Dissertation Defense Announcements

Safety issues of lithium-ion batteries (LIBs) are usually initiated from an internal short circuit (ISC) that can be triggered by external accidental abusive loadings. The generated heat and the increased temperature would lead to several complicated physio-chemical changes of the batteries, e.g., thermal runaway (TR). Thus, investigation of the multiphysics behaviors of lithium-ion batteries becomes a paramount task to understand the battery safety issues. Experimental characterization and numerical simulation are essential ways to understand the underlying nature of the multiphysics behavior of batteries. However, experimental observation may only provide insufficient data due to the limitation of experimental technology. Particularly, in-situ and operando experiment methodologies are limited. Multiphysics modeling is regarded as a critical and insightful tool to unravel the nonlinear and complicated behaviors. Machine learning (ML) model with data-driven methodology is another important tool to realize fast and accurate estimation and classification. Herein, an ML-based ISC risk evaluation model will be first developed based on the training dataset generated by the combination of experimental data and simulation data. A Representative Volume Element (RVE) based mechanical model, which can predict accurate mechanical behaviors at a much lower calculation time cost, will be established to assist the data generation. Next, an ML-based classifier will be developed to classify the cell’s safety levels under various work conditions. A multiphysics model will be developed to assist the generation of training data samples. Finally, two typical safety issues: defect and TR propagation are systematically studied. The safety risk of the defective batteries will be further evaluated. Electrochemical and mechanical characterization tests will be designed and conducted. The multiphysics model will be used to provide necessary auxiliary instructions of the related mechanisms. TR propagation behaviors of battery packs will be experimentally and numerically investigated. The battery pack TR model will be developed based on the single-cell multiphysics model.
This study comprehensively investigates the multiphysics behavior of LIB cells under mechanical abusive loadings, highlights the promise of combining the physical model with a data-driven model, and provides an innovative solution for the recognition of the battery safety risks for battery safety monitoring.

In recent years, social media have dramatically improved the dissemination speed of information, which also includes health misinformation. To date, most of the computational approaches to addressing this problem have focused on detecting and flagging misinformation content. However, the majority of these approaches have overlooked many important aspects of health misinformation, such as the behavior of evidence sources and the sharing decisions of social media users. To address the limitations, this dissertation research develops an evidence-based approach to detecting health misinformation and to intervening user sharing intention on social media sites. This work takes on a new perspective regarding health misinformation by understanding user stance (i.e., for, against, neutral) due to their motivation of influencing others. Moreover, this research investigates arguments that combine both stance and evidence for assessing the credibility of health information for the very first time. Our analysis of evidence distribution in health information tweets shows that 70% of tweets contain source-based evidence, which provides the foundation for proposing an evidence-based approach to misinformation detection. Based on these results, we built argument detection models to identify stance positions within arguments. Our results demonstrate the importance of evidence-based features in identifying the stance within arguments on social media sites. Drawing on the evidentiality theory, information credibility heuristics, and consistency heuristics, we propose a research model that seeks to explain health misinformation detection and sharing behavior with evidence-based interventions. To test the research model, we designed and developed eleven types of evidence-based digital nudges and used them to conduct user experiments. The empirical results demonstrate that our nudge design improves credibility assessment of health misinformation. This dissertation makes several research contributions. First, it extends an evidentiality theory and credibility cognitive heuristics provided by health experts to analyze the types of evidence included in health-related user-generated content Second, it presents an evidence-based schema for categorizing evidence in user-generated content. Third, it uses evidentiality theory as the kernel theory to guide the design of digital nudges. In particular, it illustrates how evidence-based design artifacts can be used to support augmented intelligence for mitigating the spread of health-related misinformation on social media sites. Finally, it combines cognitive heuristics to the design of digital nudges. Specifically, it uses information credibility and consistency heuristics to analyze user-generated content on social media sites. The outcomes of this research have significant implications for augmenting users’ assessment of health information credibility and enabling timely intervention of misinformation on social media sites.

