Eric Sembrat's Test Bonanza

Exceptional ballistic transport was observed in sidewall epitaxial graphene nanoribbons on SiC (SWGNRs) at room temperature[1]. These objects are of fundamental interest as they provide direct access to charge neutral graphene with excellent transport properties. In this thesis, beyond sidewalls, we fabricate epitaxial graphene devices on different crystal faces on SiC, including the Si-face and non-polar facets. We introduce novel fabrication processes flows that have high temperature annealing and Al2O3 as a protective layer to reduce the edge roughness of ribbons and the contamination from resist residue. Then we discuss transport measurement results of graphene nanoribbons on Si-face as well as on non-polar SiC facets, which might reveal a ballistic edge state channel 0+ with mean free path on the order of µm and another edge state channel 0− activated by temperature. These special epitaxial graphene edge states are interesting from a fundamental physics standpoint and may find applications in future graphene electronic devices.

[1] Baringhaus, Jens, et al. "Exceptional ballistic transport in epitaxial graphene nanoribbons." Nature 506.7488 (2014): 349-354.

Online via Bluejeans: https://bluejeans.com/243952537/9010 

Title: Astrometric Methods for Star-Based Navigation

Abstract: Humans have used stars for navigation since antiquity. It should come as no surprise, therefore, that star sightings were amongst the first observables used to navigate exploration spacecraft (both crewed and robotic). Today, star observations remain central to the navigation of almost every modern spacecraft. However, as space exploration missions become more ambitious and the technologies for star-based navigation improve, we begin to run into limitations of existing methods for observing and cataloging stars. In this seminar we will discuss a variety of recent advancements at the intersection of astronomy and navigation, such as generalized astrometric calibration and velocity estimation from stellar aberration. The objective is to familiarize the CRA community with current problems at the intersection of astrophysics, astrometry, and spacecraft engineering — with the hope of fostering an a growing dialog between physics and aerospace engineering at Georgia Tech.

Advanced quantum systems are integral to scientific research and modern technology enabling a wide range of emerging applications. Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, are naturally relevant in this context due to their unprecedented spatial and field sensitivity, single-spin addressability, and remarkable functionality over a broad temperature range. Many of these advantages derive from the quantum-mechanical nature of NV centers that are endowed by excellent quantum coherence, controllable entanglement, and high fidelity of operations. In this talk, I will present our recent work on developing state-of-the-art NV-based quantum sensing and imaging techniques and demonstrate their direct applications to address the current challenges in both condensed matter physics and quantum sciences and technologies. Specifically, we have utilized NV centers to probe the exotic charge and spin properties of emergent quantum materials including high-Tc superconductors, magnetic topological materials, and antiferromagnetic insulators. We also integrate NV centers with functional magnetic devices to develop hybrid quantum systems, promoting the role of NV center at the forefront of quantum technologies. Lastly, I will briefly discuss our ongoing efforts to explore quantum sensing using emergent color centers beyond NV centers.

The LIGO and Virgo detectors have detected 90 gravitational wave events from collisions of pairs of black holes and neutron stars. The astrophysical interpretation of these events gives insights into the source properties and relies on sophisticated modeling. In this thesis, I explore biases introduced in this interpretation by un-modeled astrophysical or terrestrial effects, and probe the validity of general relativity and nuclear effects in post-mergers of binary neutron stars. For this, I use BayesWave, a wavelet-based algorithm that is sensitive to a wide range of waveform morphologies. I introduce the analysis method, its characterization via simulation studies, and its implementation on data from the first three observing runs of LIGO and Virgo.

Organic based polymer materials have an abundance of potential applications due to the ability to tailor their molecular components and network topology. The experimental work featured in this thesis focuses on a small selection of quasi-2D materials within two subclasses of these polymers, metal organic frameworks (MOFs) and covalent organic frameworks (COFs). Both are porous networks of organic molecules linked together by metal ions (MOFs) or covalent bonds (COFs). In layered 3D variants, this porosity grants the material an impressive internal surface area available for catalysis, gas storage, and molecular separations. In 2D, the organic molecular network produces a variety of electronic structures, with pi-bonding molecular orbitals predicted to create both Dirac bands and flat bands. This work presents studies of the atomic and electronic structure of these materials by ultrahigh vacuum (UHV) scanning probe microscopy (SPM).

Abstract: The general solution of many-particle quantum systems is exponentially complex, requiring a quantum computer to solve. But for many of the most important properties of realistic experimental systems, the exponential complexity is avoidable, because while the entanglement of the system is high enough to make the system interesting, it is much lower than quantum mechanics allows. Tensor network methods exploit this low entanglement to enable simulations of many quantum systems on an ordinary computer.

In this talk, I will give an overview of these ideas and methods and then detail our recent progress in simulating high temperature superconductors, systems with exotic entangled states which we are increasingly able to understand through simulation.

Turbulence is one of the most ubiquitous features of the world around us. Its signatures can be found at every scale, in the small eddies of streams to the vortex wakes of airliners, from ocean currents to the cosmic swirls of interstellar gases. Fluids like these have been studied for centuries, but while many advances have been made toward understanding the captivating patterns that they create, predicting the evolution of turbulent fluids remains one of the most notoriously difficult unsolved problems in classical physics.

