Physics Commons^™

Dynamics Of Spin And Charge Of Color Centers In Diamond Under Cryogenic Conditions, Richard G. Monge

Dissertations, Theses, and Capstone Projects

Individual quantum systems in semiconductors are currently the most sought-after platform for applications in quantum science. Most notably, the nitrogen-vacancy (NV) center in diamond features a defect deep within the electronic bandgap, making it amenable for precise manipulation to help pave the way to perform fundamental quantum physics experimentation. The NV center also offers long coherence times and versatile spin-dependent fluorescent properties, making it an ideal candidate for a nanoscale magnetometer. Furthermore, multi-color excitation offers deterministic charge state manipulation. While ambient operation has been key to their appeal, bringing NVs to cryogenic conditions opens new opportunities for alternate forms of …

The Role Of Nuclear Quantum Effects In Supercooled Water And Amorphous Ice, Ali H. Eltareb

Dissertations, Theses, and Capstone Projects

Water is one of the most important substances on Earth and plays a fundamental role in numerous scientific and engineering applications. Interestingly, water behaves much differently than other liquids. For example, water shows an anomalous density maximum at 277 K, the solid phase (ice) is less denser than the liquid, and its thermodynamic response functions, such as the specific heat C_P and isothermal compressibility κ_T, also increase anomalously upon cooling. In the glassy state, water can exist in two different forms, low-density and high-density amorphous ice (LDA and HDA). While water has been scrutinized for many centuries, …

Nonlinear Processes In Room Temperature Exciton-Polaritons, Prathmesh Deshmukh

Dissertations, Theses, and Capstone Projects

Strong light-matter coupling in solid state systems is an intriguing process that allows one to exploit the advantages of both light and matter. In this context, microcavities have become essential platforms for studying the strong coupling regime, where hybrid light-matter states known as exciton-polaritons form, leading to enhanced light matter interaction, modified material properties, and novel quantum phenomena. In this thesis, we explore the phenomenology of exciton-polaritons in strained TMD microcavities, 2D perovskites, fluorescent proteins and organic dyes encompassing thermalization, polariton lasing, and the observation of nonlinear effects.

Transition metal dichalcogenides (TMDs) have emerged as a remarkable class of two- …

The Study Of Excitons In 2d Novel Materials And Their Van Der Waals Heterostructures In The Magnetic Field, Anastasia Spiridonova

Dissertations, Theses, and Capstone Projects

This research focuses on the direct and indirect excitons in Rydberg states in monolayers, bilayers, and van der Waals heterostructures composed of 2D semiconductors in the presence of the external magnetic field. In our work, we report binding energies of direct and indirect excitons in Rydberg states, the energy contribution from the magnetic field to the binding energies of magnetoexcitons, and diamagnetic coefficients (DMCs) of magnetoexcitons.

We study isotropic materials: transition metal dichalcogenides, TMDCs (WSe₂, WS₂, MoSe₂, MoS₂), and Xenes (silicene, germanene, stanene), and anisotropic materials: phosphorene and transition metal trichalcogenides, TMTCs …

Revealing The Three-Dimensional Magnetic Texture With Machine Learning Models, Shihua Zhao

Dissertations, Theses, and Capstone Projects

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.

We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, …