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- All Subjects: engineering
- Creators: Arizona State University
Description
A general review of film growth with various mechanisms is given. Additives and their potential effects on film properties are also discussed. Experimental light-induced aluminum (Al) plating tool design is discussed. Light-induced electroplating of Al as the front electrode on the n-type emitter of silicon (Si) solar cells is proposed as a substitute for screen-printed Silver (Ag). The advantages and disadvantages of Al over copper (Cu) as a suitable Ag replacement are examined. Optimization of the power given to a green laser for silicon nitride (SiNx) anitreflection coating patterning is performed. Laser damage and contamination removal conditions on post-patterned cell surfaces are identified. Plating and post-annealing temperature effects on Al morphology and film resistivity are explored. Morphology and resistivity improvement of the Al film are also investigated through several plating additives. The lowest resistivity of 3.1 µΩ-cm is given by nicotinic acid. Laser induced damage to the cell emitter experimentally limits the contact resistivity between light-induced Al and Si to approximately 69 mΩ-cm2. Phosphorus pentachloride (PCl5) is introduced into the plating bath and improved the the contact resistivity between light induced Al and Si to a range of 0.1-1 mΩ-cm2. Secondary ion mass spectroscopy (SIMS) was performed on a film deposited with PCl5 and showed a phosphorus peak, indicating emitter phosphorus concentration may be the reason for the low contact resistivity between light-induced Al and Si. SEM also shows that PCl5 improves Al film density and plating throwing power. Post plating annealing performed at a temperature of 500°C allows Al to spike through the thin n-type emitter causing cell failure. Atmospheric moisture causes poor process reproducibility.
ContributorsRicci, Lewis (Author) / Tao, Meng (Thesis advisor) / Goryll, Michael (Committee member) / Kozicki, Michael (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021
Description
Dealing with relational data structures is central to a wide-range of applications including social networks, epidemic modeling, molecular chemistry, medicine, energy distribution, and transportation. Machine learning models that can exploit the inherent structural/relational bias in the graph structured data have gained prominence in recent times. A recurring idea that appears in all approaches is to encode the nodes in the graph (or the entire graph) as low-dimensional vectors also known as embeddings, prior to carrying out downstream task-specific learning. It is crucial to eliminate hand-crafted features and instead directly incorporate the structural inductive bias into the deep learning architectures.
In this dissertation, deep learning models that directly operate on graph structured data are proposed for effective representation learning. A literature review on existing graph representation learning is provided in the beginning of the dissertation. The primary focus of dissertation is on building novel graph neural network architectures that are robust against adversarial attacks. The proposed graph neural network models are extended to multiplex graphs (heterogeneous graphs). Finally, a relational neural network model is proposed to operate on a human structural connectome. For every research contribution of this dissertation, several empirical studies are conducted on benchmark datasets. The proposed graph neural network models, approaches, and architectures demonstrate significant performance improvements in comparison to the existing state-of-the-art graph embedding strategies.
