Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

Displaying 1 - 2 of 2
Filtering by

Clear all filters

171946-Thumbnail Image.png

Investigating the Solid Oxide Fuel Cell Anode Degradation under Siloxane Contamination

Description
Siloxane, a common contaminant present in biogas, is known for adverse effects on cogeneration prime movers. In this work, the solid oxide fuel cell (SOFC) nickel-yttria stabilized zirconia (Ni-YSZ) anode degradation due to poisoning by siloxane was investigated. For this purpose, experiments with different fuels, different deposition substrate materials, different…

Siloxane, a common contaminant present in biogas, is known for adverse effects on cogeneration prime movers. In this work, the solid oxide fuel cell (SOFC) nickel-yttria stabilized zirconia (Ni-YSZ) anode degradation due to poisoning by siloxane was investigated. For this purpose, experiments with different fuels, different deposition substrate materials, different structure of contamination siloxane (cyclic and linear) and entire failure process are conducted in this study. The electrochemical and material characterization methods, such as Electrochemical Impedance Spectroscopy (EIS), Scanning Electron Microscope- Wavelength Dispersive Spectrometers (SEM-WDS), X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), and Raman spectroscopy, were applied to investigate the anode degradation behavior. The electrochemical characterization results show that the SOFCs performance degradation caused by siloxane contamination is irreversible under bio-syngas condition. An equivalent circuit model (ECM) is developed based on electrochemical characterization results. Based on the Distribution of Relaxation Time (DRT) method, the detailed microstructure parameter changes are evaluated corresponding to the ECM results. The results contradict the previously proposed siloxane degradation mechanism as the experimental results show that water can inhibit anode deactivation. For anode materials, Ni is considered a major factor in siloxane deposition reactions in Ni-YSZ anode. Based on the results of XPS, XRD and WDS analysis, an initial layer of carbon deposition develops and is considered a critical process for the siloxane deposition reaction. Based on the experimental results in this study and previous studies about siloxane deposition on metal oxides, the proposed siloxane deposition process occurs in stages consisting of the siloxane adsorption, initial carbon deposition, siloxane polymerization and amorphous silicon dioxide deposition.
ContributorsTian, Jiashen (Author) / Milcarek, Ryan J. (Thesis advisor) / Muhich, Christopher (Committee member) / Wang, Liping (Committee member) / Phelan, Patrick (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2022
193656-Thumbnail Image.png

System Identification and Control Systems Engineering Approaches for Optimal and Practical Personalized mHealth Interventions for Physical Activity

Description
Physical inactivity is a major contributor to chronic illnesses and mortality globally. However, most interventions to address it rely on static, aggregate models that overlook idiographic (i.e., individual-level) dynamics, limiting intervention effectiveness. Leveraging mobile technology and control systems engineering principles, this dissertation provides a novel, comprehensive framework for personalized behavioral…

Physical inactivity is a major contributor to chronic illnesses and mortality globally. However, most interventions to address it rely on static, aggregate models that overlook idiographic (i.e., individual-level) dynamics, limiting intervention effectiveness. Leveraging mobile technology and control systems engineering principles, this dissertation provides a novel, comprehensive framework for personalized behavioral interventions that have been tested experimentally under the Control Optimization Trial (COT) paradigm. Through careful design of experiments, elaborate signal processing and model estimation, and judicious formulation of behavior intervention optimization as a control system problem, this dissertation develops tools to overcome challenges faced in the large-scale dissemination of mobile health (mHealth) interventions. A novel Three-Degrees-of-Freedom Kalman Filter-based Hybrid Model Predictive Control (3DoF-KF HMPC) controller is formulated for physical activity interventions and evaluated in a clinical trial, demonstrating its effectiveness. Furthermore, this dissertation expands on understanding the underlying dynamics influencing behavior change. Engineering principles are applied to develop a conceptual approach to generate dynamic hypotheses and translate these into first-principle dynamic models. The generated models are used in concert with system identification principles to enhance the design of experiments that yield dynamically informative data sets for behavioral medicine applications. Additionally, sophisticated search, filtering, and model estimation algorithms are applied to optimize and personalize model structures and estimate dynamic models that account for nonlinearities and “Just-in-Time” (JIT; moments of need, receptivity, and opportunity) context in behavior change systems. In addition, the pervasive issue of data missingness in interventions is addressed by integrating system identification principles with a Bayesian inference model-based technique for data imputation. The findings in this dissertation extend beyond physical activity, offering insights for promoting healthy behaviors in other applications, such as smoking cessation and weight management. The integration of control systems engineering in behavioral medicine research, as demonstrated in this dissertation, offers broad impacts by advancing the field's understanding of behavior change dynamics, enhancing accessibility to personalized behavioral health interventions, and improving patient outcomes. This research has the potential to radically improve behavioral interventions, increase affordability and accessibility, inspire interdisciplinary collaboration, and provide behavioral scientists with tools capable of addressing societal challenges in mHealth and preventive medicine.
ContributorsEl Mistiri, Mohamed (Author) / Rivera, Daniel E. (Thesis advisor) / Deng, Shuguang (Committee member) / Muhich, Christopher (Committee member) / Pavlic, Theodore P. (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2024