Dissertation Defense Announcements

Over the last few decades, optical technologies utilizing organic materials in the solid state have become ubiquitous. In the solid state, contrary to their constituent molecules, organic materials exhibit energy level continuums along which excited-state energy (excitons) can diffuse. Controlled and efficient management of excitons in systems with numerous organic molecules packed in limited degrees of freedom has been the governing principle for organic optoelectronic devices. However, given the net neutral electrical charge of an exciton and the complex excited-state energy landscape in organic molecular aggregates, efficient management of excitons becomes a challenging task.
In this lecture I will present three significant studies that explore the manipulation of exciton behavior in organic solid-state materials through self-assembly techniques. By modulating molecular packing in the solid state, the energetics and photophysical properties of the chromophores can be effectively controlled, opening up avenues for advanced optical and photonic applications. The successful modulation of exciton diffusion in Zn-metalloporphyrin solution-processable thin films and the establishment of a structure-photophysics correlation in disubstituted alkoxyphenyl thiazolo[5,4-d]thiazole (TTz)-based crystals demonstrate the potential of this approach. Furthermore, the controlled formation and relaxation kinetics of excimers in asymmetric TTz films offer a novel route to achieving high luminescence efficiency, paving the way for a new generation of optical and photonic devices. These findings highlight the importance of self-assembly as a powerful tool for tailoring the behavior of excitons in organic chromophores, leading to significant advancements in various fields.

Values-based leader behavior is commonly referenced by scholars and practitioners as an effective style of leadership. Problematically, multiple definitions of the concept exist that are either ambiguous, tautological, or valanced. Additionally, the concept has been researched almost entirely via questionnaires with little triangulated evidence. The current study reviews previous conceptualizations of values-based leader behavior as well as the key components of leadership, values, and behavior to arrive at a new conceptualization framed from a signaling theory perspective: goal-oriented action or inaction signaling an individual’s, organization’s, or society’s value structure. Then, I review three commonly referenced manifestations of values-based leader behaviors (charismatic leader tactics, ethical leader signals, and transformational leader behaviors) and make the case that pay-for-performance strategies too are strategies that can signal one’s value structure. Using a pre-registered experimental design, I explore the extent to which each of these values-based leader behaviors influence stakeholder in- and extra-role behavior compared to a control condition in a realistic text labeling task. Results found that the pay-for-performance strategies were strong predictors of both in- and extra-role behavior, charismatic leader tactics were strong predictors of extra-role behavior, and the control condition produced the least net output for in- and extra-role behavior combined. I conclude with a discussion of the theoretical and practical implications as well as future research directions.

Materials science aims to explore the properties and behaviors of different materials, from metals to advanced carbon structures. This dissertation focuses on three distinct areas of study: Inconel Alloy 740H, polycrystalline graphene, and tetragraphene (TG).
The first part of this work concentrates on developing and validating a Chaboche unified constitutive model. This model incorporates both nonlinear isotropic and kinematic hardening rules to accurately predict the stress-strain behavior of Inconel Alloy 740H, a high-temperature nickel-based superalloy. The material parameters of the model are determined and its accuracy validated through experimental data obtained from uniaxial strain-controlled loading tests across a wide temperature and strain ranges.
The second part explores the mechanical properties of polycrystalline graphene, bridging scales from nanoscale to macroscale through a multiscale molecular dynamics (MD)–finite element (FE) modeling approach. By studying the behavior of graphene sheets with different grain boundaries and atomic structures, insights are gained into the influence of grain size on mechanical properties like the Young modulus and fracture stress.
The third part of this dissertation investigates the mechanical properties of tetragraphene (TG), a quasi-2D semiconductor carbon allotrope, with a focus on addressing graphene's limitations in electronic applications. Through MD simulations, the research examines TG's fracture properties under mixed mode I and II loading, considering variables such as loading phase angle, crack structure, and temperature.

Autonomous vehicles have gained huge interest across private industry, academia, government, and the public because they promise higher road efficiency, improved safety, better energy consumption, and improved emissions. However, the widespread adoption of autonomous vehicle technology will likely take place over several years (if not decades) as the technology becomes more widely accepted by the general public and more cost-effective. Therefore, there will be a long period of time when we have both autonomous and human-driven vehicles sharing the same road and it is essential to develop traffic management strategies that take the uncertainty associated with the heterogeneity in the traffic networks into account. Furthermore, it is crucial to understand the extent to which these control strategies improve the performance of the traffic network.
This research aims to develop, enhance, and validate hierarchical infrastructure-based control framework designs for improving the mobility of large-scale heterogeneous traffic networks. In this work, heterogeneity is defined as a multi-vehicle traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs), distinguished by their operational characteristics and controllability. To capture the realistic nature of large-scale heterogeneous traffic networks, we adopt the heterogeneous (multi-class) METANET model wherein the density and velocity dynamics of each vehicle class in each cell are described mathematically.
Moreover, in this research, we propose a hierarchical distributed infrastructure-based control framework to manage large-scale heterogeneous traffic networks. At the lower-level, we employed the Distributed Filtered Feedback Linearization (D-FFL) controller which only requires limited information from the plant model. The purpose of this controller is to track the desired density of each vehicle class in the target cells which is set by the upper-level controller. D-FFL tracks the reference density by controlling the suggested velocity of vehicles in the target cell and its upstream cell. At the upper-level, in our initial design, a Distributed Extremum-Seeking (D-ES) controller is designed and implemented to find the optimal operating densities of each vehicle class in the target cells over time. Gradient-based D-ES is a model-free, real-time adaptive control algorithm that is useful for adapting control parameters to unknown system dynamics and unknown mappings from control parameters to an objective function. To improve the performance of the designed hierarchical controller and reduce the convergence time, we designed and implemented Lyapunov-based Switch Newton Extremum Seeking (LSNES) at the upper level of the hierarchy to feed the optimal density of each vehicle class in the target cells to the lower-level controller. One of the key distinctions between the Newton algorithm and the gradient algorithm is that the convergence of the former is not solely contingent on the second derivative (Hessian) of the cost map and it is user-assignable.
Finally, we established a MATLAB-VISSIM COM interface that allows closed-loop control of a simulated traffic scenario in PTV-VISSIM to test and validate the effectiveness of the distributed control approaches in large-scale traffic networks. The simulation results show that our control framework design can effectively reduce congestion and prevent congestion back-propagation during peak hours in large-scale traffic networks.