The Navier-Stokes equation is a deterministic, high-dimensional partial differential equation which allows us to make limited predictions about fluids under particular conditions. However, a characteristic feature of turbulence is that it is chaotic, meaning the evolution of a turbulent fluid is highly sensitive to its initial conditions. In practice these initial conditions can never be measured precisely enough to make long-term predictions tractable, and often measurements cannot be made of every physical quantity that goes into the Navier-Stokes equation. Physicists have thus turned recently to developing data-driven computational fluid models to gather statistics about recurring patterns that guide the flow's evolution, which can then be used to characterize an experimental flow.

In this dissertation, two data-driven approaches are explored in the context of a shallow, driven fluid flow, an experimental approximation of two-dimensional (2D) Kolmogorov flow. The first approach provides experimental evidence for the statistical role of exact, unstable solutions of the Navier-Stokes equation known as exact coherent structures. In particular, periodic orbits are shown to play an important role in guiding the dynamics of a turbulent flow, as the fluid flow spends a large amount of time shadowing the most relevant orbits as predicted by periodic orbit theory. In the second approach, a weak formulation of the symbolic regression algorithm is used to develop a model of the 3D fluid using only a 2D approximation of the velocity field. The model can then be used to recreate the pressure and forcing fields, yielding a modified, quasi-2D Navier-Stokes equation that governs the flow and agrees with the first-principles model derived in previous studies. Finally, as fluid properties change, the variation in the coefficients of this quasi-2D model are also in agreement with predictions from previous work, and provide a useful diagnostic tool for common experimental errors. The substantial progress provided by this dissertations suggests that physics-informed data-driven analysis of turbulent flows provides an important validation of existing models and theories.

Spatiotemporal chaos, especially fluid turbulence, is ubiquitous in nature but can be difficult to characterize because analytic solutions of the strongly nonlinear partial differential equations that govern the behavior are often intractable. However, the topology of structures observed in both experiments and numerical simulations of spatiotemporally chaotic flows can provide insights into the underlying dynamics. The topological properties of spatiotemporally chaotic data can be investigated using persistent homology, a technique of topological data analysis.

In this thesis, persistent homology is used to investigate the dynamics of two different spatiotemporally chaotic fluid flows. First, in the Kuramoto-Sivashinsky equation, a popular "toy model" system that mimics the spatiotemporal chaos exhibited by fully turbulent fluid flows, persistent homology is used to detect and quantify shadowing of exact coherent structures (ECS). ECS are invariant solutions to the governing equations that structure the dynamics of spatiotemporal chaos. Persistent homology is found to be an advantageous tool for quantifying shadowing in the Kuramoto-Sivashinsky equation because it quotients out the system's continuous symmetry. Second, in Rayleigh-B\'enard convection, persistent homology is used to detect and quantify plumes, which are observable pattern features in experiments and simulations. In simulations, plumes indicate spatial regions of the convective flow in which the leading Lyapunov vector magnitude, a fundamental quantity that characterizes the dynamics of the flow, is high. A long-term goal is to use plumes to connect dynamics in simulations, where the leading Lyapunov vector can be computed, to experiments, where this quantity cannot be observed. This thesis advances research in both of these topics and demonstrates that persistent homology is a powerful tool for analyzing topological structure associated with the dynamics of spatiotemporally chaotic flow.

This dissertation presents three elements of a project to investigate development of a novel solid state particle detector. The detector design incorporates Schottky-diode-connected HEMTs formed of gallium nitride (GaN) layered with aluminum gallium nitride (AlGaN); GaN/AlGaN is an ultra-wide bandgap semiconductor that is also piezoelectric. We hypothesize that particle interactions in the detector cause local and impermanent variations in polarization at the metal-semiconductor interface in the Schottky diode, which can be observed as transient signals at the circuit output. This detector design is based on the bandgap reference circuit, an analog circuit whose output is stable under varying temperature, supply variation, and loading conditions. The circuit design achieves this stability by incorporating two circuit elements whose temperature dependencies have equal but opposite coefficients. We describe the development of this circuit design and present simulation and experimental temperature data that demonstrate improved stability over semiconductor device temperature dependence. To investigate this design, we fabricated two prototypes with discrete components and irradiated them. The first prototype was irradiated at Sandia National Laboratories, where transient signals were observed under alpha and neutron irradiation. The second prototype was irradiated at the Radiological Sciences and Engineering Laboratory in the Boggs Building at Georgia Tech. Analysis of this set of data from the Georgia Tech experiments provides evidence from changes in the frequency spectrum and high values of cross-correlation between trials that alpha particles were observed. However, data from the neutron does not show the same magnitude of changes, so we believe additional filtering and experimentation are required to provide stronger evidence of neutron observation. Finally, we began simulations in Geant4 to start testing the physical mechanism hypothesis; a roadmap of future work is discussed to serve as a guide for future researchers. Ideas for future work regarding circuit simulation, experimental changes, and Geant4 simulation will be shared. In conclusion, this dissertation presents circuit development, irradiation experimentation, and preliminary simulations towards the development of a novel solid state semiconductor detector that utilizes ultra-wide bandgap materials for particle detection.