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Title: Developing Advanced Materials for Carbon Dioxide Electroreduction to Value-Added Chemicals and Fuels
Creator: Esmaeilirad, Mohammadreza
Date: 2023
Description: Developing highly efficient electrocatalysts for the carbon dioxide reductionreaction (CO2RR) to value-added fuels and chemicals offers a...
Show moreDeveloping highly efficient electrocatalysts for the carbon dioxide reductionreaction (CO2RR) to value-added fuels and chemicals offers a feasible pathway for renewable energy storage and could help mitigate the ever-increasing carbon dioxide (CO2) emissions from human activities. Different catalysts are known to catalyze CO2RR in aqueous solutions. Most known catalysts are only capable of transferring 2 electrons with needed protons to CO2 producing either carbon monoxide (CO) or formic acid (HCOOH). Copper (Cu) is the only electrocatalytic material that converts CO2 into different types of hydrocarbon products. Additionally, owing to Cu’s natural abundance and low cost, it has been intensively studied for CO2RR for decades. However, the required high input energy (overpotential), low product selectivity towards valuable fuel products, and the lack of long-term stability remain major challenges for Cu-based catalysts. This work aims to develop new materials that produce hydrocarbons at lower overpotentials with higher rates and greater selectivity than current copper catalysts. By implementing a process referred to as the electrocatalyst discovery cycle iterations between predications, catalyst testing, and active site characterization allow for the rational design and discovery of new and improved electrocatalysts for CO2RR. This methodology led to the discovery of different heteroatomic catalysts as low overpotential catalysts for electroreduction of CO2 high energy density hydrocarbon products.
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Title: Transactive Energy Market for Electric Vehicle Charging Stations in Constrained Power and Transportation Networks
Creator: Affolabi, Larissa Arielle Sèfiath
Date: 2023
Description: In response to the urgent need for decarbonization, our society is actively working towards reducing carbon emissions across various sectors....
Show moreIn response to the urgent need for decarbonization, our society is actively working towards reducing carbon emissions across various sectors. These efforts have resulted in the widespread adoption of distributed energy resources (DERs) in the electricity sector and the widespread adoption of electric vehicles (EVs) in the transportation sector. The growing popularity of EVs has resulted in rapid growth of charging infrastructure to meet the increasing demand. Recently, combined efforts across those two sectors have gained popularity with the deployment of EV charging stations (EVCSs) with on-site DERs like solar photovoltaic and/or battery energy storage systems not only to defer or avoid the need for power distribution equipment upgrades but also to achieve more environmentally friendly outcomes in terms of decarbonization goals. To increase transportation electrification, we need to expand further the charging infrastructure. The key challenge lies in accelerating charging station deployment while ensuring the safe and efficient operation of the power distribution system where most of this new load will be concentrated. Numerous research efforts have been dedicated to the study of EVCSs, with a focus on either optimizing the pricing of charging services or addressing the energy management challenges from the perspective of system operators. While these aspects are crucial, it is essential to recognize the importance of attracting private sector stakeholders to invest in and support the expansion of the EVCS network. Relying solely on subsidies is insufficient to finance the necessary scale of EVCS deployment required to accelerate the widespread adoption of EVs. The increasing adoption of EVCSs integrated with on-site DERs highlights the potential for Transactive Energy Market (TEM) operations among EVCSs. However, unlike regular prosumers, EVCS operations are uniquely influenced by both the power distribution and the transportation networks. In light of this issue, this dissertation proposes several multi-agent frameworks that leverage on-site DERs at EVCSs to establish a secondary revenue stream through a TEM. This dissertation investigates the technical and economic aspects of these multi-agent frameworks. At its core, we propose two holistic frameworks to solve the energy management problem of EVCSs within a TEM environment. Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on the day-ahead traffic assignment problem solved by the traffic operator agent. For the TEM clearing process, we propose two distinct lines of approach. First, a centralized approach where a single entity assumes both the market operator and grid operator functions. This integrated approach streamlines the decision-making process and ensures coordinated operations between the market and the power grid. Second, a decentralized approach, where separate entities take on the roles of the market operator and grid operator, respectively. This decentralized structure allows for more flexibility and distributed decision-making within the TEM. Furthermore, in contrast to many TEM related studies that overlook the complexity of the power distribution system, we introduce a comprehensive three-phase unbalanced optimal power flow model. This model incorporates features such as network reconfiguration and tap changers, allowing for a more accurate representation and understanding of the power distribution system's operation. Various case studies are used to prove the effectiveness of our proposed lines of approach to EVCSs’ day-ahead energy management problem.
