The list of projects below contains both experimental and computational projectcs. Many of the REU programs at the University of Florida campus are planning to run in person this summer, while many physics REU program across the country are planning on running virtually. Given the uncertainties involved with COVID-19 we are keeping our options open at this point. If you would prefer an experimental project, please choose one and we will work with you to have a backup computational project in case we do go virtual.

Electric field effect on the magnetism of La0.33Pr0.34Ca0.33MnO3 , Prof. Amlan Biswas
Revealing Long-Lived Nuclear Spin State in Gases via Symmetry-Breaking Interactions , Prof. Russ Bowers
Dimensional Reduction of Biophysical Data Prof. Purushottam Dixit
Protein phase separation and its implication in cancer , Prof. Juan Guan
High-pressure studies of unconventional superconductors , Prof. James Hamlin
Computational Exploration of Geometries for Organic Solar Cells Prof. Selman Hershfield
Numerical simulations: investigating quantum turbulence using NEMS/MEMS resonators Prof. Yoonseok Lee
Entropy-stabilzed Cuprate Superconductors Prof. Ryan Need
Modeling Spin-Lattice Coupling in Molecular Magnets Prof. Xiaoguang Zhang


Electric field effect on the magnetism of La0.33Pr0.34Ca0.33MnO3, Prof. Amlan Biswas
        Ferromagnetic materials such as iron can be permanently magnetized using a magnetic field and are therefore used for data storage. In certain materials it is also possible to control the magnetism using an electric field which is of great interest both due to the underlying physics and possible device applications. In this project we will study a compound with chemical formula La0.33Pr0.34Ca0.33MnO3 (LPCMO), which is a ferromagnet and non-magnetic phases coexist forming a multiphase state in which ferromagnetic islands are formed in a non-magnetic matrix. While the material is a crystalline solid, the ferromagnetic phase can move like a fluid in the non-magnetic matrix. The main goal of this project is to use a time varying electric field to reshape the fluid-like ferromagnetic phase and thus control the magnetic properties using an electric field.
        The REU student will characterize the properties of the thin film samples using scanning probe microscopy, transport, and magnetization measurements. Once the basic properties have been optimized, the student will study the effect of a time varying electric field on the resistance and magnetization of LPCMO. The main techniques that the student will learn are scanning probe microscopy, low temperature resistance and magnetization measurements, and photolithography for fabricating micro/nanostructures of LPCMO.
        As a computational project we simulate electrode configurations which generate non-uniform electric fields in the LPCMO compound and model the time-dependent movement of the fluid-like ferromagnetic phase. The simulation results will be compared to the experimental data obtained in the Biswas lab and will also be used to design new device configurations for generating electric field effects in ferromagnetic LPCMO.
        The REU student will use Python and C++ programs to model the behavior of ferromagnetic and metallic regions in a non-uniform electric field. The programs used will be completely understandable for the student since they are based on undergraduate level electricity and magnetism. The student will also gain experience in condensed matter systems where the crystal structure determines their magnetic and electronic properties leading to tunability and possible applications in data storage devices.

Revealing Long-Lived Nuclear Spin States in Gases via Symmetry-Breaking Interactions, Prof. Russ Bowers
        In molecular systems containing well-isolated pairs of coupled proton spins-1/2 embedded in molecules with a high degree of symmetry, a long-lived mode of spin order called singlet order may exist. The singlet order, which corresponds to the population difference between the nuclear singlet and triplet states, relaxes more slowly than ordinary magnetization, since it is immune to the dominant intra-pair dipole-dipole relaxation mechanism. Such phenomena have been long known in the context of nuclear spin isomerism, which occurs in small, highly symmetrical molecules with a high degree of rotational freedom, ortho- and parahydrogen being the seminal example. In spin isomerism, the Pauli principle, which constrains the overall symmetry of allowed quantum states, entangles the spatial wavefunctions and spin states, such that nuclear energy levels are invested with energy differences associated with the spatial quantum mechanics of the rotating molecules. Singlet order has potential applications ranging from biomedicine to quantum computing. This project involves the experimental preparation and storage of long-lived states as well as phenomena for revealing them using symmetry breaking interactions in nanotubes, microporous solids, or surface adsorption.
        The REU student will participate in experiments for the preparation and storage of long-lived states, as well as their application in studying symmetry breaking interactions in nanotubes, microporous solids, or surface adsorption. The student will learn about state of the art nuclear magnetic resonance, low temperature techniques, and the quantum mechanics of symmetry and symmetry breaking.
        Should the face-to-face REU program not move forward as hoped, the project will switch to a fully computational one. The student will learn how to use the SpinDynamica package running under Mathematica to perform spin dynamics simulations of singlet-to-magnetization conversion induced by magnetic field cycling, fluid flows through magnetic field gradients, and radio frequency pulse sequences. The results will be used in the lab to optimize the experimental spin-order conversions.

