VictorJuiz/thesis huggingface dataset:
Comparative Analysis of Chemical and Enzymatic Pathways to Improve Suitability of Industrial Lignins for Polyurethane Applications

The utilization of all biorefinery products and by-products, including lignin, will be a game-changer to optimize the biorefinery concept and make corresponding processes economically viable. Softwood kraft lignin (SKL) is a significant bioresource that could play an essential role in achieving a sustainable bio-based economy. Due to existing aliphatic and phenolic hydroxyl groups in lignin, it could be used as biobased polyol for polyurethane (PU) application. PUs represent a large class of polymers with urethane repeating units produced by reacting polyols and diisocyanates. Major hurdles to the valorization of kraft lignins include high heterogeneity, inaccessibility of hydroxyl groups for reaction with isocyanates, and low solubility in most organic solvents. As such, technologies to enhance kraft lignin homogeneity are particularly crucial to unlocking its enormous potential and entirely replace petroleum-based polyol in PUs. To overcome the bottlenecks in valorization of SKL for applications beyond energy production, chemoenzymatic methods were established and compared to propel our knowledge of effective ways to tailor lignin structure for PU resin applications. The first method focused on fragmentation of lignin structures using ionic liquid 1-butyl-3-methylimidazolium [BMIM]Br treatment to gain access to the existing hydroxyl groups. However, the expected compositional changes were not detected on SKL (Indulin-AT) after IL treatment, as confirmed by phosphorus nuclear magnetic resonance spectroscopy (31P NMR) and gel permeation chromatography (GPC) analysis. The second method was a one-step acetone fractionation of LignoBoost SKL that resulted in acetone soluble and insoluble fractions, offering components suitable to entirely replace phenol and polyol in phenolic and PU resins, respectively, without the need for further chemical modification of the lignin structure. Additionally, the possibility of using a cellulose-derived solvent in PU formulation was successfully tested. Finally, to eliminate organic solvents and mimic the oxidation of lignin phenolic compounds in nature, an alkaline tolerant laccase was used to graft acrylate monomer onto LignoBoost SKL. Enzymatic grafting was confirmed by Fourier-transform infrared spectroscopy and 1H NMR techniques, and was shown to enhance the reactivity of lignin as a polyol substitute in PU resin applications. The reported enzymatic and aqueous process presents an opportunity for the sustainable valorization of SKL.

Keywords: 31P NMR
hydroxyl
laccase
lignin
polyol
polyurethane
Advisor: Master, Emma R
Nejad, Mojgan
3D Antenna Placement for Cellular Communications

The evolution of wireless communications continues to be driven by the ever-increasing demand for higher capacity and throughput rates to enable new digital applications. While this demand creates bottlenecks across wireless systems, the limited availability of radiofrequency resources remains the most challenging of these bottlenecks. A key approach to circumventing limited spectrum availability employs antenna arrays to enable the transmission of higher data rates using high-gain beams using hybrid or phased-array MIMO antennas. Critical to the performance of antenna arrays is the placement and number of array elements, with most wireless systems employing a limited number of antenna elements arranged in a basic linear or two-dimensional geometry that limits the angular coverage of antenna elements. Nevertheless, such an approach for antenna element placement has limited scalability and thus fails to meet the needs of contemporary wireless communication systems. This thesis addresses this challenge by providing an effective and novel approach for antenna array structuring using Three-dimensional (3D) array structures. A detailed spatial and angular analysis of 3D antenna arrays is provided, and substantial gains are showcased for both single, MIMO and hybrid transmission setups. To maximize the performance of antenna arrays in general and 3D phased-MIMO transmission in particular, algorithms for the structuring of antenna arrays and selection of array elements being used for transmission/reception are introduced. Specifically, a distribution algorithm is introduced to distribute antenna elements in any defined space to maximize the achievable throughput rate along with a reduction algorithm to minimize energy consumption by utilizing the lowest possible number of available elements in any antenna array to achieve the required performance. Furthermore, the proposed approach and algorithms are shown to enable the optimization for substantial performance gains in various deployment setups of wireless cellular communication systems and paves the way for empowering various emerging applications such as various vehicular communication setups and deployments utilizing higher frequency bands.