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Title: High-Entropy Stabilization as a Designing Tool for Li-Ion Electrodes
Creator: Bandeira Jovino Marques, Otavio Jose
Date: 2023
Description: High-Entropy oxides (HEOs) form a new class of materials where the configurational entropy plays the stabilizing role of multicomponent...
Show moreHigh-Entropy oxides (HEOs) form a new class of materials where the configurational entropy plays the stabilizing role of multicomponent systems at high temperatures. Recently, it raised much attention for energy storage applications, especially on Li-ion batteries, where the combination of several different elements in a single solid solution can synergistically act to overcome some of its main drawbacks, improving the battery’s performance. The entropy stabilization opens new boundaries on electrode’s design by increasing the compositional space available for different structures and compounds. Not long ago, the high-entropy oxide (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O demonstrated a big potential as anode material in Li-ion batteries. Its high capacity and long cycling stability raised a lot of questions about the role of the transition metals in the conversion reaction, and the configurational entropy contribution to the electrochemical reaction, further supporting the electrode’s stability. In order to investigate the structural evolution, the role of the multicomponent oxides and structures on the battery’s performance, and the entropic contribution to the electrode’s stability, this research proposes a systematic and robust methodology around the (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O high-entropy oxide (HEO). The project heavily relies on the EXAFS ability to determine the short-range structure and the chemical sensitivity to isolate the elemental contribution of the compound at different cycling and charging states. First, the role of different metallic cations on the electrochemical reaction mechanism of the HEO was analyzed by the change in local structure during different charging steps of a Li-ion battery (Chapter 3). Secondly, the entropy contribution and tunability effects on electrochemical performance were tested in a series of medium and high-entropy oxides derived from the seminal HEO. Mg, Co, Ni, Cu, and Zn were individually removed from the HEO’s composition at a time and tested as Li-ion electrode. Fe was also added to the HEO’s composition (HEO+Fe) in order to prove the tunability effects and entropy contribution (Chapter 4). Operando x-ray absorption spectroscopy (XAS) was used to capture the short lived phases and the transient nature of the conversion reaction, to explain the origins of the extra storage capacity encountered on entropy stabilized systems (Chapter 5). Finally, the role of the high-entropy oxide initial structure was investigated and compared, to check versatility of the elements that can be used on a high-entropy system (Chapter 6).
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Title: Using High-Pressure Reverse Osmosis Technique to Desalinate Produced Water
Creator: Dallalzadeh Atoufi, Hossein
Date: 2023
Description: This dissertation presents a comprehensive investigation into the use of high-pressure reverse osmosis (HPRO) to desalinate produced water (PW...
Show moreThis dissertation presents a comprehensive investigation into the use of high-pressure reverse osmosis (HPRO) to desalinate produced water (PW) in the oil and gas industry, with the aim of developing sustainable water management strategies. The study analyzes fouling mechanisms in HPRO desalination, revealing the applicability of Hermia's fouling mechanism to high-salinity waters and highlighting the negligible impact of complete pore blocking and standard pore blocking in crossflow reverse osmosis (RO) desalination. Furthermore, the research investigates ion transport through commercial polyamide thin film composite membranes using the solution-friction model, elucidating the influence of factors such as pressure, temperature, and crossflow velocity on the initial flux while minimal impact on steady-state flux is observed. The assessment of oil and gas waste discharge in water systems provides insights into potential environmental consequences, and the analysis of the behavior of per- and polyfluoroalkyl substances (PFAS) in contaminated sediments using passive sampling demonstrates the rapid uptake of shorter-chain PFAS compounds due to their lower sorption potential and faster diffusion rates. The dissertation contributes to the development of sustainable water management strategies, addressing the challenges of produced water treatment and environmental contamination in the oil and gas industry, and offers valuable information on fouling mechanisms, impacts of ion transport mechanisms, waste discharge and PFAS behavior, enabling optimized desalination processes, informed waste management practices, and a better understanding of environmental contamination issues.
