microscopy. Moreover, chiral solitons can be created under an in-plane magnetic field, and the soliton confinement and discretization effects were also observed. Our work introduces a new paradigm to soliton physics and provides an effective strategy for seeking novel 2D magnets.
  • Liquid Metal - Based Inertial Sensors for Motion Monitoring and Human Machine Interfaces

    Babatain, Wedyan (2022-07) [Dissertation]
    Advisor: Hussain, Muhammad Mustafa
    Committee members: Alouini, Mohamed-Slim; Saikaly, Pascal; Ma, Zhenqiang
    Inertial sensing technologies, including accelerometers and gyroscopes, have been invaluable in numerous fields ranging from consumer electronics to healthcare and clinical practices. Inertial measurement units, specifically accelerometers, represent the most widely used microelectromechanical systems (MEMS) devices with excellent and reliable performance. Although MEMS-based accelerometers have many attractive attributes, such as their tiny footprint, high sensitivity, high reliability, and multiple functionalities, they are limited by their complex and expensive microfabrication processes and cumbersome, fragile structures that suffer from mechanical fatigue over time. Moreover, the rigid nature of beams and spring-like structures of conventional accelerometers limit their applications for wearable devices and soft-human machine interfaces where physical compliance that is compatible with human skin is a priority. In this dissertation, the development of novel practical resistive and capacitive-type inertial sensors using liquid metal as a functional proof mass material is presented. Utilizing the unique electromechanical properties of liquid metal, the novel inertial sensor design confines a graphene-coated liquid metal droplet inside tubular and 3D architectures, enabling motion sensing in single and multiple directions. Combining the graphene-coated liquid metal droplet with printed sensing elements offers a robust fatigue-free alternative material for rigid, proof mass-based accelerometers. Resistive and capacitive sensing mechanisms were both developed, characterized, and evaluated. Emerging rapid fabrication technologies such as direct laser writing and 3D printing were mainly adopted, offering a scalable fabrication strategy independent of advanced microfabrication facilities. The developed inertial sensor was integrated with a programmable system on a chip (PSoC) to function as a stand-alone system and demonstrate its application for real-time- monitoring of human health/ physical activity and for soft human-machine interfaces. The developed inertial sensor architecture and materials in this work offer a new paradigm for manufacturing these widely used sensors that have the potential to complement the performance of their silicon-based counterparts and extend their applications.
  • Molecular Dynamics Modeling of Kaolinite Particles Aggregates and their interaction with Amorphous Glass Surfaces

    Volkova, Evgeniya (2022-07) [Dissertation]
    Advisor: Stenchikov, Georgiy L.
    Committee members: Schwingenschlögl, Udo; Sun, Shuyu; Qiao, Rui
    The most abundant atmospheric aerosol is mineral dust. Particles of dust are capable of traveling thousands of kilometers from their point of origin and eventually deposit, affecting vegetation, structures, transportation, and solar panel installations. A major dust storm could lift in the air 100 Mt of dust. These large-scale processes depend on dust size, density, and chemical composition, all defined at a submicron scale. As deposited dust reduces solar energy income, it is essential to understand dust particles' fundamental properties to enhance the efficiency of solar farms developing in desert regions to reduce reliance on fossil fuels. Although much research has been done in the last seven decades, the fundamentals of dust particle-to-particle interactions and particle-to-PV surfaces are still not fully understood and not well quantified. I developed a new procedure for obtaining, at the atomic level, finite kaolinite particles of hexagonal shape with complex chemistry. The finite kaolinite particle, 10 nm in diameter with pronounced edges and a platy hexagonal shape, was proven to be a minimum fundamental building block. I built associations using different particle preconditions. Random placements of building blocks can construct the larger particle associations. For the first time, I obtained all possible particle associations previously described in the literature using an atomic method. The critical initial angle for forming the aggregates was also found. As a macroscopic characteristic of packing, the packing density was calculated, and its value was compared between different particle associations. The amorphous glass surface was successfully modeled using the three-stage annealing method. The interaction force between kaolinite particle/associations and the amorphous glass surface was computed by the Mechanical approach introduced in the thesis.
  • Assessment of the Bacterial Growth Potential of Reverse Osmosis Produced Chlorinated Drinking Water