Keywords: 5G
Algorithms
Antenna Arrays
LTE
MIMO
Optimization
Advisor: Sousa, Elvino
Synthesis, Surface Patterning and Self-assembly of Plasmonic Nanoparticles

Nanoparticles (NPs) have a broad range of applications. Nanoparticle properties are governed by their composition, dimensions, shapes and surface chemistry, as well as the assembly in clusters. This thesis describes three ways to control nanoparticle properties via (i) synthesis, (ii) surface patterning and (iii) self-assembly.As the synthesis of nanoparticles often involves multiple reagents, the optimization of reaction conditions is time- and labor-intensive, combining machine learning and microfluidics can lead to a self-driving platform for the accelerated optimization of nanoparticle synthesis along with low regent consumption. This thesis describes a machine learning-driven, oscillatory microfluidic platform for the synthesis of inorganic nanoparticles with targeted spectroscopic characteristics. Several machine learning algorithms were applied to study and optimize the effect of reaction conditions on the spectroscopic characteristics of the gold NPs. Surface-patterned nanoparticles are an emerging class of colloidal building blocks of functional hierarchical nanomaterials. This thesis describes a strategy for nanoparticle surface patterning by phase separation of incompatible polymer ligands. By varying the molar ratio of polystyrene and poly(ethylene glycol) polymer brushes on the surface of gold nanorods, several surface patterns have been generated, including a helicoidal pattern of polystyrene patches wrapping around the nanorods. The helicoidally patterned nanorods exhibited long-term colloidal stability in a good solvent. Furthermore, for nanoparticles surface-patterned with polystyrene patches, we studied temperature-dependent morphological transitions. The surface pattern was preserved upon heating up to 40 °C, while at 80 °C, the number of patches decreased and their contact angle with nanoparticle surface increased, due to the increased lateral mobility and coalescence of the patches. Ultimately the polymer ligands desorbed from the NPs, due to the enhanced solvent quality. Finally, the temperature-responsive self-assembly of nanoparticles end-grafted with polystyrene ligands was studied. Upon cooling the nanoparticle solution below the transition temperature, the nanoparticles assembled in clusters, while upon heating these clusters dissociated into individual nanoparticles. The self-assembly was reversible and reproducible, and the dimensions of nanoparticle clusters were controlled by the superposition of temperature and incubation time. The transition temperature was governed by the solvent quality for the polymer ligands and was tuned by varying solvent composition.

Advisor: Kumecheva, Eugenia EK
Layer-by-layer Design of Organic-carbon Composite Electrodes for Capacitive Charge Storage

Delivering clean and sustainable energy is currently a major challenge worldwide. Among the promising energy storage solutions are electrochemical capacitors (ECs), which have seen tremendous improvements recently, but suffer from their low energy densities and fabrication costs. A potential remedy to these issues is the development of electrodes that combine the two main charge storage mechanisms of ECs, namely electrical double-layer capacitance (EDLC) and pseudocapacitance. This combination has been successfully realized by depositing conducting polymers on carbon, which greatly increases the energy density of the resulting composites. The main shortcomings of these composites are their slower charge storage kinetics and instability, resulting from thick active layer. The aim of this thesis is to address the issues above, in the case of composite electrodes based on carbon nanotubes (CNT) modified with redox polyluminol (CpLum) and sulfonated porphyrin macrocycles (TPPS). This work is divided into three parts, the first part starts with a single layer approach to identify the advantages and shortcomings of the developed composites. Compared to bare CNT electrodes, the obtained CpLum-CNT and TPPS-CNT composites have enhanced capacitive charge storage abilities and good stability. The charge storage kinetics of the composite was studied to gauge the capacitive performance of said composites. In the second part of the thesis, the knowledge gained from the first part is leveraged to develop a TPPS-CpLum-CNT composite. This dual layer material exhibits enhanced capacitive charge and faster storage kinetics compared to the individual single layer-CNT. The last part of this work tackles the layer-by-layer (LbL) assembly of molybdovanadogermanic (GeMoV) polyoxometalates. The synergy between redox active CpLum and GeMoV along with the anchoring polycation on CNT give rise to several improvements, including improved capacitive behavior, faster storage kinetics, and a wider operating potential window. The fundamental aspects of the contributions above enabled the design and optimization of carbon-based composites via a feasible and scalable LbL assembly method through an in-depth understanding of the physico-chemical and electrochemical properties of the materials at hand. On top of obtaining higher energy and power densities for ECs, the approaches in this work can be extended to improve other electrochemical devices.

