Search results

Title: ENGINEERING 2D PHOTO-REACTING COF FOR PATTERNING AND DRUG DELIVERY
Creator: Chen, Kuo Hao
Date: 2017, 2017-07
Description: Covalent Organic Frameworks (COFs) are 2-dimensional polymers that exhibit rigid and large surface area as well as porous architectures....
Show moreCovalent Organic Frameworks (COFs) are 2-dimensional polymers that exhibit rigid and large surface area as well as porous architectures. Currently, COFs are tailored for gas storage applications, drug delivery, catalysis and they are used as filtering membranes for water treatment. It is well documented that at the nano/micro scale, COFs can form multi-layered architecture with respect to the basic molecular building blocks. In this picture, it is possible that the 2D intra-layer and 3D inter-layer interactions of the basic molecular units COFs may dictate the overall efficiency of the aforementioned applications. To understand the dimensionality-function relationship of COFs, we are engineering hybrid 1D-2D organic polymers. This hybrid architecture will allow us to study the propagation of energy/exciton transfer within the resulting materials among other applications such as drug delivery and light-induced nano/micro-patterning. To achieve our objectives, I exploited the photo-reacting properties of two molecular systems: The first system is used to prepare the 2D COF of interest and the other system is used to engineer a 1D crystalline solid. Although I have not tested the energy/exciton propagation with the desired material, I have successfully engineered a 1D crystalline solid and synthesized the expected 2D COFs. Using a combination of synthetic strategies, I prepared and characterized photoreacting tetra-phenyl ketone building block that was used to form the desired polymer. I have also engineered 1D needle-like crystals of bisphenyl cyclopropenone compound. Moreover, the two materials were characterized by optical and electron microscopy methods. This thesis will detail the synthesis and characterization of all precursors of the basic molecular units that were used to engineer the 1D crystalline solid and 2D COF materials. Condignly, the optical and scanning electron microscopy images highlight the microscale features of the materials of interest. I am certain that this preliminary investigation will pave the way to study the dimensionality of energy/exciton transfer and reaction propagation in the many organic materials.
M.S. in Chemistry, July 2017
Show less

Title: 3D reconstruction of lake surface using camera and lidar sensor fusion
Creator: Khan, Shahrukh
Date: 2020
Description: Global Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing...
Show moreGlobal Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing the reflected signal to obtain surface characteristics. GNSS-R has become one of the many additional applications of the readily available GNSS signals, alongside more traditional remote sensing of ionospheric monitoring, beyond the intended GNSS purposes of providing position, navigation, and timing estimation. In previous work, GPS signals reflected off Lake Michigan in Chicago have been collected using a specially designed portable sensor suite. The data collected is then analyzed to differentiate between surface ice and water conditions, as well as obtain other characteristic information such as surface reflectivity. The goal is to provide a way for remote sensing of seasonal ice formation beyond just satellite imagery which can be affected by cloud cover. To confirm the validity of the GNSS-R results there needs to be a separate reference against which to compare. This work demonstrates the sensor fusion between camera and lidar to reconstruct the lake surface, to provide that truth reference for comparison against the results of the GPS reflectometry signal processing. For this setup, the camera provides visual information about the lake surface, while the lidar provides distance information with respect to the sensor suite. Combining the data from the two sensors allows backward projection of the camera image to reconstruct the lake surface and its features. The backward projection relies upon knowledge of the camera's intrinsic properties alongside distance information of the features captured by the camera. Each pixel of the camera image is then transformed to its 3D position relative to the sensor system. This produces a 3D map of the lake surface, as captured by the sensors. The estimated point at which the GPS signal reflects off the surface, the specular point, is calculated by the satellite position at the time of interest and the receiver location. This point is then mapped onto the reconstructed surface to identify the exact location where the signal reflected and compare the surface visually to the results from the signal analysis.Time-varying camera-lidar-specular-point maps of the data campaigns conducted for this project are created for comparison with the GPS signal analysis. Multiple data campaigns were performed during which the Lake Michigan surface had surface ice, water or a mixture of the two. The lake surface is reconstructed for different timestamps, using the appropriate image frame and lidar frame. Combining chronologically, the changes in the lake surface can then be observed along with the movement of the specular point, due to the movement of the GPS satellites. Any satellites passing over a boundary between water and ice on the lake surface are identified and time stamped, to then be compared to the GPS signal analysis results.
Show less