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Title: Machine Learning (ML) for Extreme Weather Power Outage Forecasting in Power Distribution Networks
Creator: Bahrami, Anahita
Date: 2023
Description: The Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms,...
Show moreThe Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms, heavy rain, lightning, tornadoes, and high winds pose a threat, while the colder season brings ice storms, snowstorms, high winds, and sleet storms, all of which can cause significant damage to the environment, properties, transportation systems, and power grids. The average climate in the Midwest is influenced by factors such as latitude, solar input, water systems' typical positions and movements, topography, the Great Lakes, and human activities. The combination of these conditions during different seasons contributes to the development of various types of storms. Therefore, it is crucial to predict the impacts of such atmospheric events on distribution and transmission lines, enabling utilities to assess and implement preventive measures and strategies to minimize the economic losses associated with these disasters. Additionally, the accurate classification of storm modes through an automated system allows operators to study trends in relation to climate change and implement necessary strategies to ensure grid reliability and resilience.In recent years, a significant number of power outages have occurred due to extreme ice formation on transmission and distribution networks, posing a threat to the power grid's resilience and reliability. To prepare power providers for snowstorms, extensive research has been conducted on snow accretion on power lines. Over the past two decades, many scientists have turned to machine learning (ML) algorithms for predicting ice accretion on overhead conductors, as ML models demonstrate superior accuracy compared to statistical forecasting models when it comes to forecasting challenging and fine-grained problems. However, most existing models primarily focus on predicting ice formation on power lines and fail to forecast the resulting damage to the distribution network. Therefore, this project proposes a model for predicting power outages caused by snow and ice storms in the distribution network. The goal is to aid in the planning process for disaster response and ensure the resilience and reliability of the power grid. The proposed outage prediction model incorporates statistical and machine learning techniques, taking into account features related to weather conditions, storm events, and information about the power network feeders.
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Title: TechNews, April 03, 2012
Creator: Illinois Institute of Technology
Date: 2012-04-03, 2012-04-03
Collection: Technology News print collection, 1940-2019

Title: A Limited Case Study: Perspectives of Students, High School Teachers, and College Instructors About Advanced Placement Mathematics Courses
Creator: Joseph, DeJuana V.
Date: 2023
Description: ABSTRACTBackground. The underrepresentation of Black and Latinx students in STEM is highlighted due to structural impediments. Although AP...
Show moreABSTRACTBackground. The underrepresentation of Black and Latinx students in STEM is highlighted due to structural impediments. Although AP mathematics classes are being introduced as a solution, students from underprivileged schools continue to confront challenges. The study project seeks to investigate the obstacles associated with teaching AP mathematics courses in disadvantaged high schools and to gather perspectives from various stakeholders.Methodology. The study employs qualitative one-on-one interviews and an interpretive research paradigm. The sample comprises mathematics faculty, first-year college students, high school AP math teachers, and one high school student. Thematic analysis is used and ethical considerations are taken into account. Limitations include time and resource constraints, a small sample size, and potential inconsistencies in participant responses.Results and Findings. AP mathematics classes may inspire students to continue STEM in college, yet obstacles such as time constraints and knowledge gaps exist. Students' employment choices are influenced by their own interests and abilities. High school math teachers and college professors highlight algebraic skills as crucial for success in college mathematics, particularly in calculus classes.Discussion. The STEM enrollment problem does not only affect minority students; AP math can be a pathway for all kids. Improving AP math readiness for all students is critical for addressing the STEM professional shortage. It is essential to use extra measures to increase student interest in STEM. Strong foundational algebraic knowledge is required for success in higher education, necessitating stakeholders to focus on improving STEM enrollment and completion rates.