Advisor: Lian, Keryn
Robust Statistical Arbitrage: Trading Foreign Exchange Triplets and Trading Assets with Short Term Alphas

Decision making in a stochastic environment depends on the decision makers' models of the environment. To reach their goal, decision makers consider not only the randomness of the outcomes described by a certain probability law, but also their uncertainty of the probability law itself. We call such decision makers ambiguity averse. In the area of algorithmic trading, financial agents often trade multiple assets with some inherent price structure. The main goal of this work is to investigate how these agents behave when facing model uncertainty by considering two real-world algorithmic trading problems: trading foreign exchange triplets and trading short term alpha. In the trading foreign exchange (FX) triplets problem, an FX broker accepts trades from her clients, and hence needs to manage positions in multiple currencies by acquisition and liquidation. She admits that her model for FX rates may be misspecified and we explore the optimal strategy for her to liquidate a large position in an illiquid currency pair. We find that the ambiguity averse broker can improve her liquidation profit by trading a currency triplet consisting of the illiquid currency pair and two relatively liquid currency pairs. In the trading short term alpha problem, our trader can predict prices of N assets with M alpha signals. She is uncertain about the models of asset prices and alpha signals and wants to maximize her final trading wealth. We formulate the trader's stochastic control problem and solve it in closed form. We also calibrate our model to real market data and numerically demonstrate that our robust strategy can increase the mean and reduce the standard deviation of the trader's Profit and Loss (P).

Keywords: Algorithmic Trading
Ambiguity Aversion
Foreign Exchange
Short Term Alpha
Advisor: Jaimungal, Sebastian
Neutron Scattering Study of Magnetic Excitations in Quantum Magnets Bi2CuO4 and CoTiO3

Anisotropic spin interactions beyond simple Heisenberg models have been intensively studied in recent years as new routes for realizing exotic magnetic phases. Although theories of anisotropic interactions have been developed extensively, careful experimental studies that determine the forms and magnitude of these interactions in actual magnetic systems have been scarce. In this thesis, I present inelastic neutron scattering (INS) studies of magnetic excitations in two ordered antiferromagnets, Bi2CuO4 and CoTiO3, to elucidate their microscopic spin interactions. Bi2CuO4 is an easy-plane antiferromagnet with a tetragonal crystal structure. Despite many earlier studies using INS and other techniques, nature of low energy magnons and hence the form of anisotropic interactions in Bi2CuO4 have been controversial. Using higher resolution than all previous INS work, we determined the dispersions of the low energy magnons, which we used to construct an anisotropic spin model based on lattice symmetry. Remarkably, the anisotropic interactions in our model were shown to play a fundamental role by producing an in-plane anisotropy through a quantum order by disorder mechanism. This anisotropy was directly observed in a field-dependent neutron scattering measurement. CoTiO3 has an ilmenite structure with a stacked honeycomb lattice of Co2+ ions. We obtained its full energy-momentum resolved magnon spectrum, which we showed to be consistent with a simple XXZ model. A Dirac-cone like dispersion was discovered in CoTiO3, making it a promising candidate for realizing topological magnons. A high-resolution measurement revealed important features of the magnon dispersion, namely a zone center and a Dirac magnon gap, that can only be explained by anisotropic interactions beyond an XXZ model. Field dependence of these features was studied and used to constrain the forms of the anisotropic interactions. Higher energy spin-orbit exciton in CoTiO3 was studied as a function of temperature across the magnetic ordering transition. A strong temperature dependence was observed, which we attributed to a mixing between the ground and excited multiplets using a multi-level model. By comparing the theoretically determined dispersion of the spin-orbit exciton to our data at different temperatures, we found indirect evidence for higher order spin interactions beyond a bilinear spin model.