Progress in nanofabrication made possible development of metamaterials and nanoplamonics two decades ago. The area of mesoscale photonics where the characteristic dimensions of spherical, pyramidal or other building blocks are on the order of several wavelengths remained relatively less studied. However, the optical properties of such structures are extremely interesting due to their ability to create tightly focused beams, so-called “photonic nanojets”, and to resonantly trap light inside their building blocks. In this dissertation, we focus on two main applications of such structures. It is proposed to use dielectric microcons for concentrating light on photodetector focal plane arrays (FPAs) and it is proposed to use contact high-index dielectric microspheres (also termed ball lenses) for improving resolution in cellphone-based microscopy.
We proposed and developed three designs of silicon (Si) microconical arrays which can be used as light concentrators for integration with FPAs operating in mid-infrared (MWIR) region. Such structures can be fabricated by anisotropic wet etching of Si. The spectral and angular dependencies of power enhancement factors (PEFs) provided by such high-index (n~3.5) Si microcones are calculated using finite-difference time-domain modeling. In addition, we observed and studied resonant trapping of photons inside such microcones which can lead to their applications in multispectral imaging devices with a large angle-of-view (AoV).
It is shown that similar microconical light concentrators formed by low-index materials (n = 1.6) which can be fabricated by Nanoscirbe or by plastic injection molding. It is demonstrated that PEFs ~100 times can be achieved in such structures with optimized geometry. It was demonstrated a good agreement of our numerical modeling results with the experiments performed previously on structures with a suboptimal geometry.
We proposed a novel label-free cellphone microscopy assisted by high index contact ball lenses. Resolution of the cellphones is limited by the pixilation of the images. Previous microoptics-based imaging solutions provided insufficient magnification and suffered from spherical aberrations and pincushion distortion. In our work, it is shown that use of ball lenses with n~2 specially designed to provide maximal magnification values (up to 50 times) allows to reduce the role of pixilation and reach diffraction limited resolution values of ~600 nm based on rigorous resolution quantification criteria. It is demonstrated that dispersion properties of the ball lens material significantly influence the magnification in such cellphone imaging. It is shown a semi-quantitative agreement of observed magnification with a simplified geometrical optics model. We demonstrated imaging of various biomedical samples by using proposed cellphone microscopy. It is shown that it can be used as a compact and inexpensive replacement of conventional microscopes to diagnose diseases such as melanoma in vivo without invasive biopsy.
To extend FoV, we assembled centimeter-scale arrays of ball lenses using either micromanipulation or air suction through microhole arrays obtained by laser burning or micromachining. It was found this technology allows obtaining ordered arrays for sufficiently large (>300 μm) ball lenses, but assembling smaller microspheres can in principle be also achieved in future work. It was demonstrated that such microspherical arrays can be used as: a) superresolution coverslips with wide FoV (after embedding in plastic), and b) retroreflectors with ultranarrow reflection cone and highly dispersive properties.

To determine how secondary, Math I teachers understand student engagement in the classroom setting by exploring their lived experiences, the researcher utilized a constructivist paradigm to frame the phenomenological multiple case studies of one
southwestern North Carolina school district. The intent of the researcher was to describe the understanding of the phenomenon of classroom engagement from the perspectives of high school Math 1 teachers. The researcher engaged in conversations with a purpose
which is characterized by Burgess (1984) as a conversational dialogue that is achieved through active engagement by the interviewer and interviewee around a relevant issue.
Research regarding engagement began in the early 1980’s. The topic of engagement has become increasingly popular in education and psychological research due to its emphasis on explaining student behaviors (van Uden, Ritzen, & Pieters, 2013). Multiple definitions and variables within the research have emerged in attempts to articulate a single definition of classroom engagement (Azevado, 2015). Yet, a widely agreed upon definition and measurement of engagement still does not exist.
The findings presented emphasize participants’ understanding of the importance of Cooper’s (2011) Classroom Engagement Framework’s “Connective Teaching” as the foundational point of entry to engaging students within the Math 1 classroom setting.
Furthermore, the findings present the unique challenges faced by Math 1 teachers as they teach primarily freshmen who need to learn content as well as skills for success within the Math 1 classroom and in high school.