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Title: Development of data assimilation for analysis of ion drifts during geomagnetic storms
Creator: Hu, Jiahui
Date: 2024
Description: The primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity....
Show moreThe primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity. Geomagnetic storms affect our everyday lives because they give rise to transient signal loss, data transmission errors, negatively impacting users of satellite navigation systems. The Nighttime Localized Ionospheric Enhancement (NILE) is a localized plasma enhancement that because it is not well understood, drives the design of satellite-based augmentationsystems. To better secure operation of technological infrastructure, it is essential to build a comprehensive understanding of the atmospheric drivers, especially during solar active periods. Instrument measurements and climate models serve as valuable tools in obtaining information regarding the occurrence of space weather events; nonetheless, both sources exhibit quantitative and qualitative limitations. Data assimilation, an evolving technique, integrates measurements and model information to optimize the state estimations. This dissertation presents developments in a data assimilation algorithm known as Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), and its applications in investigating the atmospheric behaviors under varying solar conditions. EMPIRE is a data assimilation algorithm specifically designed for upper atmospheric driver estimation of neutral wind and ion drifts at user-defined spatial and temporal scales. The EMPIRE application in this work aims to contribute to a more comprehensive understanding of the effects of the NILE. EMPIRE utilizes the Kalman filter to optimize state calculations primarily based on electron density rates, provided by other data assimilation algorithms. Earlier runs of the algorithm used pre-defined values for the background state covariance cross time. To address model limitations under changing geomagnetic conditions, the algorithm is enhanced by concurrently updating the background state covariance during assimilation processes. Additionally, representation error is incor- porated as a component of the observation error, and error analysis is performed through a synthetic-data study. Previously, EMPIRE fused Fabry-Perot Interferometer (FPI) neutral wind measurements, demonstrating increased agreement with validation neutral wind data. In this work, this approach is extended to augment Coherent Scatter Radar (CSR) ion drift measurements from Super Dual Auroral Radar Network (SuperDARN), providing additional insights into EMPIRE’s estimated field-perpendicular ion motion. For an in-depth exploration of storm-related NILE, both EMPIRE and another data assimilation method, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension coupled with Data Assimilation Research Testbed (WACCM-X + DART), is implemented for a storm event to test the proposed NILE driving mechanism. Furthermore, this dissertation introduces a Kalman smoother technique into the EMPIRE to enhance its ability to assess past storm events, and to explore the potential for algorithm improvements.
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Title: #MeToo: What Urged Users to Post?
Creator: Hirsh, Rachel Anna
Date: 2023
Description: In this dissertation, I explore the motivations that compelled individuals to share their stories during the #MeToo movement, an unprecedented...
