Search results

Title: Armour Tech News, October 17, 1933
Creator: Armour Institute of Technology
Date: 1933-10-17, 1933-10-17
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, October 18, 1928
Creator: Armour Institute of Technology
Date: 1928-10-18, 1928-10-18
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, January 17, 1929
Creator: Armour Institute of Technology
Date: 1929-01-17, 1929-01-17
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, September 27, 1928
Creator: Armour Institute of Technology
Date: 1928-09-27, 1928-09-27
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, January 24, 1929
Creator: Armour Institute of Technology
Date: 1929-01-24, 1929-01-24
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, October 25, 1928
Creator: Armour Institute of Technology
Date: 1928-10-25, 1928-10-25
Collection: Technology News Microfilm collection, 1928-1981

Title: A Hybrid Data-Driven Simulation Framework For Integrated Energy-Air Quality (iE-AQ) Modeling at Multiple Urban Scales
Creator: Ashayeri, Mehdi
Date: 2020
Description: To date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of...
Show moreTo date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of sustainable and healthy built environments. Main limitations to doing so include the existence of large spatiotemporal gaps in local outdoor air pollution data and a lack of a formal theoretical framework to effectively integrate localized urban air pollution data into sustainable built environment design strategies such as natural ventilation in buildings. This work hypothesizes that emerging advanced computational modeling approaches, including artificial intelligence (AI) and machine learning (ML) techniques, along with big open data set initiatives, can be used to fill some of those gaps. This can be achieved if urban air quality explanatory factors are properly identified and effectively connected to the current building performance simulation workflows.Therefore, the primary objective of this dissertation is to develop a hybrid AI-based data-driven simulation framework for integrated Energy-Air Quality (iE-AQ) modeling to quantify the combined energy reduction profiles and health risks implications of sustainable built environment design. This framework (1) incorporates dynamic human-centered factors, including mobility and building occupancy among others into the model, (2) interlinks land use regression (LUR), inverse distance weighting (IDW), and building energy simulation (BES) approaches via the R computational platform for developing the model, and (3) develops a web-based platform and interactive tool for visualizing and communicating the results. A series of novel machine learning approaches are tested within the workflow to improve efficiency and accuracy of the simulation model. A multi-scale model of urban air quality (using PM2.5 concentrations as the end point) and weather localization model with high spatiotemporal resolution was developed for Chicago, IL using low-cost sensor data. The integrated energy and air quality model was tested for the prototype office building at multiple urban scales in Chicago through applying air pollution-adjusted natural ventilation suitable hours.Results showed that the proposed ML approaches improved model accuracy above traditional simulation and statistical modeling approaches and that incorporating dynamic building-related factors such as human activity patterns can further improve urban air quality prediction models. The results of integrated energy and air quality (iE-AQ) analysis highlight that the energy saving potentials for natural ventilation considering local ambient air pollution and micro-climate data vary from 5.2% to 17% within Chicago. The proposed framework and tool have the potential to aid architects, engineers, planners and urban health policymakers in designing sustainable cities and empowering analytical solutions for reducing human health risk.
Show less