Title: Effects of the Silicon Content on the Dimensional Changes of Electrodes for Lithium-ion Cells: An Electrochemical Dilatometry Study
Creator: Rodrigues Prado, Andressa Yasmim
Date: 2021
Description: The continuous growth of the electric vehicle market has significantly increased the demand for Li-ion batteries (LIBs). However, state-of-the...
Show moreThe continuous growth of the electric vehicle market has significantly increased the demand for Li-ion batteries (LIBs). However, state-of-the-art LIBs are not yet able to meet the EV industry demand for high energy density and long cycle life rechargeable batteries, prompting efforts to improve the performance of Li-ion cells. In this context, silicon became the most promising next-generation active material for LIBs negative electrodes, especially because Si can significantly increase the lithium storage capacity of the commonly available anodes. Nonetheless, commercialization of Si-based electrodes has been hindered by the poor electrochemical performance of these electrodes, which is mainly attributed to the severe volumetric changes in the silicon particles related to the electrochemical reactions with Li. Since the electrodes are composites with a complex combination of various materials interspaced by pores, the electrode-level swelling may differ significantly from the particle-scale expansion. Furthermore, an increase in electrode thickness due to silicon expansion can have a direct effect on how Li-ion cells are designed, as the accommodation of electrode dilation requires additional cell space to prevent significant dynamic stresses. Thus, the actual volumetric energy density of a LIB cell depends on the electrode swelling, since the higher the magnitude of the electrode expansion, the lower the gains in energy density. Monitoring the electrode dilation is just as important as the electrochemical evaluation when designing cells with Si-based anodes.In this work, we use high-resolution operando electrochemical dilatometry to quantify the (de)lithiation-induced expansion/contraction of silicon, blended silicon-graphite and graphite electrodes, upon electrochemical cycling. We evaluate the relationship between electrode capacity and dilation and observe that while the lithiation capacity improved with increasing the silicon content, the electrode swelling is highly aggravated. For silicon-rich anodes, the electrode dilation can be higher than 300%, and the expansion profile consists of a combination of slow swelling at low levels of lithiation followed by an accelerated increase at higher lithium contents. This non-linear dilation allows for narrowing the swelling by limiting the electrode capacity. In addition, we investigate how electrode properties, such as porosity, affect the dilation profile, and quantify the irreversible expansion of the electrodes. Finally, we discuss some of the challenges associated with the dilatometry technique and suggest experimental approaches for obtaining consistent and reliable data.
Show less

Title: ENHANCED OPTICAL TOMOGRAPHY IN DIFFUSE MEDIA USING OPTICAL GATING OF EARLY PHOTONS
Creator: Ghosh, Aishwarya
Date: 2020
Description: Tissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about...
Show moreTissue biopsies, where a volume of tissue is removed from a patient, typically through needle extraction, provides critical information about the cellular and molecular aspects of an individual patient’s health and/or disease. However, current pathological assessments of tissue biopsies evaluate less than 1% of the volume of the tissue (e.g., one to a few 5-micron slices are sectioned out of the biopsy and stained/processed for microscopic analysis). Since the bulk of tissue biopsy is carried out through optical imaging (absorption or fluorescence), a more 3D, “whole-biopsy” view is conceivably possible with optical projection tomography (OPT). The challenge with OPT has been that for clinically relevant sized biopsies, most photons undergo multiple scattering events that lead to loss of spatial resolution that makes accurate pathological analysis intractable. In my MS thesis, I worked on the development of an enhanced OPT system that employs optical gating based on non-linear up-conversion of infrared ultrashort laser pulses to isolate “early-arriving” photons that experience significantly less scatter than the bulk of photons transiting a scattering biological sample. Considering the complexity of such a system, the entirety of my MS thesis work was spent constructing and testing the femtosecond optical gated OPT system and though I was unable to validate its operation in biological samples, simulations suggest that the properties we were able to achieve could allow high resolution optical imaging in 0.1-1 cm-diameter specimens.
Show less