Dimensional Reduction of Biophysical Data, Prof. Purushottam Dixit
        Biophysical systems are characterized by their complexity where typically thousands of components interact with each other. The data describing these systems, for example, single cell RNA sequencing or microbiome data are therefore extremely high dimensional. Akin to statistical physics, a central task in biophysics is to identify a handful of descriptors that explain the observed data. In data science a common technique used to discover relationships is called dimensional reduction, which allows one to transform higher dimensional data to a lower dimensional space. For example, in statistical physics a small number of intensive variables can fully explain the probability distribution over a large number of phase space variables.
        In the project the REU student will learn and apply non-linear dimensionality reduction to sample biophysical data sets including microbiome data and single cell RNA sequencing data. Specifically, the student will explore how to construct geodesics between data points in the high dimensional data set. The basic formalism of this approach has already been written in Mathematica, and the REU student will translate that code into Matlab or Python and then test it on real data sets. Some computational experience is required and experience with statistical physics is helpful. The REU student will learn about data science and its application to biophysical systems as well get hands on computational experience.

Protein phase separation and its implications in cancer, Prof. Juan Guan
        Phase separation is a concept well described in Physics where a system exhibits two or more distinct yet co-existing phases. Protein phase separation is a phenomenon discovered recently in biological systems and has been shown to play a critical role in a variety of biological processes. Through high-resolution cell imaging studies, it is shown that under physiologically conditions there are micrometer-scale structures spontaneously assembled in the cells that are composed of proteins. In shape contrast to the traditional subcellular compartment and organelles, these protein-based structures have been found to be membrane-less. As knowledge is rapidly expanding in this area, it is increasingly recognized that many subcellular structures are formed through protein phase separation in both normal physiological processes and in processes involving diseases like cancer.
        The REU student will express a variety of cancer-causing proteins in model cell lines and monitor their subcellular localization and cell signaling outcome in the context of cancer. The project will employ cutting-edge optics and biophysics techniques. The REU student will gain experience with working at the interface of physics and biology, biophysical sample preparation, high-resolution fluorescence imaging, and quantitative data analysis skills. If we cannot accommodate the student physically in lab due to the pandemic, the student will participate in the project by developing image analysis algorithm to extract information from high-resolution fluorescence images we generate in lab.

High-pressure studies of unconventional superconductors, Prof. James Hamlin
        Superconductivity is a phenomena with enormous applications potential. However, this promise has yet to be fully realized, in part because of our incomplete understanding of the conditions under which superconductivity develops in certain materials. The Hamlin group is utilizing applied high pressures both to understand the properties of known superconductors and to help discover new ones. Pressure is a powerful control parameter, capable of rapidly and continuously tuning a single sample between insulating, metallic, magnetic, or superconducting ground states.
        The REU student will participate in the synthesis of new materials, particularly in single crystalline form, and will learn about the characterization of these materials by x-ray diffraction, electrical transport, magnetic susceptibility, and specific heat measurements. The student will also gain experience in the use of diamond anvil cells, which allow the application of pressures spanning the range from kilobars (the pressure at the bottom of the ocean) to megabars (nearing the pressure at the center of the earth).
        If we cannot accommodate the student in the laboratory due to the pandemic, the student will engage in a complementary part of the project that is focused on data mining and machine learning methods to identify materials that exhibit novel high temperature superconducting states at high pressure.