    Felix, Alejandra Ibarra (2022-07) [Thesis]
    Advisor: Vrouwenvelder, Johannes S.
    Committee members: Saikaly, Pascal; Burton, Jones; Farhat, Nadia
    Reverse Osmosis (RO) filtration is capable of producing high quality drinking water with an ultra-low nutrient level. Therefore, a very low bacterial growth potential (BGP). BGP is a key bioassay to evaluate microbial quality and the biological stability of drinking water. Current methods to assess BGP in drinking water need to be adapted to the wide variety of water types due to results could highly vary from each, providing unreliable insights to the biological stability of the assessed water. This study evaluates the application of an FCM-based BGP assay for RO produced chlorinated drinking water. The approach combines (i) the standardization of a quenching agent concentration, (ii) the impact of sample pre-treatment such as filtration and pasteurization on the BGP of RO produced chlorinated water, (iii) the effect of different inoculums (an indigenous community and a mixture with bottled water) on the bacterial growth and their longevity after being stored, (iv) the use of BGP to assess the performance of carbon filters in removing chlorine and (v) the use of BGP to assess the effect of the addition of magnesium on bacterial growth. The results showed that high concentrations of sodium metabisulphite (> 7.5 mg/L) decrease the pH levels of the water,thus, inhibiting bacterial growth. Filtration had a significant effect on BGP values (2.62 x10^5 intact cells/mL) in comparison to pasteurization (9.02 x 10^4 intact cells/mL), when compared to the control. Using a mixture of water types as inoculum might provide a better insight to bacterial growth potential in water due to a higher consumption of nutrients. BGP demonstrated to be a sensitive tool to test the performance of carbonfilters applied to remove chlorine and its applicability to evaluate the biological stability of RO produced chlorinated drinking water. The concentrations of magnesium chloride tested in this study did not have a significant effect on cell numbers.
  • The Effects of Gasoline Composition and Additive Concentration on the Lubricity of Gasoline Blends

    Al Ashkar, Youssef (2022-07) [Thesis]
    Advisor: Sarathy, Mani
    Committee members: Roberts, William L.; Szekely, Gyorgy
    Under current regulations, gasoline engines are facing lubricity and wear challenges that need to be met by enhanced gasoline lubricity. Gasoline lubricity can be enhanced by lubricity improvers such as heavy fatty acid methyl esters. This thesis presents the ‘High Frequency Reciprocating Rig’ (HFRR) tests carried out on a standardized tribological test rig as per a modified version of ASTM D6079, to account for the effects of volatility of gasoline. Testing 5 gasoline types (gasolines A-E) blended with 2 lubricity improver types (LI1-2) at 2 concentrations, 250 and 500 ppm, provided insights on the changes in lubrication behavior with different gasoline composition, LI type, and concentration. The gasoline types with higher aromatic content and average carbon number (lower volatility) resulted in less wear and better lubricity regardless of LI concentration. The highly aromatic gasoline “A” performed better with the fatty acid-based LI1. Gasolines “B-E”, which are less aromatic, resulted in less wear with the ester-based LI2. The decrease in wear volumes with LI2 was more pronounced with the highly volatile gasolines B and E. These insights were mainly challenged by the failure of some tests due to the high volatility of gasoline. To mitigate this effect and confirm the findings, less volatile gasoline surrogates were designed to mimic the composition of the gasoline types on functional group basis, and were blended with the same lubricity improvers, and then tested using the same method. This improved the results and showed that high aromaticity enhanced the lubricity of the gasoline blends, especially with fatty-acid based LI1, but degraded it beyond 50% aromatic content. The enhancement of lubricity with higher average carbon number was also highlighted. To create deeper understanding of the lubrication mechanisms involved, it is recommended to study the rheological properties of the blends, analyze the chemical composition of the deposits on the wear tracks, and repeat the tests with continuous supply of lubricant to further decrease the effect of gasoline volatility

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