ContributorsShanthamallu, Uday Shankar (Author) / Spanias, Andreas (Thesis advisor) / Thiagarajan, Jayaraman J (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2021
Description
The fast pace of global urbanization makes cities the hotspots of population density and anthropogenic activities, leading to intensive emissions of heat and carbon dioxide (CO2), a primary greenhouse gas. Urban climate scientists have been actively seeking effective mitigation strategies over the past decades, aiming to improve the environmental quality for urban dwellers. Prior studies have identified the role of urban green spaces in the relief of urban heat stress. Yet little effort was devoted to quantify their contribution to local and regional CO2 budget. In fact, urban biogenic CO2 fluxes from photosynthesis and respiration are influenced by the microclimate in the built environment and are sensitive to anthropogenic disturbance. The high complexity of the urban ecosystem leads to an outstanding challenge for numerical urban models to disentangling and quantifying the interplay between heat and carbon dynamics.This dissertation aims to advance the simulation of thermal and carbon dynamics in urban land surface models, and to investigate the role of urban greening practices and urban system design in mitigating heat and CO2 emissions. The biogenic CO2 exchange in cities is parameterized by incorporating plant physiological functions into an advanced single-layer urban canopy model in the built environment. The simulation result replicates the microclimate and CO2 flux patterns measured from an eddy covariance system over a residential neighborhood in Phoenix, Arizona with satisfactory accuracy. Moreover, the model decomposes the total CO2 flux from observation and identifies the significant CO2 efflux from soil respiration. The model is then applied to quantify the impact of urban greening practices on heat and biogenic CO2 exchange over designed scenarios. The result shows the use of urban greenery is effective in mitigating both urban heat and carbon emissions, providing environmental co-benefit in cities. Furthermore, to seek the optimal urban system design in terms of thermal comfort and CO2 reduction, a multi-objective optimization algorithm is applied to the machine learning surrogates of the physical urban land surface model. There are manifest trade-offs among ameliorating diverse urban environmental indicators despite the co-benefit from urban greening. The findings of this dissertation, along with its implications on urban planning and landscaping management, would promote sustainable urban development strategies for achieving optimal environmental quality for policy makers, urban residents, and practitioners.
ContributorsLi, Peiyuan (Author) / Wang, Zhihua (Thesis advisor) / Vivoni, Enrique (Committee member) / Huang, Huei-Ping (Committee member) / Myint, Soe (Committee member) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The practice of Facility Condition Assessments (FCA’s) has received academic attention with over 20 condition assessment methodologies to date, focusing on condition gradients and scale ratings. However, little attention has been brought to the life cycle of an FCA, specifically how building owners and managers plan and conduct an FCA. FCA methodologies in academic research are complex, sophisticated and require time for implementation that a typical facility manager does not have. This work showcases the need for simpler, more practical planning variables for a facility manager to begin the process of planning for an FCA in their management of a facilities portfolio. This research is a compilation of two national studies, the creation of an FCA project lifecycle analytical framework, and the creation of an organizational FCA maturity self-assessment model. Data was collected through semi-structured interviews from facility managers and facility condition assessment service providers to gain in-depth insight and understanding of the current practice of facility condition assessments in the facility management profession. This data was used to develop national surveys for both facility owners/managers and FCA service providers. An FCA project delivery model was developed through a Delphi study, representing an FCA project lifecycle. The development of a multi-phased FCA project delivery method provides a relative position and sequence of phases representing an FCA project lifecycle. An organizational FCA maturity self-assessment model was created as the first step for organizations to measure their current state of FCA awareness, FCA practice, state of reliability, asset knowledge posture and historical capital spending. The resulting research makes two distinct contributions to the literature. The first contribution is the sequencing of FCA project phases provides an analytic framework for understanding an FCA project lifecycle, providing owners, FCA practitioners and researchers to acknowledge that an FCA project represents a lifecycle model. The second contribution is an FCA planning tool for building owners and managers that allows an organization to bring to light the current state of FCA awareness and help communicate the value proposition FCA’s can afford to an organization. Recommendations for future research on the role of an FCA are provided.
ContributorsHillestad, Derek (Author) / Sullivan, Kenneth (Thesis advisor) / Ayer, Steven (Committee member) / Hurtado, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ophthalmoscopes are integral to diagnosing various eye conditions; however, they often come at a hefty cost and are not generally portable, limiting access. With the increase in the prevalence of smart devices and improvements to their imaging capabilities, these devices have the potential to benefit areas where specialized imaging infrastructure is not well established. Smart device cameras alone cannot replace an ophthalmoscope. However, with the addition of lens and optics, it becomes possible to take diagnostic quality images. The goal is to design a modular system that acts as an adapter to a smart device enabling any user to take retinal images and corneal images with little to no previous experience. The device should be cost-effective, reliable, and easy to use. The device is not meant to replace conventional funduscopes but acts in areas where current units fail. Applications in non-optimal settings, low resource areas, or areas that currently receive suboptimal care due to geographic or socioeconomic barriers are examples where this device could be used. The introduction of screening programs run by nonspecialized medical personnel with devices that can capture and transmit quality eye images minimizes the long-term complications of degenerative eye conditions.