Keywords: Magnetism
Neutron Scattering
Advisor: Kim, Young-June
Anthranilate Analogs as a Druggable Target for the Proteopathy and Immunopathy of Alzheimer's Disease

β-Amyloid (Aβ) triggered proteopathic and immunopathic processes are a postulated cause of Alzheimer’s disease (AD). Monomeric Aβ is derived from amyloid precursor protein, whereupon it aggregates into various assemblies, including oligomers and fibrils, which disrupt neuronal membrane integrity and induce cellular damage. Aβ is directly neurotoxic, but may also induce neuroinflammation through activation of microglia. This project aims to identify drug candidates that may concomitantly inhibit Aβ aggregation and neuroinflammation, and further investigate their mechanism of actions. Previous work has demonstrated that 3-hydroxyanthranilic acid, an endogenous tryptophan metabolite, is a bifunctional inhibitor against Aβ aggregation and neuroinflammation. This study therefor sought known drugs with an anthranilate backbone, hypothesizing that anthranilate analogs may provide a platform for drug design targeting both the proteopathy and immunopathy of AD. Furosemide was initially identified as a lead compound. The results demonstrate that furosemide is a probe molecule for the treatment of neuroinflammation in AD. It inhibits proinflammatory responses, enhances anti-inflammatory responses, and promotes phagocytic activity. Mechanism studies further demonstrate that furosemide suppresses upregulation of endoplasmic reticulum (ER)-stress marker genes during inflammation, suggesting an interplay between ER-stress and inflammation. To further explore the pharmacologic effects of furosemide, this study devised and synthesized a series of furosemide analogs that target both Aβ aggregation and neuroinflammation. Forty compounds were synthesized and evaluated. Compounds 3c, 3g (SI-W052), and 20 inhibit Aβ oligomerization; 33 and 34 inhibit Aβ fibrilization; 3g and 34 inhibit the production of tumor necrosis factor-α, interleukin-6, and nitric oxide while downregulating the expression of cyclooxygenase-2 and inducible nitric oxide synthase and promoting microglial phagocytosis, addressing the combined proteopathic-immunopathic pathogenesis of AD. This study further investigates the mechanism of compound 3g. Based on the potent activity and its structure similarity with furosemide, the mechanism study focuses on the downstream signaling of ER-stress, autophagy. The data identify a beneficial anti-neuroinflammatory role for mild ER-stress mediated autophagy. This protective mechanism is impaired during prolonged neuroinflammation. Compound 3g is capable of restoring the protective effects of autophagy through mTOR. In addition, 3g attenuates the downregulation of ER-phagy receptor TEX264 during neuroinflammation, suggesting promoted ER self-renovation.

Keywords: Alzheimer's disease
autophagy
ER-phagy
microglia
neuroinflammation
β-amyloid
Advisor: Weaver, Donald F
Applications and Methods for Energy-based Models at Scale

Energy-Based Models (EBMs) are a class of generative models like Variational Autoencoders, Normalizing Flows, and Autoregressive Models. It is a commonly held belief that generative models like these can help to improve downstream discriminative machine learning applications. Generative models present an avenue for learning about underlying low-dimensional structure hidden within high-dimensional datasets and they can be trained on unlabeled data, enabling a pathway for building more label-efficient learning systems. Unfortunately, this dream has not been fully realized as most classes of generative models perform poorly at discriminative applications. EBMs parameterize probability distributions in a fundamentally different way than other generative models which allows them to be more expressive and have more architectural flexibility. We demonstrate that we can take advantage of this additional freedom to apply EBMs successfully to downstream discriminative tasks, notably improving performance over alternative classes of generative models and many other baselines. Unfortunately, this freedom comes at a price, and in practice, EBMs are notoriously difficult to work with, train, scale, and evaluate. The remainder of the thesis covers my work to address these issues. In particular, we explore the use of alternative (non-KL) divergences for EBM training and evaluation. Next, we explore the use of generators to improve EBM training and reduce their dependency on MCMC sampling. Finally, we present a new approach to sampling from discrete distributions which enables recently developed methods for training EBMs on continuous data to be applied to discrete data as well.