Title: A MULTIPLE CASE STUDY OF COLLEGE MATHEMATICS INSTRUCTORS’ TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) AND ITS RELATIONSHIP TO THE INTEGRATION OF INFORMATION AND COMMUNICATIONS TECHNOLOGY (ICT) IN THEIR TEACHING PRACTICES AND STUDENTS’ LEARNING
Creator: Alhejoj, Kawkab
Date: 2020
Description: This multiple-case study aimed to investigate the following essential aspects of instructors’ ICT integration in higher education: self...
Show moreThis multiple-case study aimed to investigate the following essential aspects of instructors’ ICT integration in higher education: self-reported technological pedagogical content knowledge (TPACK), level of ICT integration, and motivations-challenges to integrate specific ICT tools to teach particular mathematics concepts. Four college math instructors were selected purposefully from four community colleges. The TPACK conceptual framework was adopted through the use of the TPACK-M self-assessment survey to understand the perceived TPACK knowledge of the instructors. The model of Niess et al (2009), which describes the teacher’s level of practical ICT integration in the light of their TPACK, assisted in exploring the way college math instructors used ICT. Data collection involved surveys, semi-structured interviews, and direct classroom observations. Quantitative data was analyzed using descriptive statistics, while Atlas.ti software was applied for qualitative data. The findings showed that the total TPACK-M was rated high, with TPK the lowest among all the constructs. In terms of the ICT integration model, one instructor fitted into the recognizing level, another into the adapting level, and two others into the accepting level. There was a misalignment between the self-reported TPACK knowledge and the in-class level of ICT integration. Instructors need more support in developing practical TPACK abilities via effective PD and activating the “teacher model” to help college math instructors integrate ICT in creative and successful practice. Also, more research in higher education is recommended using a larger sample in the area of designing a TPACK instrument for college math instructors to help capture their perceptions and recognize any gap between what they know and what they do in higher education contexts.
Show less

Title: CITIZENSHIP PRESSURE, JOB STRESS, AND WORK-TO-FAMILY CONFLICT: THE MODERATING ROLE OF FLEXIBILITY IDIOSYNCRATIC DEALS
Creator: Ahmed, Shujaat Farah
Date: 2020
Description: Organizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties...
Show moreOrganizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties which may not be out of volition (Bolino, Turnley, Gilstrap, & Suazo, 2010). However, this extra work comes at a price, as employees are exhausted (Bolino et al., 2010) which can have health implications. Yet, no previous studies have examined the mechanism by which citizenship pressure is related to work interfering with family (WIF) conflict. Consequently, this study investigated an underlying mechanism, job stress, through which citizenship pressure was hypothesized to be related to work-family conflict from the work perspective, i.e., WIF conflict. This study also sought to identify the moderating role of flexibility idiosyncratic deals (i-deals) in the relationship of citizenship pressure with a) job stress, and b) WIF conflict. Data were collected across two waves with a time separation of one month in between waves. A total of 323 workers (mean age = 36.2) across industries in the United States participated in the study. Sixty-three percent identified as women, and 37% were men. Regression analyses were used to test the first three hypotheses. PROCESS was used to test the remainder of the hypotheses. Results for the regressions indicated that citizenship pressure was related at job stress and WIF conflict. Further, job stress was related to WIF conflict. Subsequently, the mediation hypothesis was significant. However, the moderation, and moderated mediation models were not statistically significant. I conducted post-hoc analyses to determine other possible significant paths in the model. The indirect effect of WIF conflict through the citizenship pressure and job stress link was statistically significant, thereby supporting an alternate mediation hypothesis. Perceived flexibility i-deals significantly moderated citizenship pressure and WIF conflict at time 1 only. The implications of this study are: managers should focus on their employees’ stressor experiences, as extra work beyond one’s specified job role is increasingly expected of employees. By doing so, pressure may be reduced through improving perceptions that employees can negotiate flexibility i-deals. This is especially important in an era of scarce resources, as pressure to go the extra mile is linked to a number of negative outcomes, such as increased WIF conflict and job stress.
Show less

Title: Unsupervised Learning of Visual Odometry Using Direct Motion Modeling
Creator: Andrei, Silviu Stefan
Date: 2020
Description: Data for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on...
Show moreData for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on unsupervised learning methods and achieved remarkable results which surpass in some instances the accuracy of supervised methods. Many unsupervised approaches rely on predicted monocular depth and so ignore motion information. Moreover, unsupervised methods which do incorporate motion information do so only indirectly by designing the depth prediction network as an RNN. Hence, none of the existing methods model motion directly. In this work, we show that it is possible to achieve superior pose estimation results by modeling motion explicitly. Our method uses a novel learning-based formulation for depth propagation and refinement which transforms predicted depth maps from the current frame onto the next frame where it serves as a prior for predicting the next frame's depth map. Experimental results demonstrate that the proposed approach surpasses state of the art techniques for the pose prediction task while being better or on par with other methods for the depth prediction task.
Show less

