137091 research outputs found
Sort by
Computational Analysis and Screening of Nitrogen Conversion Reactions over Metal Oxide Surfaces
No full textThis thesis uses first-principles modeling to guide the design of catalysts and electrode materials for nitrogen conversion applications, such as decentralized ammonia production and nutrient recovery. Since ammonia from nitrogen fixation is primarily produced through the Haber-Bosch process, we explore catalyst design for less energy-intensive and lower carbon footprint applications, such as photocatalysis and electrolysis of waste activated sludge, that provide alternative nitrogen conversion pathways.
We first present an overview of emerging alternative nitrogen fixation technologies, including electrocatalysis, plasma catalysis, mechanocatalysis, and photocatalysis. We highlight the need for selective and stable catalysts that can operate under ambient conditions using air as the nitrogen source.
In the first technical chapter, DFT simulations show that carbon species derived from methanol can couple with nitrogen on titania. The resulting C–N intermediates lower thermodynamic barriers for reduction, reframing carbon from a hole scavenger to a co-reactant in photocatalysis. This mechanistic study offers a new route to activate molecular nitrogen at the catalyst surface.
Next, a high-throughput DFT screen focused on N2 versus O2 adsorption selectivity identifies a small set of photocatalyst surfaces. Metastable TiO2 and vanadium borates emerge as promising candidates. We address the challenge of photocatalytic nitrogen fixation without pure nitrogen feedstock.
Finally, we develop Pourbaix analysis to assess bulk material stability under electrochemical nutrient recovery conditions. Incorporating ligand effects from NH3, glycine, and CN- shows that common electrode materials like Ni and Au are prone to dissolution, while Ti-based alloys remain thermodynamically stable under EWAS-relevant conditions.
Overall, this work shows how first-principles modeling can guide the design of materials that balance catalytic performance and sustainability. By advancing frameworks for nitrogen fixation and recovery, the thesis contributes new strategies for supporting a circular nitrogen economy at the molecular level.Ph.D.Chemical and Biomolecular Engineerin
Biomechanical and Transcriptional Analysis of Segmental Outflow and Ocular Hypertension
No full textGlaucoma is the leading cause of irreversible blindness, and the most common form of glaucoma is primary open-angle glaucoma (POAG). The main risk factor for developing most forms of glaucoma is elevated intraocular pressure (IOP), which is maintained by the rate of production and the resistance to outflow of aqueous humor from the anterior chamber of the eye. The trabecular meshwork (TM) is a main regulator of aqueous humor outflow and IOP homeostasis. AH outflow around the circumference of the eye is non-uniform, i.e. there are high flow (HF) and low flow (LF) regions within the TM. This phenomenon, known as segmental flow, occurs in both normal and glaucomatous eyes, but the mechanisms underlying segmental flow are unknown. Better understanding of the functional and molecular differences between HF and LF regions could shed light on aqueous humor outflow regulation and inform development of more targeted IOP-lowering therapies. This thesis investigates biomechanical and transcriptomic differences between HF and LF regions of the TM using mouse and human tissue.
In Aim 1, we quantified segmental flow patterns in normal and dexamethasone-induced (DEX) ocular hypertensive mice and found differences in flow distribution but no change in the overall proportion of high versus low flow regions. In Aim 2, we assessed HF versus LF TM stiffness and matrix composition in normal and DEX-treated mice and found no stiffness differences between regions, although DEX treatment increased TM fibronectin (FN) and α-SMA levels relative to controls. In Aim 3, we performed transcriptomic profiling of HF and LF regions in non-glaucomatous human and mouse TM and identified differential expression of several glaucoma-associated genes, as well as differential enrichment of cellular stress- and epigenetics-related pathways. Together, these findings provide new insights into segmental outflow in both mouse models of glaucoma and human eyes and identify candidate mechanisms that could support the development of more precise IOP-lowering strategies for POAG
Toward Practical Code Designs for Covert Communication
No full textCovert communication concerns the situation in which legitimate parties want to hide the presence of communication from an external eavesdropper. Covert communications are governed by a "square root law," by which the code rate is required to vanish as the block length increases, posing challenges to code design as conventional linear codes do not apply. The proposed research identifies two ways to extend previous work on covert code design: A more general and flexible coding scheme, termed the hybrid scheme, and an explicit code for the additive white Gaussian noise (AWGN) channels. We show that the hybrid scheme enables simpler code design by using an additional but negligible amount of secret key. For explicit code design for AWGN channels, we extend a channel quantization technique, leading to an experimentally feasible design with a short block length compared to existing constructions.M.S.Electrical and Computer Engineerin
Min-Time Coverage in Constricted Environments
No full textWe address a new set of time-optimal coverage problems in the area of networked robotic systems, where a fleet of mobile robots must execute a set of inspection tasks in a constricted operational environment. The robots are separated from each other by adhering to a zoning policy, and furthermore, they must maintain a multi-hop wireless communication network connecting them with each other and with a command and control center that supervises the entire operation. These requirements give rise to resource allocation structures and traffic dynamics that transcend the state of the art of the corresponding theory and challenge our current understandings and insights for these dynamics and their effective management. We provide a systematic introduction of the considered problems and of the elements that differentiate them from similar task allocations and traffic scheduling problems already studied in the literature. The presented developments and the results for the considered problems are organized into three main stages.