Show moreIn this dissertation, I explore the motivations that compelled individuals to share their stories during the #MeToo movement, an unprecedented digital phenomenon that thrust discussions of sexual harassment and assault into the public sphere. The central research question guiding this study was, "What urged users to post during the #MeToo movement?," which worked to uncover why and how the movement became so widespread.. Research demonstrates that when people are sexually harassed or assaulted, often times they do not come forward (Hlavka, 2014). Spencer et al. shares some of the common reasons women do not come forward are that they don’t classify their harassment or assault as a big enough deal, they do not know who or how to report it, they are afraid, they were drunk, they are ashamed, they don’t want to get their assailant in trouble, or they blame themselves (2017). However, those reasons fell by the wayside, as so many people came forward during the #MeToo movement. This paper aims to figure out why that was and how we can continuously get survivors to come forward. This paper also asks the question, did people come forward to share testimony, be part of a movement, or both?Two distinct hypotheses were formulated to unpack the complex dynamics at play The first hypothesis posited that users who engaged with central nodes, encompassing key figures within the #MeToo movement, original contributors, celebrities, and influencers, were more inclined to hold a positive outlook on the movement as a progressive step for women. This data was collected through a quantitative survey, and the analysis yielded inconclusive results, with 79.15% of the sample population expressing support for the movement while only 54.17% reported following central nodes. Qualitative interviews further underscored the multifaceted nature of motivations.The second hypothesis posited that individuals were more inclined to share their personal experiences of harassment or assault online when they observed weak ties within their social networks, such as acquaintances or friends of friends, sharing their own stories. The findings from survey data revealed that 68.87% of participants witnessed weak ties sharing personal experiences or using the #MeToo hashtag on their social media platforms. Qualitative interviews unanimously highlighted the significant influence of observing friends or weak ties posting about their experiences, further underscoring the diversity of motivators behind #MeToo participation.These findings shed light on the multifaceted nature of online activism and the pivotal role of personal networks in shaping the movement's trajectory. In essence, this research demonstrates that while the motivations for user participation in the #MeToo movement are diverse and complex, the presence of weak ties, or distant social relationships or relationships with infrequent interactions, within social networks emerges as a critical influence.
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Title: Optimization of Large-Scale NOMA With Incidence Matrix Design and Physical Layer Security
Creator: Hwang, Eli W.
Date: 2024
Description: The Non-Orthogonal Multiple Access (NOMA) system is recognized for its capability to achieve higher spectral efficiency and massive...
Show moreThe Non-Orthogonal Multiple Access (NOMA) system is recognized for its capability to achieve higher spectral efficiency and massive connectivity. NOMA is intended to transmit massive user communications. The incidence matrix governs the relationship between users and resources for the Code domain NOMA (CD-NOMA). However, NOMA studies focus less on the design and optimization of the incidence matrix.Therefore, this thesis aims to investigate the development of a secure and large-scale NOMA system based on incidence matrix design. The main contributions are outlined as follows: Firstly, this research introduces a novel NOMA system. Distinct from existing studies, the NOMA system is based on combinatorial design. This innovative approach, coupled with a unique constellation design, eliminates the surjective mapping from the linear adding data of multiusers, reducing the complexity of constellation design and Multiuser Detection (MUD). The characteristics of the incidence matrix designs, Simple Orthogonal Multi-Arrays (SOMA), are explored, which display a distinct Latin Square pattern. The SOMA design's unique structure allows for the creation of a highly flexible and fair resource allocation matrix. The NOMA system's theoretical performance analysis equations are established, supporting dynamic adaptability and optimization. The design is validated by Monte Carlo simulation. Compared to other NOMA schemes, it offers higher degrees of freedom and lower complexity while maintaining graceful error rates to transmit a larger number of users. Secondly, a novel NOMA system utilizing incidence matrix information in the uplink is investigated. The incidence matrix pattern is exploited for MUD to achieve large-scale user connectivity. The incidence matrix is designed based on two critical mathematical concepts: parallel classes in hypergraph theory and orthogonal arrays (OAs) in combinatorial designs. Unlike other NOMA schemes, which require modification of their receiver and transmitter to decode superimposed multiuser signals, the unique pattern of the OA structure enables the use of conventional modulators. Consequently, the system load increases and the complexity and latency are reduced. The order of magnitude of the decoding complexity can be significantly reduced from O(N^3) to O(N) compared to the conventional minimum mean-square estimation (MMSE) decoder. Monte Carlo simulation validates that this novel NOMA system outperforms other NOMA designs in terms of error rate, data rate, and system size. Finally, a reconfigurable convolutional encoder design that integrates security and error correction based on physical layer security (PLS) and randomness is developed. This design addresses concerns over privacy, security, and reliability of Internet of Things devices in edge computing networks. The lightweight Convolutional encoders are designed to ensure security by updating the transfer function dynamically with user data. The reconfigurability of the design is achieved by replacing the fixed adder that represents the generator polynomials with the switch adder, enabling the use of 87 billion distinct updating structures, thereby enhancing the versatility of the design. BER-based PLS paradigms are demonstrated in the simulation. In the simulation, the robustness and randomness of this design are further validated through tests suggested by the National Institute of Standards and Technology for cryptographically secure pseudorandom number generators, such as the monobits, longest one, and run tests.