Computational Exploration of Geometries for Organic Solar Cells, Prof. Selman Hershfield
        Solar cells composed of organic semiconductors have the potential to greatly reduce the cost of solar cells. However, these solar cells are less effiecient than traditional solar cells because the active region for converting light to electric current is much smaller than with conventional semiconductors like silicon. This inefficiency is partially overcome by mixing the p-type and n-type organic semiconductors to create more surface area in what is called a bulk heterojunction. The question addressed in this project is by how much can one increase the efficiency by mixing the p-type and n-type materials in an organized rather than random fashion. For example, one could consider pillars of a p-type material embedded in an n-type material. Much more complex geometries are possible to explore computationally.
        The REU student will use existing three dimensional simulation software after first writing a simplified one dimensional version themselves to understand the physics involved. This software was used by a previous REU student student to study the effect of varying the percentage of p-type and n-type material in a random sample (REU paper, CUWiP poster). The REU student will learn about the physics of organic solar cells and how to run simulations. Depending on the student background and interests, it is also be possible to run jobs on the University of Florida's HiPerGator supercomputer to attempt to allow the computer to find an optimized geometry.

Numerical simulations: investigating quantum turbulence using NEMS/MEMS resonators, Prof. Yoonseok Lee
        Superfluid Helium-4 supports a breadth of exotic physical phenomenon, one of which is quantum turbulence. While quantum turbulence is a chaotic system that lacks an analytical solution just like its classical counterpart, quantum mechanics imposes restrictions on the vorticity of a superfluid. This leads to stable topological defects with quantized circulation called quantum vortices, which form the basis of quantum turbulence. Superfluid Helium-4 also hosts elementary excitations in the form of phonons and rotons. These quasi-particles carry information about physical processes taking place within the superfluid. By placing vibrating objects like nano- or micro-electromechanical systems (NEMS or MEMS) in superfluid and measuring their mechanical responses, we can extract information about the properties of the fluid, as well as statistical properties of vortices and quasi-particles. When faced with trying to understand such complex situations, simulation is one of the most powerful tools we have at our disposal. Not only does it help in optimizing experimental setups, but also for discovering relationships by directly probing the experimental parameter space.
        The REU student will model and simulate mechanical oscillators in the presence of vortices and/or quasi-particle showers using COMSOL Multiphysics to see how they affect the damping of the device. In turn, this will show how such damping will manifest itself through changes in the oscillator's resonance frequency, response amplitude, and the broadening of the resonance response. The REU student will learn about the key physical principles of superfluid helium, mechanical oscillators, and noise in measurements in addition to learning how to actually build models and run simulations using the COMSOL Multiphysics software.

Entropy-stabilized Cuprate Superconductors, Prof. Ryan Need
        Copper oxide (cuprate) superconductors hold the record for highest superconducting temperature of any material, at ambient pressure. However, that record is still well below room temperature, limiting the application of cuprate superconductors in everyday technology, and there are still gaps in our understanding of these superconductors. The goal of this project is to gain new insight into the physics of cuprate superconductors by introducing a large amount of disorder on specific sites (i.e. atomic positions) in the cuprate's crystal lattice. To do this, we will apply a phenomenon known as entropy-stabilization, which enables the formation of a single, homogenous phase with large disorder by increasing the system’s entropy. Entropy stabilization has not yet been achieved for the cuprate superconductors.
        The REU student working on this project will use solid-state synthesis methods (e.g. grinding, pressing, and firing powders) to create samples entropy-stabilized versions of cuprate superconductors based on the prototype YBa2Cu3O7 (YBCO). The student will vary their precursor composition and/or heat treatment, then use powder X-ray diffraction to analyze the crystal structure of their samples and determine relationships between synthesis and the resulting crystal structure. On high-purity samples, the student will collect electrical conductivity and magnetization measurements to determine the material's superconducting behavior and how it depends on the crystal structure and processing.

Modeling Spin-Lattice Coupling in Molecular Magnets, Prof. Xiaoguang Zhang
        Single molecule magnets have magnetic atoms such as Mn which are coupled so as to behave like a single large magnetic moment. Potential applications of single molecule magnets include magnetic bits for data storage and qubits for quantum computers. Prof. Xiaoguang Zhang is studying the spin-lattice coupling in molecular magnets. When the magnetic moment in a molecular magnet changes its orientation, there is a deformation in the positions of the atoms in the molecule.
        The REU will apply computational materials techniques such as the semi-empirical quantum chemistry method to model spin-lattice coupling in molecular magnets. They will also have the opportunity to collaborate with others in his group using density functional theory and machine learning techniques. Some computational experience is preferable for this project. The REU student will learn some modern computational materials modeling techniques and the physics and chemistry of single molecule magnets.