Keywords: Energy-Based Models
Generative Models
Machine Learning
MCMC Sampling
Semi-supervised Learning
Unsupervised Learning
Advisor: Zemel, Richard
Duvenaud, David
Urban Biotope Development for Green Infrastructure and Urban Forest Planning in Mexico City

As cities continue to expand and their environmental problems intensify, there is a growing need to incorporate urban forests into land-use planning to enhance and preserve the many environmental services they provide. The environmental problems and demands for greener and more sustainable urban areas are magnified in Mexico City, the largest city in North America. Here, the urban biotope approach was used to understand the extent and structure of the urban forest to support urban land use and planning. First, the urban forest was measured in 500 fixed-area plots (400 m2) and then field data were collected from a subset of plots. A total of 25 urban forest structural and compositional variables were generated from these plots, and then Principal Component Analysis was used to reduce the number of forest variables to five. Hierarchical clustering was used to derive three broader-level biotope groups defined by the dominant land cover and eight finer-level biotope classes characterized by the urban forest. Statistical modelling, based on a random forest algorithm, yielded high classification errors suggesting relatively low accuracy and poor model fit. Alternatively, mapping of four landscape classes based on canopy and environmental variables across the study area provided a much-improved classification system. This work is the first to develop urban biotopes for the entire area of Mexico City and demonstrates that urban biotope information analyzed at different spatial and operational scales is relevant for planning at the neighbourhood, borough, or land use levels. Land use plans at the city-wide level could be developed by incorporating biotope information into planning instruments operating at the neighbourhood and borough levels. Urban biotopes have many practical applications, from informing land-use planning, defining tree planting and stewardship priority areas, setting tree diversity targets, and supporting the development of comprehensive urban forest management and green infrastructure spatial plans.

Keywords: Mexico City
Predictive modelling
Spatial planning
Urban biotopes
Urban forest
Urban planning
Advisor: Puric-Mladenovic, Danijela
Smith, Sandy M.
Symmetry-broken Phases and Transport in Multiband Systems

In this thesis, we study the interplay between electronic, orbital, structural, and dipolar degrees of freedom in systems with different unit cells and crystal symmetries. The first project was motivated by experiments revealing transport anisotropies in 2D electron gas systems formed at the surface of certain oxide heterostructures. We propose that multiorbital electronic correlations leading to orbital order can drive nematicity and account for the observed anisotropy. In the second project, we suggest an alternative explanation for the reported large in-plane anisotropic magnetoresistance by considering small momentum transfer scattering, spin-orbit coupling, and structural distortions. In the third project, we move away from transport and explore an old question that has recently resurfaced in the community -- how dilute superconductors can emerge and lead to dome signatures in Tc. We provide a geometric picture for how long-range attractive interactions naturally predict the presence of a peak of Tc and prove its robustness against fluctuations from competing channels in the low-density regime. Lastly, we consider a system of electric dipole moments packed in a triangular array, inducing a restricted motion of the surface electrons along an effective honeycomb lattice and giving rise to relativistic Dirac dispersions. We show that the dipolar interactions lead to stripe and vortex ground states and that the coupling to fermions can embed them with mass as well as cause nematicity via an order-by-disorder mechanism.

Keywords: Electronic transport
Monte Carlo
Multiband and multiorbital systems
Nematicity
Superconductivity
Symmetry-breaking
Advisor: Paramekanti, Arun