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Title: A Kernel-Free Boundary Integral Method for Two-Dimensional Magnetostatics Analysis
Creator: Jin, Zichao
Date: 2023
Description: Performing magnetostatic analysis accurately and efficiently is crucial for the multi-objective optimization of electromagnetic device designs...
Show morePerforming magnetostatic analysis accurately and efficiently is crucial for the multi-objective optimization of electromagnetic device designs. Therefore, an accurate and computationally efficient method is essential. Kernel Free Boundary Integral Method is a numerical method that can accurately and efficiently solve partial differential equations. Unlike traditional boundary integral or boundary element methods, KFBIM does not require an analytical form of Green’s function for evaluating integrals via numerical quadrature. Instead, KFBIM computes integrals by solving an equivalent interface problem on a Cartesian mesh. Compared with traditional finite difference methods for solving the governing PDEs directly, KFBIM produces a well-conditioned linear system. Therefore, the numerical solution of KFBIM is not sensitive to computer round-off errors, and the KFBIM requires only a fixed number of iterations when an iterative method (e.g., GMRES) is applied to solve the linear system.In this research, the KFBIM is introduced for solving magnetic computations in a toroidal core geometry in 2D. This study is very relevant in designing and optimizing toroidal inductors or transformers used in electrical systems, where lighter weight, higher inductance, higher efficiency, and lower leakage flux are required. The results are then compared with a commercial finite element solver (ANSYS), which shows excellent agreement. It should be noted that, compared with FEM, the KFBIM does not require a body-fitted mesh and can achieve high accuracy with a coarse mesh. In particular, the magnetic potential and tangential field intensity calculations on the boundaries are more stable and exhibit almost no oscillations.Furthermore, although KFBIM is accurate and computationally efficient, sharp corners can be a significant problem for KFBIM. Therefore, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal core with an airgap (C-core) is modeled to show the effectiveness of the proposed approach in addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out from KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method.
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Title: Modeling and Optimization of Embedded Active Flow Control Systems
Creator: Henry, James M.
Date: 2024
Description: This thesis presents research focused on the aerodynamic performance of circulation control on two-dimensional and quasi-two-dimensional wings...
Show moreThis thesis presents research focused on the aerodynamic performance of circulation control on two-dimensional and quasi-two-dimensional wings. Aerodynamic loads, namely lift, drag, and moment coefficients, are measured through Reynolds Averaged Navier Stokes (RANS) modeling and wind tunnel experiment. A simplified and parameterized RANS model is presented as a rapidly iterable approach to estimating the performance of trailing-edge circulation control on two dimensional airfoils, with the hypothesis that an optimized airfoil shape can be found which maximizes the lift coefficient increment generated by circulation control, through modification of the wing profile. The simplified modeling setup is compared with more conventional approaches to numerical simulation of circulation control. The performance of the simplified modeling scheme is then compared with wind tunnel studies, for both steady-state and dynamic performance, as functions of both momentum coefficient dCμ and chord-based Reynolds number Re_c. The dynamic performance for the model is studied to find an analog to the theoretical unsteady models of Wagner and Theodorsen. An adjoint optimization framework is used to find an optimal airfoil profile for circulation control. The optimized profile is then compared in both a simulation and a wind tunnel test study against a NACA0015 airfoil. In simulation, improvement between 12% and 15% is seen for the lift control authority for all values of dCμ and Re_c tested. In experiment, the optimized profile demonstrated improvements of up to 28% in lift control authority, dCL/dCμfor values of Cμ, and decreased performance for higher values of Cμ.