The first stage (Chapters 2 - 4) addresses the special cases of the considered problems where the robot fleets are homogeneous in terms of their operational capabilities and they operate in tree-structured guidepath networks. More specifically, in Chapter 2, we provide a complete characterization of these special problem versions in the form of mathematical programming (MP) formulations, and a formal analysis of their worst-case computational complexity. In Chapter 3, we establish some structural results for these problem versions that are useful for the strengthening of the aforementioned MP formulations. Furthermore, we introduce strong combinatorial relaxations of the aforementioned MP formulations for the considered problem versions and an efficient post-processing algorithm that constructs an optimal solution for the original formulation from an optimal solution of these relaxations. In Chapter 4, we employ the insights and the results of the previous chapters towards the development of an efficient heuristic algorithm for addressing larger problem instances.
In the second stage (Chapters 5 and 6), we extend our investigation to the more general cases of the considered problems where the underlying guidepath networks have arbitrary topologies. In Chapter 5, we provide a detailed characterization of the considered MMRS operations and the ensuing traffic management problems in this new setting, and formulate these new problems as integer programs (IPs). Furthermore, we establish that it is possible to relax the integrality requirement for a large subset of the decision variables in these IPs without compromising the feasibility and the optimality of the obtained solutions. In Chapter 6, we develop a heuristic algorithm for the general problem versions. The development of the heuristic capitalizes on the insights and the results for the special versions in the first stage: the heuristic (i) constructs systematically a pertinently selected subtree that is rooted at the command and control center and contains all target locations, and then (ii) solves the induced restriction by adapting the computation procedures of the heuristic for the special problem versions in Chapter 4. This approach enables the computation of good-quality solutions for large-scale problem instances that remain intractable for the relaxations developed in Chapter 5.
In the third stage (Chapter 7), we address the problem versions where the robotic fleet is heterogeneous. In this setting, the tasks at the target locations must be executed by specific robot types equipped with the required capabilities. We provide a detailed characterization of the corresponding MMRS operations under heterogeneous robotic fleets, and the ensuing traffic-management problems. We formulate these problems as dynamic, integer multi-commodity flow problems. Furthermore, we develop strong combinatorial relaxations of the derived IP formulations that reduce the number of integer variables involved while maintaining the feasibility of the obtained solutions
Polymer informatics advancements to accelerate the design of sustainable packaging materials
No full textThe advent of polymers sparked immediate innovation in design, especially in the realm of packaging materials, which constitute a significant portion of contemporary polymer usage. However, conventional packaging materials are designed within a linear economy framework, often leading to persistent environmental challenges due to limited end-of-life management. Addressing this issue requires the development of sustainable polymers that balance high-performance properties with recyclability, including chemical depolymerization, biodegradation, and the use of bio-derived feedstocks.
This thesis presents a comprehensive polymer informatics workflow that integrates computational simulations, predictive machine learning models, and digital polymerization reactions to accelerate the design and discovery of sustainable packaging materials. High-throughput molecular dynamics and Monte Carlo simulations generate low-fidelity data, which, when combined with experimental measurements, improve predictive accuracy across key properties such as gas and water permeability, mechanical strength, and thermal stability. Multi-task learning models enable reliable extrapolation to unexplored chemical spaces, guiding the screening of millions of hypothetical polymers generated through virtual forward synthesis.