Title: Combining Simulation and Emulation for Planning and Evaluation of Smart Grid Security, Resilience, and Operations
Creator: Hannon, Christopher
Date: 2020
Description: The modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements....
Show moreThe modern power grid is a complex, large scale cyber-physical system comprising of generation, transmission and distribution elements. However, advancements in information technology have not yet caught up to the legacy operational technology used in the electric power system. Coupled with the proliferation of renewable energy sources, the electric power grid is in a transition to a smarter grid; operators are now being equipped with the tools to make real-time operational changes and the ability to monitor and provide situational awareness of the system. This shift in electric power grid priorities requires an expansive and reliable communication network to enhance efficiency and resilience of the Smart Grid. This trend calls for a simulation-based platform that provides sufficient flexibility and controllability for evaluating network application designs, and facilitating the transition from in-house research ideas into production systems. In this Thesis, I present techniques to efficiently combine simulation systems, emulation systems, and real hardware into testbed systems to evaluate security, resilience, and operations of the electric power grid. While simulating the dynamics of the physical components of the electric power grid, the cyber components including devices, applications, and networking functions are able to be emulated or even implemented using real hardware. In addition to novel synchronization algorithms between simulation and emulation systems, multiple test cases in applying software-defined networking, an emerging networking paradigm, to the power grid for security and resilience and phasor measurement unit analytics for grid operations are presented which motivate the need for a simulation-based testbed. The contributions of this work lay in the design of a virtual time system with tight controllability on the execution of the emulation systems, i.e., pausing and resuming any specified container processes in the perception of their own virtual clocks, and also lay in the distributed virtual time based synchronization across embedded Linux devices.
Show less

Title: A NEURAL NETWORK BASED MODEL FOR BIOMASS GASIFICATION IN FLUIDIZED BED
Creator: Dirbaz, Mohsen
Date: 2020
Description: Biomass is a renewable energy resource and its utilization has received great attention due to its life cycle carbon-neutrality and the...
Show moreBiomass is a renewable energy resource and its utilization has received great attention due to its life cycle carbon-neutrality and the potential to substitute fossil fuel to produce a variety of energy-related products. Thermochemical gasification is an important route for conversion of biomass that results in a product gas mainly consisting of H2, CO, CO2, CH4 and other light hydrocarbons that can be used as fuel gas to generate power, or as well as raw material to produce a variety of chemicals. Among the existing gasifiers, fluidized beds (FB) offer many advantages such as high conversion efficiency and great flexibility over types of feedstock.More than 200 data sets of biomass gasification in fluidized bed were collected featuring a wide range of operating condition and fuel types. An axiom-based reasoning was used to develop a multiphase statistical pathway needed as a precondition to effectively quantify the entanglements of different important factors in the process.Specifically, by creating an interconnected chain of analysis based on trigonometric functions, geometric projections, and design of a statistical inference tool utilizing neural network units, multiple partial measures of associations between biomass constituents, and operating condition were effectively consolidated and embedded in a single characteristic matrix that consequently led to detection of monotonic relationships for prediction of carbon conversion efficiency and product gas yield. The black box model in comparison to three different models showed better accuracy in predicting four major components of product gas, over the largest applicable range of all the influential parameters of the process, namely, temperature, air equivalent ratio, steam to biomass ratio, and type of fuel. In part of our methodology, we introduce a novel technique for obtaining a dynamical property value for stationary objects, based on a “specific computational time” of an “abstract mechanical operation on characteristics matrices”. The specific computational time (sct) showed excellent capability in capturing the non-equilibrium factor of the process which itself was function of several interrelated variables.
Show less