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Title: Pilgrim Baptist Church, Chicago, Illinois, ca. 1964
Creator: Weil, F. Peter
Date: 1964
Description: Pilgrim Baptist Church (3301 S. Indiana Ave, Chicago, IL) photographed by Institute of Design student F. Peter Weil. Date is estimated as 1964...
Show morePilgrim Baptist Church (3301 S. Indiana Ave, Chicago, IL) photographed by Institute of Design student F. Peter Weil. Date is estimated as 1964 from other evidence in the collection.
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Collection: F. Peter Weil photographs, 1952-1964

Title: Correlating Microstructural Properties to Macroscopic Shear Mechanics to Improve the Understanding of Tissue Biomechanics
Creator: Cahoon, Stacey Marie
Date: 2023
Description: Understanding tissue biomechanics is of interest for modeling organ injury from external loads, development of tissue surrogate materials, and...
Show moreUnderstanding tissue biomechanics is of interest for modeling organ injury from external loads, development of tissue surrogate materials, and creating new biomarkers for disease. Probing the response of soft tissue in shear can provide information on histopathology, provided a methodology exists that connects the macroscopic mechanical properties with cell-level properties. Two of the available methods to measure the macroscopic shear viscoelastic properties of soft tissue are oscillatory shear rheometry and ultrasound shear wave elastography (SWE). Due to its accuracy, rheometry is the gold standard, but it is destructive, requires excised homogeneous samples, and can only be applied ex-vivo. SWE is an emerging non-invasive imaging technique which requires validation, ostensibly by comparing with rheometry. Histology is the gold standard for providing morphological information on the cell level, which can determine tissue pathology. The challenge is to connect the macroscopic mechanical metrics derived from SWE and rheometry to the tissue microstructure. To address this challenge, mathematical models can be used that employ multiple, judiciously chosen measurements of macroscopic shear properties and histology to estimate intrinsic mechanical properties at the cell level.A class of homogeneous and composite lipid phantoms mimicking the mechanical properties of brain white matter were fabricated to test a novel stereotactic system and an optimized SWE imaging protocol. The shear stiffness measurements obtained with SWE on the whole phantom were validated with rheometry performed on a series of samples made with the same material as the phantoms. The same procedure was applied to porcine brain white matter excised from fresh whole brains (n=3). Cylindrical cores were extracted from the corpus callosum area, sliced into discs and microscopic sections were subsequently removed for histology. Good agreement was found between the SWE and rheometry measurements of shear stiffness, which generally increases with the level of compressive prestress. Immunofluorescence was used to stain separately the axon neurofilaments and myelin sheaths, and digital image analysis of the confocal microscopy images allowed the estimation of axon volume fraction and axon-to-myelin ratio in the corpus callosum. Using these metrics and a composite mechanical model, a connection between the macroscopic shear measurements and the viscoelastic properties of axon and glia matrix was made for porcine brain tissue. Similarly, rheometry was used to measure the macroscopic properties of decellularized porcine myocardium extracellular matrix (ECM) in two different fiber locations, and for three different fiber orientations. The mechanical properties were found to be dependent upon fiber location, but not on fiber orientation. Since collagen is a primary supportive structure for the ECM, several microscopic slices were probed with immunofluorescence to compute the collagen I and collagen IV volume fractions. Another mechanical model was employed to establish a connection between the macroscopic properties and the mechanical properties of the collagen matrix in decellularized porcine myocardial ECM.This dissertation highlights the use and integration of three different experimental techniques (rheometry, ultrasound SWE, and histology) to correlate key microstructural properties of soft, fibrous tissues (ex-vivo healthy porcine brain white matter and myocardium ECM) with macroscopic shear mechanics. The consideration of the effect of compressive prestress is noteworthy. The reported baseline data for the tissues under shear loading and prestress are pertinent to the physiological function of these tissues, and therefore constitute preliminary data and a necessary first step before a systematic study of the biomechanics of the same tissues in vivo is performed.
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