The workflow is applied to ring-opening polymerization (ROP) polymers, resulting in the identification of promising candidates and the experimental validation of one polymer, poly(dioxanone), which exhibits strong water barrier performance. A Python-based package was also developed to standardize simulation workflows and facilitate broader adoption within the scientific community.
Overall, this work demonstrates the potential of polymer informatics to accelerate the discovery of high-performance, environmentally responsible packaging materials, forging a path toward a more sustainable future by closing the loop on polymer design and addressing critical environmental challenges
Novel millimeter wave beamforming architectures and procedures for initial access
No full textThe objective of the dissertation is to explore a combination of novel architectural and procedural changes in mmWave systems, to shorten initial access delay, reduce angle estimation error in the acquisition phase, and increase the capacity or spectral efficiency in the data communication phase. To improve initial access delay, we show that the wrong second beam can be selected in the auxiliary beam pair (ABP) technique when the target or user is near the center of one of the beams and the coincident nulls of the other beams. We propose to eliminate the coincident nulls problem by using non-orthogonal beam sets for the beam scanning process. We explore three different approaches to generate the non-orthogonal beam sets. First, we introduce the narrowed beam gap approach, which utilizes reduced spacing between the beams formed using conventional phased array beamforming. For the other two approaches, we maintain the orthogonal beam spacing as used in ABP but adopt different array patterns with larger mainlobes, created using the Dolph-Chebyshev and Gaussian array pattern synthesis methods. We compare the performance of the three beam sets in terms of root mean-squared angle estimation error, the number of beams required to cover the sector (which influences initial access delay), and the effective received signal-to-noise ratio, taking into account the tapering loss inherent in non-uniform beamforming weights. Because good mmWave channels have line-of-sight, we propose to increase the capacity by applying methods that improve free-space MIMO performance, specifically, by increasing the sub-array spacing for mmWave MIMO channels and by using a novel array architecture called the array-of-slanted subarrays, which allows a non-zero relative angle between subarrays on a terminal. Considering 2-D (azimuth-only) beamforming, two sub-arrays per terminal, and a range of base station and user equipment relative rotations, we identify the optimal slanting of subarrays under four different channel models of propagation, in terms of the average spectral efficiency of the link.Ph.D.Electrical and Computer Engineerin
Optimization Methods for Wildfire-Resilient Transmission Grid Operations and Infrastructure Planning
No full textClimate change-driven natural disasters pose an increasing threat to the power grid. Wildfires pose a unique challenge as power systems can ignite these destructive events, exposing utilities to liability. To mitigate the risk of ignitions, system operators proactively de-energize high-risk transmission lines in Public Safety Power Shutoff (PSPS) events. While effective for ignition risk mitigation, PSPS events can cause significant load shed. Utilities can also pursue infrastructure investments, such as line hardening or batteries, to mitigate the risk of ignition or maintain service but these decisions add complexity to already challenging optimization problems.
The contributions of this thesis include advances in modeling for optimally mitigating wildfire ignition risks, improved computational methods that leverage decomposition and machine-learning techniques for scalable algorithms on large and realistic test networks, and applications in infrastructure investments, climate resilience, and equity relevant to policymakers and utilities. This thesis provides detailed optimal power shutoff formulations in work evaluating sensitivity of decisions to ignition risk aggregation metrics and power flow formulations. Optimal power shutoff results achieve an order of magnitude reduction in load shed relative to methods comparable to industry standards. Extensions to infrastructure investment planning to support PSPS events are presented through tractable optimization algorithms, including flexibility to consider policy, equity, and alternative extreme weather events. Computational improvements are introduced through machine learning techniques and a novel temporal decomposition method that enables long time horizons to be modeled, resulting in fast, high-quality solutions that outperforms what was previously possible. These methods provide results on the order of an hour of computing time compared to days required under previous methods. In summary, this dissertation presents a detailed understanding of optimal transmission switching for PSPS events, offering further insights for engineers, utilities, and policymakers through flexible tools with realistic simulations
Reverse Osmosis Using Renewable Energy: System Design and Technoeconomic Optimization for Distributed Desalination
No full textFreshwater use in energy generation, manufacturing, and agriculture has resulted in a significant increase in global water demand. Desalination is a technology solution that augments freshwater reserves by treating saline sources using an energy input. Reverse osmosis (RO) is a mature membrane-based technology for seawater desalination, but centralized systems consume significant amounts of energy that contribute to the cost and carbon footprint of the product water. This has led to an emerging interest in coupling RO with renewable energy sources (RES) at a distributed scale to treat inland saline sources, like brackish groundwater or oil and gas produced water. However, the intermittency of solar photovoltaics (PV) and wind is a challenge given that conventional RO is a constant pressure process designed to operate continuously and maximize uptime. Batch Reverse Osmosis (BRO) is a novel variable pressure process that has been demonstrated to operate at higher water fluxes, higher water recovery, and with lower energy input. These attributes make it suitable for a renewably paired system, but this has yet to be investigated. This research (i) develops an analysis framework for RO driven by intermittent RES, and (ii) evaluates the performance of BRO compared to conventional RO.