Title: THE SPATIAL BLOCK: NATURAL VENTILATION IN HOT AND DRY CLIMATES OF TURKEY
Creator: BAY, EZGI
Date: 2020
Description: The housing deficit is a global problem. In Turkey, solutions to remedy scarce, unaffordable, and low-grade housing are being proposed by TOKI...
Show moreThe housing deficit is a global problem. In Turkey, solutions to remedy scarce, unaffordable, and low-grade housing are being proposed by TOKI, the governmental mass housing administration. Its residential projects based on ‘standard regulations’ and ‘high-rise typologies’ have been widely criticized. The ‘one size fits all’ approach is known for its limited exploration of contemporary needs of this society. Low quality urban and architectural conditions in TOKI projects are believed to marginalize the living standards of the residents. Sprawling rapidly throughout different regions around the country, a permanent complaint of TOKI residents is related to outdoor and indoor thermal conditions. As consequence of this ‘homogenization effect’, overheated and underheated conditions are experienced in these ‘naturally ventilated buildings’ designed with few considerations regarding the surrounding environment. Minimal research has been done on how TOKI towers perform under extreme seasonal conditions and what other building forms could be used in consonance with localized Turkish climates. Most TOKI projects have been developed for ‘hot and dry climates’ that also correspond to areas with larger urban growth from recent migrations. Through post-occupancy evaluations, this dissertation investigates a TOKI built in this climatic context. At the same time, this study brings new ‘typological’ alternatives analyzed through energy simulations and computer fluid dynamics (CFD). These methods are intended to bring clarity about the dynamic of thermal stress inside this project, and how renewable sources, such as prevailing winds, could be used to alleviate thermal related problems in consonance with ‘building forms’ derived from ‘vernacular architecture’ in this region.Inputs from residents illustrate the dynamics of thermal stress and reliance on natural ventilation in summer conditions. It is confirmed through results of the Predicted Percentage Dissatisfied (PPD) and the Air Changes per Hour (ACH) obtained from Simulations in the IES-VE software. The relationship between human thermal comfort and indoor microclimate in TOKI housing can be improved through the reformulation of its residential typologies. The ‘Spatial Block’ approach presented in this dissertation brings the idea of how urban and architectural decisions in addition to improving indoor climatic conditions and thermal satisfaction or residents, brings them improved social integration.
Show less

Title: DATA PRIVACY AND DEEP LEARNING IN THE MOBILE ERA: TRACEABILITY AND PROTECTION
Creator: Chen, Linlin
Date: 2020
Description: Privacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly...
Show morePrivacy and deep learning have been two of the most exciting research trends in both academia and industry. On the one hand, big data rapidly expedite lots of data orientated applications, especially like deep learning services. With the tremendous value exhibited by the data, the privacy of data subjects who generate the data, has also raised much attention. Meanwhile more regulations and legislation have been enacted or enforced, intending to enforce the companies and organizations to strictly comply with the personal privacy protection while collecting or utilizing their data. All these moves will substantially change the ways to train the deep learning models and provide AI services, and in some ways might hinder the development of deep learning if not coming up with some sophisticated mechanisms. On the other hand, deep learning has been showing incredibly promising performance in a variety of areas like face recognition, voice recognition, recommendation & advertising, autonomous driving, medical imaging, etc.. This keeps us thinking will deep learning also in turn influence privacy and be leveraged to compromise privacy. Meanwhile we also observe that mobile devices become so ubiquitous that more shares of data are generated on mobile devices, and mostly those data are both extremely sensitive for data subjects as well as extremely valuable for developing deep learning. We shouldn’t neglect the impact of mobile devices on both privacy and deep learning.In this thesis I explore the research on the interactions between privacy and deep learning, especially with the mobile devices being involved in. Specifically I work on: 1). How does privacy change the way we use the data when building deep learning models, and present the mechanism for privacy protection towards deep learning. 2). How does deep learning in turn make privacy more vulnerable to be compromised, and demonstrate the privacy compromise by facilitating deep learning to trace the source mobile devices and link the personal identities.
Show less