ContributorsSpyres, Dean (Author) / McDaniel, Troy (Thesis advisor) / Patel, Dave (Committee member) / Gintz, Jerry (Committee member) / Arizona State University (Publisher)
Created2022
Description
Traditional public health strategies for assessing human behavior, exposure, and activity are considered resource-exhaustive, time-consuming, and expensive, warranting a need for alternative methods to enhance data acquisition and subsequent interventions. This dissertation critically evaluated the use of wastewater-based epidemiology (WBE) as an inclusive and non-invasive tool for conducting near real-time population health assessments. A rigorous literature review was performed to gauge the current landscape of WBE to monitor for biomarkers indicative of diet, as well as exposure to estrogen-mimicking endocrine disrupting (EED) chemicals via route of ingestion. Wastewater-derived measurements of phytoestrogens from August 2017 through July 2019 (n = 156 samples) in a small sewer catchment revealed seasonal patterns, with highest average per capita consumption rates in January through March of each year (2018: 7.0 ± 2.0 mg d-1; 2019: 8.2 ± 2.3 mg d-1) and statistically significant differences (p = 0.01) between fall and winter (3.4 ± 1.2 vs. 6.1 ± 2.9 mg d-1; p ≤ 0.01) and spring and summer (5.6 ± 2.1 vs. 3.4 ± 1.5 mg d-1; p ≤ 0.01). Additional investigations, including a human gut microbial composition analysis of community wastewater, were performed to support a methodological framework for future implementation of WBE to assess population-level dietary behavior. In response to the COVID-19 global pandemic, a high-frequency, high-resolution sample collection approach with public data sharing was implemented throughout the City of Tempe, Arizona, and analyzed for SARS-CoV-2 (E gene) from April 2020 through March 2021 (n = 1,556 samples). Results indicate early warning capability during the first wave (June 2020) compared to newly reported clinical cases (8.5 ± 2.1 days), later transitioning to a slight lagging indicator in December/January 2020-21 (-2.0 ± 1.4 days). A viral hotspot from within a larger catchment area was detected, prompting targeted interventions to successfully mitigate community spread; reinforcing the importance of sample collection within the sewer infrastructure. I conclude that by working in tandem with traditional approaches, WBE can enlighten a comprehensive understanding of population health, with methods and strategies implemented in this work recommended for future expansion to produce timely, actionable data in support of public health.
ContributorsBowes, Devin Ashley (Author) / Halden, Rolf U (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Conroy-Ben, Otakuye (Committee member) / Varsani, Arvind (Committee member) / Whisner, Corrie (Committee member) / Arizona State University (Publisher)
Created2022
Description
Existing water quality sensors in surface, environmental, and drinking water systems are not well suited for long-term, scalable use as they require calibration, replacement of reagents, and are subject to biofouling which degrades measurement accuracy. Microbial Potentiometric Sensors (MPSs) offer an alternative approach to water quality monitoring by monitoring a biofilm-mediated potentiometric response to diverse water quality parameters. MPS biofilms grow naturally on graphite electrodes in diverse aqueous systems, are regenerative, and their potentiometric response correlates with numerous water quality parameters. As such, the overarching hypothesis of this dissertation is that MPS signal can be used to assess water quality trends and that its signal is driven by biofilm vitality. To test this hypothesis, machine learning, statistical regression, and the use of more complex, impedimetric measurement techniques were explored to characterize water quality trends in diverse water systems. This was accomplished by completing three dissertation objectives: 1.) Assess whether Machine Learning/Artificial Intelligence (ML/AI) tools can be used to disaggregate various surface water quality parameter values from Open Circuit Potential (OCP) signals produced by MPSs; 2.) Determine whether residual free chlorine concentration in drinking water could be determined by monitoring MPSs; and 3.) Determine whether OCP and/or Electrochemical Impedance Spectroscopy (EIS)-derived impedance data from an MPS can be used to determine water quality trends while confirming its biological origins. The findings confirm the hypothesis by demonstrating that ML/AI can be used to disaggregate MPS signal and determine numerous water quality parameters, offering unique opportunities for real-time monitoring of aqueous environments. Additionally, MPSs are particularly useful in measuring free chlorine concentrations in drinking water distribution systems which offers opportunities for scalable, in-situ, continuous monitoring of chlorine throughout a distribution network. Finally, the findings demonstrate that coupling MPSs’ OCP signal with more advanced measurement techniques such as EIS can improve understanding of drinking water quality trends, however current open source, affordable technologies capable of conducting EIS are prone to high measurement noise and are not currently accurate enough to be used in drinking water systems.