Title: Frictional behavior of bronze-graphite composite as sliding element in the base isolation system
Creator: You, Da
Date: 2021
Description: There are many calamities around the world, one of the most dangerous disasters is earthquake which threatens the safety of people and the...
Show moreThere are many calamities around the world, one of the most dangerous disasters is earthquake which threatens the safety of people and the structures. Almost every year, there are a lot of property losses and casualties caused by earthquakes. To mitigate the bad effect of the earthquake, the base isolation system was proposed by previous researchers. With the contribution of many researchers, several seismic isolations have been developed. Until now, many structures have installed seismic isolations to resist seismic energy and vibration. The seismic isolation system works well during the earthquake period, and it does help reduce the casualty and property loss induced by earthquakes. There are two main types of bearings used in the seismic isolation system. One is the elastomeric bearings and the other is the sliding bearings. The mechanics of the seismic isolation system preventing the influence of the earthquake and reducing the horizontal acceleration of the structure is to elongate the natural frequency of structure. As for the sliding bearings, the simplest way to increase the period is to reduce the friction coefficient of the two sliding elements. In conventional, two stainless steel plates are commonly used in the pure flat sliding bearing. This study tries to use bronze-graphite composite in the sliding bearing to decrease the friction coefficient.Consequently, the testing results suggest that the bronze-graphite composite has a lower friction coefficient, especially the graphite acting as a lubricant. The friction coefficient of the bronze-graphite plate is in the range of 0.12 to 0.23 under the load of 160 kg - 800kg. With a higher ratio of graphite to bronze at the sliding surface, the effect of reducing the friction coefficient more obviously. And the friction coefficient changes during the increasing loads period. It decreases at the beginning, and starts to increase at a certain load applied on it. Finally, it is reasonable to bronze-graphite composite in a low rise structure which has a relatively low weight. Because the load applied in the test is not high enough, the consequence may not work for high or heavy structure. Taken together, the use of new material with similar properties in the seismic isolation system can help improve the performance of resisting the earthquake. It should be accounted for further research in this field.
Show less

Title: INTELLIGENT STREET LIGHTING AND REMOTE POWER UNITS AS CASE STUDIES FOR CITIES TO DECARBONIZE
Creator: Burgess, Patrick G.
Date: 2022
Description: There is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if...
Show moreThere is a scientific consensus that atmospheric warming caused by the release of emissions will reach critical levels in our lifetime if significant efforts are not made to decarbonize our buildings and power grid. The City of Los Angeles is a prime example of the challenges of decarbonizing, balancing global, federal, and state policies and issues and addressing environmental justice. The first research case studies of the details and challenges of decarbonization efforts include the implementation of the first networked light-emitting diode (LED) streetlights in the city of Chicago on IIT’s campus to improve the reliability and economics of its main campus, 2.5 mi south of downtown Chicago. Research shows that these networked LED streetlights greatly reduce a city's rising energy costs, but the CSMART project team has set out to prove the benefits of integrating an intelligent communications and control system with an existing smart grid infrastructure, such as an existing network and supervisory control and data acquisition (SCADA) systems. In addition to assessing the economic and environmental drivers for the intelligent streetlight solution, the project team is dedicated to assessing the potential cybersecurity vulnerabilities of such a system and working to mitigate or eliminate them. The second research case study covers off-grid remote power units providing continuous illumination for safer streets and safer driving that is unaffected by power outages. Thanks to individual lighting control potentially allowing for dimming, blinking, and even color changing, streetlights powered by RPUs can be used as emergency signaling devices, directing traffic during a city evacuation or other emergency. The RPU control and monitoring can be accessed through the cloud, thereby avoiding reliance on local servers.
Show less