Title: A FRAMEWORK FOR MANAGING UNSPECIFIED ASSUMPTIONS IN SAFETY-CRITICAL CYBER-PHYSICAL SYSTEMS
Creator: Fu, Zhicheng
Date: 2020
Description: For a cyber-physical system, its execution behaviors are often impacted by its operating environment. However, the assumptions about a cyber...
Show moreFor a cyber-physical system, its execution behaviors are often impacted by its operating environment. However, the assumptions about a cyber-physical system’s expected environment are often informally documented, or even left unspecified during the system development process. Unfortunately, such unspecified assumptions made in cyber-physical systems, such as medical cyber-physical systems, can result in patients’ injures and loss of lives. Based on the U.S. Food and Drug Administration (FDA) data, from 2006 to 2011, there were 5,294 recalls and 1,154,451 adverse events resulting in 92,600 patient injuries and 25,800 deaths. One of the most critical reasons for these medical device recalls is the violations of unspecified assumptions. These compelling data motivated us to research unspecified assumptions issues in safety-critical cyber-physical systems, and develop approaches to reduce the failures caused by unspecified assumptions.In particular, this thesis is to study the issues of unspecified assumptions in cyber-physical system design process, and to develop an unspecified assumption management framework to (1) identify unspecified assumptions in system design models; (2) facilitate domain experts to perform impact analysis on the failures caused by violating unspecified assumptions; and (3) explicitly model unspecified assumptions in system design models for system safety validation and verification.Before starting to develop the unspecified assumption management framework, we first need to study how unspecified assumptions may be introduced into cyber- physical systems. We took cases from the FDA medical device recall database to analyze the root causes of medical device failures. By analyzing these cases, we found two important facts: (1) one of the major reasons that causes medical device recalls is violation of some unspecified assumptions; and (2) unspecified assumptions are often introduced into the system design models through syntactic carriers. Based on the two findings, we propose a framework for managing unspecified assumption in cyber- physical system development process. The framework has three components. The first component is called the Unspecified Assumption Carrier Finder (UACFinder), which is to identify unspecified assumptions in system design models through automatically extracting syntactic carriers associated with unspecified assumptions. However, as the number of unspecified assumptions identified from system design models can be large, and it may not be always feasible for domain experts to validate and address the most safety-critical assumptions at different system development phases. Therefore, the second component of the framework is a methodology that uses the Failure Mode and Effects Analysis (FMEA) based prioritization approach to facilitate domain experts to perform impact analysis on unspecified assumptions identified by the UACFinder and asses their safety-critical level. The third component of the framework describes a model architecture and corresponding algorithms to model and integrate assumptions into system design models, so that system safety associated with these unspecified assumptions can be validated and formally verified by existing tools.We also have conducted case-studies on representative system models to demonstrate how UACFinder can identify unspecified assumptions from system design mod- els, and how the FMEA based prioritizing approach can facilitate domain experts to verify the appropriateness of identified assumptions. In addition, case studies are also conducted to demonstrate how system safety properties can be improved by modeling and integrating unspecified assumptions into system models. The results of case-studies indicate that the unspecified assumption management framework can identify unspecified assumptions, facilitate domain experts to validate and verify the appropriateness of identified assumptions, and explicitly specify assumptions that would cause defects in these systems.
Show less