ContributorsSaboe, Daniel (Author) / Hristovski, Kiril (Thesis advisor) / Olson, Larry (Committee member) / Perreault, Francois (Committee member) / Arizona State University (Publisher)
Created2022
Description
In-Band Full-Duplex (IBFD) can maximize the spectral resources and enable new types of technology, but generates self-interference (SI) that must be mitigated to enable practical applications. Analog domain SI cancellation (SIC), usually implemented as a digitally controlled adaptive filter, is one technique that is necessary to mitigate the interference below the noise floor. To maximize the efficiency and performance of the adaptive filter this thesis studies how key design choices impact the performance so that device designers can make better tradeoff decisions. Additionally, algorithms are introduced to maximize the SIC that incorporate the hardware constraints. The provided simulations show up to 45dB SIC with 7 bits of precision at 100MHz bandwidth.
ContributorsMorgenstern, Carl Willis (Author) / Bliss, Daniel W (Thesis advisor) / Herschfelt, Andrew (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rong, Yu (Committee member) / Allee, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
Although knowledge about effective teaching and learning exists, and theories of change strategies are considered, the lack of the understanding of the behavior of engineering faculty during curricular change remains a major contributor against robust efforts for change. In this work, faculty adaptability is conceptualized as self-regulation during curricular change. Faculty participants were recruited from two divergent curricular change contexts: one that is prescribed with interdependence while the other is emergent with uncertainty. In this study, attitude toward context’s strength is conceptualized along the four dimensions of clarity, consistency, constraints, and consequences of the context, while faculty’s self-efficacy and willingness for adaptability are conceptualized along the three dimensions of planning, reflecting, and adjusting. This study uses a mixed method, quantitative-qualitative, sequential explanatory research design. The quantitative phase addresses the question of “How does faculty group in the first context differ from faculty group in the second context in terms of self-efficacy and willingness for planning, adjusting, and reflecting?” The qualitative phase addresses the question of “How do faculty respond to curricular change, as exhibited in their activities of planning, adjusting, and reflecting during change?” Findings point to differences in patterns of correlations between attitude toward context with both self-efficacy and willingness across the two contexts, even though analysis showed no significant differences between attitude toward context, self-efficacy, and willingness across the two contexts. Moreover, faculty participants’ willingness for adjusting, in both contexts, was not correlated with neither attitude toward context’s clarity nor constraints. Furthermore, in the prescribed context, Group A faculty (self-identified as Lecturers, Senior Lecturers, or Adjunct Faculty) showed higher willingness for planning, adjusting, and reflecting activities, compared to Group B faculty (self-identified as Assistant, Associate or Full Professors). Also, in the prescribed context, Group A faculty showed no overall significant correlation with attitude toward context. This study has implications on the way change is conceived of, designed, and implemented, when special attention is given to faculty as key change agents. Without the comprehensive understanding of the adaptability of faculty as key change agents in the educational system, the effective enacting of curricular change initiatives will remain unfulfilled.
ContributorsAli, Hadi (Author) / McKenna, Ann (Thesis advisor) / Bekki, Jennifer (Committee member) / Roscoe, Rod (Committee member) / Arizona State University (Publisher)
Created2021