The system design comprises four subsystems: energy generation (RES), energy storage (battery), RO (conventional and BRO), and water storage (tank). The RO energy consumption and flux are obtained using the solution-diffusion model. System operation over one year is simulated, and techno-economic modeling and optimization techniques are applied to identify subsystem sizes that yield the lowest levelized cost of water (LCOW). This framework is also applied to other energy resources (wind, hybrid, and grid-connected), feed salinities (brackish and produced water), and water demand profiles (flat and agricultural). For PV-RO with 33.3 ppt feed, the analysis reveals that the higher flux operation of BRO can (i) increase direct use of renewables 40%, (ii) reduce energy storage needs 16×, and (iii) reduce curtailed electricity 6%. BRO can also (iv) reduce brine production 1.19× by operating at higher recovery, or (v) reduce membrane area and energy system size 1.10× by operating at a lower specific energy consumption. These attributes result in a total LCOW reduction of 1.01−1.08× for BRO compared to conventional RO. At brackish water salinities (3 – 10 ppt), BRO is less energy efficient than conventional RO but can reduce brine production ~2×. The case studies also reveal that wind-RO and hybrid-RO have 1.03× and 1.14× lower costs than PV-RO, respectively. Overall, this work identifies key design tradeoffs in RES-driven RO systems to enable sustainable water production.M.S.Mechanical Engineerin
Fresh Start
No full textInterview portion of Lost in the Stacks, episode 668. Features show hosts discussing new takes on projects, habits, and professional work in the new year.Interview portion of Lost in the Stacks, episode 668. Features show hosts discussing new takes on projects, habits, and professional work in the new year
Advanced Data-Driven Methodologies for Enhanced Demand Forecasting in Supply Chain Management
No full textIn the complex and dynamic realm of supply chain management, the accuracy of demand forecasting is paramount for achieving operational efficiency, reducing costs, and enhancing customer satisfaction. This dissertation introduces a series of innovative, data-driven methodologies that significantly enhance the precision of demand forecasts across various facets of the supply chain. By integrating cutting-edge analytics and forecasting techniques, this research addresses critical challenges encountered by logistics and retail sectors, especially in light of recent global disruptions such as the COVID-19 pandemic.
Chapter 2 presents a novel dynamic forecasting method specifically designed for predicting parcel arrivals at logistics hubs. As e-commerce continues its rapid expansion, logistics hubs are under increasing pressure to manage incoming parcel volumes efficiently. This chapter elucidates the development and implementation of an ensemble forecasting model that leverages both historical data and real-time parcel tracking information to predict short-term parcel arrival rates. This innovative approach enables logistics operators to optimize resource allocation, enhance throughput, and mitigate operational uncertainties.
Chapter 3, expands the scope of retail demand forecasting by incorporating a spatial-temporal analytical framework. Recognizing that both geographic and temporal factors significantly influence demand, this chapter unveils a clustering-based ensemble model for forecasting. It integrates augmented fuzzy c-means clustering with advanced time series and multivariate forecasting techniques to generate highly accurate demand predictions that consider both spatial correlations and temporal patterns. This comprehensive approach not only enhances forecast accuracy but also furnishes actionable insights for optimizing supply chain operations within the retail sector.
Chapter 4, explores the significant challenge of estimating true consumer demand during out-of-stock events—a frequent and impactful issue in retail operations. Conventional demand forecasting methods often fail to capture the true essence of consumer desire, particularly when products are unavailable. This chapter introduces a sophisticated model that distinguishes between original demand, substitution demand, and deferral demand, thus providing a more accurate reflection of consumer intent and behavior. This detailed perspective on demandPh.D.Industrial Engineerin