Title: Integrity based landmark generation: A method to generate landmark configurations that guarantee mobile robot localization safety
Creator: Chen, Yihe
Date: 2020
Description: From the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar...
Show moreFrom the bronze-age city Nineveh to the modern metropolitan like Tokyo, traffic shape cities and profoundly affect the life of people. Similar to how the wide-spreading of automobile had modified the modern cities in early 20th century, we are now standing on the eve of yet another traffic revolution. With the vast spreading of autonomous/semi- autonomous robotics application, it is important for the urban designers to design or retrofit urban environment that is safe and friendly to the autonomous robots; As more robots are deployed in life-critical situations, such as autonomous passenger vehicles, it is imperative to consider their safety, and in particular, their localization safety. While it would be ideal to guarantee safety in any environment without having to physically modify said environment, this is not always possible and one may have add landmarks or active beacons to reach an acceptable level of safety for landmark-based localization. Localization safety is assessed using integrity, the primary safety metric used in open-sky aviation applications that has been recently applied to mobile robots and can ac- count for the impact of rarely occurring, undetected faults. Conventional integrity monitor- ing method has high dependency on GPS system, while the traditional Global Navigation Satellite System - Inertia Measurement Unit (GNSS-IMU) based localization does not ap- plied in the metropolitan areas due to the signal blocking and multi-pathing problem caused by high-rise structures. Thus, this dissertation concentrates on the feature based integrity monitoring method. This dissertation formulates environmental localization safety problem as a system- atic optimization problem: given the robot’s trajectory and the current landmark map, add the minimal number of new landmarks at certain location such that the integrity risk along the trajectory is below a given safety threshold. This dissertation proposes two algorithms to solve the problem: Integrity-based Landmark Generator (I-LaG) and Fast I-LaG. I-LaG adds fewer landmarks but it is relatively computationally expensive; Fast I-LaG is less com- putationally intensive at the expense of more landmarks. Both simulation and experimental results are presented.
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: UTILITY OF WATERSHED MODELS: IMPROVING TMDL DEVELOPMENT THROUGH A MARGIN OF SAFETY ESTIMATION AND UNCERTAINTY COMMUNICATION
Creator: Nunoo, Robert
Date: 2020
Description: Watershed models are used to represent the physical, chemical, and biological mechanisms that determine the fate and transport of pollutants...
Show moreWatershed models are used to represent the physical, chemical, and biological mechanisms that determine the fate and transport of pollutants in waterbodies (Daniel 2011). These models, in general, are used for exploratory, planning, and regulatory purposes (Harmel et al. 2014). Watershed models have numerous applications; one such use is the development of total maximum daily load (TMDL). TMDL is the amount of pollution a waterbody can receive without becoming impaired. Because of the challenge of uncertainty associated with models and the TMDL development process, the United States Clean Water Act Section 303 (d)(1)(c) requires that a margin of safety (MOS) be specified to account for uncertainty in TMDLs. The question of how MOS is estimated in TMDL was identified as a problem by the National Research Council (NRC 2001). Since the identification of the problem about two decades ago, there have been very few inventories or audits of approved TMDL studies. This study describes a natural language processing and machine learning aided review of the MOS in approved TMDLs from 2002 to 2016. The study determined whether the MOS values incorporated followed a pattern and examined whether there exist a relationship between MOS values and some ecological conditions. Relatively few TMDLs were based on some form of calculation to estimate explicit MOS values; these TMDLs constituted only 16% of the reviewed sample. The remaining 84% used conventional values, but few of those studies provided reasons for their selected values. A statistical assessment of those MOS values revealed that the MOS depended on States (location of waterbody), USEPA regions, waterbody type, designated water use, TMDL model used, and dataavailability. The findings indicate that few TMDL developers are following the National Research Council’s suggestions of using a rigorous uncertainty estimation approach for rational choices for the MOS. An adaptive approach based on Bayes-Discrepancy was proposed for estimating an MOS for a TMDL. The approach is based on the Bayesian hierarchical framework of estimating uncertainty associated with watershed models. With this approach, TMDL developers can communicate the effects of their watershed model. The approach was applied to a Ferson Creek model of the Fox River watershed to access variability and uncertainty in the model results, and also estimate possible MOS values for two monitoring stations in the watershed. Results suggest that an MOS of 0.04 mg/L could lead to a 0.1 probability of violating the water quality standard for an underpredicting model. The Bayes-discrepancy estimation method will enable TMDL developers and watershed managers to strike a balance between implementation options and water quality concerns.
Show less