Title: Data-Driven Methods for Soft Robot Control and Turbulent Flow Models
Creator: Lopez, Esteban Fernando
Date: 2022
Description: The world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies,...
Show moreThe world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies, we are now able to collect more data than ever before. This has dawned a new age of data-driven methods which can describe systems and behaviors with increasing accuracy. Whereas before we relied on the expertise of a few professionals with domain-specific knowledge developed over years of rigorous study, we are now able to rely on collected data to reveal patterns, develop novel ideas, and offer solutions to the world’s engineering problems. No domain is safe. Within the engineering realm, data-driven methods have seen vast usage in the areas of control and system identification. In this thesis we explore two areas of data-driven methods, namely reinforcement learning and data-driven causality. Reinforcement learning is a method by which an agent learns to increase its selection of ideal actions and behaviors which result in an increasing reward. This method was applied to a soft-robotic concept called the JAMoEBA to solve various tasks of interest in the robotics community, specifically tunnel navigation, obstacle field navigation, and object manipulation. A validation study was conducted to show the complications that arise when applying reinforcement learning to such a complex system. Nevertheless, it was shown that reinforcement learning is capable of solving three key tasks (static tunnel navigation, obstacle field navigation, and object manipulation) using specific simulation and learning hyperparameters. Data-driven causality encompasses a range of metrics and methods which attempt to uncover causal relationships between variables in a system. Several information theoretic causal metrics were developed and applied to nine mode turbulent flow data set which represents the Moehlis model. It was shown that careful consideration into the method used was required to identify significant causal relationships. Causal relationships were shown to converge over several hundred realizations of the turbulent model. Furthermore, these results match the expected causal relationships given known information of self-sustaining processes in turbulence, validating the method’s ability to identify causal relationships in turbulence.
Show less

Title: RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator: Mo, Fei
Date: 2021
Description: Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
Show less

Title: High-latitude plasma drift structuring from a first principles ionospheric model
Creator: Kim, Heejin
Date: 2020
Description: In the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated...
Show moreIn the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated with ionospheric scintillation, a rapid fluctuation in the amplitude and phase of a radio signal that degrades communications and navigation systems. Predicting polar cap patch movement across the polar cap is an important subject for enabling forecasting of the scintillation.Lagrangian coherent structures (LCSs) are ridges indicating regions of maximum fluid separation in a time-varying flow. In previous studies, the Ionosphere-Thermosphere Algorithm for Lagrangian Coherent Structures (ITALCS) predicted the location of LCSs. These LCSs were shown to constrain polar cap patch source and transport regions for flow assumed to due to $\vec{E} \times \vec{B}$ plasma drift. The LCSs were predicted based on an empirical model of the high-latitude electric field for $\vec{E}$. In this thesis, the LCSs are generated using the first principles ionospheric model SAMI3 (SAMI3 is Another Model of the Ionosphere) as the model for electric field. The work relies on an understanding of various magnetic coordinate systems in space science, and includes three different approaches for attempting to generate the $\vec{E} \times \vec{B}$ drift as the flow fields that are to input to ITALCS. Finally, a representative LCS result is obtained with SAMI3 and shown to be at the high latitudes on the dayside, similar to prior work, but spanning a shorter longitudinal range.
Show less

Title: Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
Creator: Ahmed, Niyaz Afnan
Date: 2024
Description: The performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the...
Show moreThe performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies.
Show less

repository.iit

Search the repository

Enabled Filters

Refine Results

Type

Date

Department

Subject

Creator

Rights