Title: THE INTERACTION BETWEEN COINAGE OR ALKALI METALS AND POLYAROMATIC HYDROCARBONS
Creator: Liu, Shuyang
Date: 2020
Description: Theoretical study on versatile chemistry of buckybowls and related polyaromatic hydrocarbons has been comprehensively accomplished and...
Show moreTheoretical study on versatile chemistry of buckybowls and related polyaromatic hydrocarbons has been comprehensively accomplished and documented. Polyaromatic hydrocarbons from simple double bond to fullerene C60, as one of major family in buckybowls has shown a wide potential in development of various specifically purposed materials. Complexes with coinage metals evidenced tunable donor ability of related polyaromatic systems’ π-surface. Moreover, functionalization with small ligands cations interact with these π-surface also show some patterns which have certain enlightenment to the experiment. By adding the methyl group on corannulene, to pursue the relationship between geometry and stabilization which provide an alternative strategy of developing. Further study of alkali metals interacts with annulene, continuously adding with crown ether to mimic experiment environment display an interesting pattern. In the end, extended topics of some applications with computational chemistry, such as the help of Raman spectrum of L-focus.
Show less

Title: NANOMATERIALS FOR ADVANCED BATTERY CATHODES
Creator: Moazzen, Elahe
Date: 2020
Description: Cathode materials are key components that directly determine the power density of a battery. One of the most effective ways of developing high...
Show moreCathode materials are key components that directly determine the power density of a battery. One of the most effective ways of developing high power density cathodes is bringing them into the nano-scale world, which results in many expected and unexpected properties. Some of the desired characteristics include faster charge/discharge kinetics, improved capacity retention and structural stability due to the higher surface to volume ratio and shorter ion diffusion paths. In this dissertation a number of uniquely designed nano-sized cathode materials and nanocomposites are developed and investigated for alkaline aqueous and lithium ion battery applications. Nickel hydroxide (Ni(OH)2), which is one of the most important cathode materials in alkaline batteries, suffers from low conductivity, which usually leads to inefficient discharge and incomplete utilization of the material. A series of Ni(OH)2/Co(OH)2 core/shell nanoplatelets were synthesized and systematically investigated as cathode materials. Structure-property correlations revealed that electrochemical behavior and reversibility of Co(OH)2 redox conversion depended non-linearly on the average shell thickness, with the best performance (99.6% of theoretical capacity of the composite material) achieved at shell thickness of 1.9 ± 0.3 nm. Two fundamental phenomena were suggested to be responsible for the superior performance: templated shell deposition and galvanic coupling of core and shell materials.Manganese (IV) oxide (MnO2), which is another practical cathode that has a great potential to be utilized for a variety of energy storage systems, still has some major challenges including reversible cycling in rechargeable batteries. One of the most crucial challenges is the fact that polymorphs of MnO2 have different electrochemical activities as aqueous and Li-ion battery cathodes. However, most synthetic samples contain a mixture of polymorphs, which makes the structure-property correlations more complicated. This dissertation reports on systematic studies correlating synthesis, thermal and mechanical processing, and composite formation with polymorph composition, electrochemical performance and ion intercalation mechanisms. Among all the results, several main conclusions were reached: 1) Through control of the synthesis parameters and post-processing, desired phase compositions and nanoparticle morphologies, which optimize MnO2 performance in aqueous alkaline electrolyte, can be achieved. Nanoparticles with higher fraction of the akhtenskite polymorph showed higher reversible capacities in LiOH electrolyte (~210 mAh g-1), with stable performance for over 50 cycles. The effects of sub-nanoparticle organization of MnO2 polymorphs by thermal treatment without any morphology change on cycling performance, phase activation, and charge/discharge mechanisms in LiOH electrolyte as well as the detailed mechanism of the polymorph conversion during annealing were studied and for the first time, demonstrating that the electrochemical activity of MnO2 material strongly depends not only on the lattice structure of individual polymorphs but also on the sub-nanoparticle polymorph architecture and interphases.2) Several processing strategies, including thermal and mechanical processing, and composite fabrication were utilized to develop functional MnO2 cathodes for Li-ion batteries. Improvements in capacity and cycling performance were correlated to the presence of the pyrolusite phase of MnO2 and the crystallite size. Composite fabrication by graphene oxide wrapping also provided significant performance improvements through polymorph composition control and improved conductivity.
Show less

repository.iit

Search the repository

Pages

Pages

Enabled Filters

Refine Results

Type

Date

Academic status of creator

Department

Subject

Creator

Part of

Rights