Bulletin of the American Physical Society
2020 Annual Meeting of the APS Four Corners Section (Virtual)
Volume 65, Number 16
Friday–Saturday, October 23–24, 2020; Albuquerque, NM (Virtual)
Session L05: Condensed Matter Physics IVLive
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Chair: David Dunlap, University of New Mexico |
Saturday, October 24, 2020 11:00AM - 11:12AM Live |
L05.00001: Spin Dependent Transport in Magnetic Tunneling Junctions Magda Andrade Magnetic Tunneling Junctions (MTJ) are the basis for developing future high density and lower energy devices, such as applications for magnetic random-access memory (MRAM) or read heads of a Hard Disk Drive. MTJs are spin-dependent devices that consist of a sandwich-like structure of ferromagnet-insulator-ferromagnet (FM/I/FM) layers, where each FM has their own magnetization under an external magnetic field or spin polarized current, where switching can be achieved. Furthermore, in order to achieve such an advancement, our goal is to get higher Tunneling Magnetoresistance (TMR) by employing new materials, new structures, or new fabrication processes. Here we investigated the effectiveness of using an epitaxial layer, MgO barrier, and determining the barrier thickness to achieve large spin-polarization which will help us achieve high TMR. Furthermore, through a wide range of samples, I will illustrate the correlation between TMR and barrier thickness in MTJs with in-plane and perpendicular anisotropy.. [Preview Abstract] |
Saturday, October 24, 2020 11:12AM - 11:24AM Live |
L05.00002: Observation of Multiple-Electronic-Domain Bulk Heterojunctions in Non-Fullerene Organic Photovoltaic Active Layers using Cross-Sectional Scanning Tunneling Microscopy and Spectroscopy Rabindra Dulal, Ganesh Balasubramanian, Wei Chen, TeYu Chien It is widely believed that the active layers of the state-of-the-art organic photovoltaic cells (OPVCs) exhibit bulk heterojunctions (BHJs). While new non-fullerene molecular combinations are explored in the applications of OPVC, whether if the molecular domain structures still follow the BHJ architecture is an important question to answer. Here, with cross-sectional scanning tunneling microscopy and spectroscopy (XSTM/S), isotropic electronic domains are observed using the d$I$/d$V$ mapping. In addition to the electron donor (PBDB-T-2F rich) and acceptor (IT-4CL rich) domains, an intermediate electronic domain is observed. Furthermore, the evolution of the isotropic molecular domain random structures as a function of the depth of the film is presented. The confirmation of the intermediate domains and the depth dependent nano-structural evolution may provide important insights on improving the OPVC performance. [Preview Abstract] |
Saturday, October 24, 2020 11:24AM - 11:36AM Live |
L05.00003: Grain Boundary Representation and Property Prediction J. Spendlove, E. Homer, L. Seraphin, G. Hart A grain boundary (GB) is the interface between distinct crystals (or grains) of a solid, crystalline structure. The type and frequency of these GBs in a material can exert significant influence on macroscopic material properties including strength, thermal conductivity, hydrogen embrittlement and corrosion resistance; however, much is still unknown about these connections. Our aim is to develop and use expressive, smoothly varying atomic descriptions of GB structure to enable effective machine learning property prediction. One of the most promising atomic models that enables GB structure representation is the Smooth Overlap of Atomic Positions descriptor, or SOAP, which utilizes Gaussian density distributions and spherical harmonics. The SOAP characterization can be processed into two descriptors that have been developed by our group to enable machine learning: the Averaged SOAP Representation (ASR) and the Local Environment Representation (LER). In previous works, machine learning using both ASR and LER have been shown to successfully predict material properties based on GB structure. This presentation focuses on these methodologies, their integration into the Python-based scientific code \texttt{pyrelate}, and their effect on the quality of machine learning. [Preview Abstract] |
Saturday, October 24, 2020 11:36AM - 11:48AM Live |
L05.00004: Small energy gap of CrBr3 revealed by Scanning Tunneling Microscopy and Spectroscopy (STM/S) Dinesh Baral, Zhuangen Fu, Andrei S. Zadorozhnyi, Rabindra Dulal, Aaron Wang, Narendra Shrestha, Uppalaiah Erugu, Jinke Tang, Yuri Dahnovsky, Jifa Tian, TeYu Chien CrBr$_{\mathrm{3}}$ is a 2D magnetic van der Waals (vdW) material. Despite the great attention on the magnetic properties, the electronic properties of the CrBr$_{\mathrm{3}}$ are relatively unexplored. The energy gap of CrBr$_{\mathrm{3}}$ is believed to be in the range of 1.68-2.1 eV based on the optical measurements, while the DFT calculations in literature exhibited an even larger deviation of the energy gap values. Here, we present the measurements of the CrBr$_{\mathrm{3}}$ flakes (both thin and thick) by using scanning tunneling microscopy and spectroscopy (STM/S). Along with the DFT calculations, a small energy (0.57 \textpm 0.04eV) is revealed. The multiple peak d$I$/d$V$ spectra were measured on a defect free topography region. Excellent agreements between the reported optical transitions in literature and peak pair energy differences in our d$I$/d$V$ data further confirm that all the measured peaks are intrinsic to CrBr$_{\mathrm{3}}$, and leads to the small energy gap determined by the two peaks closest to the Fermi energy. DFT calculation with U $=$ 5 eV and J $=$ 3 eV reproduces this energy gap. Last, edge degradation was observed on mono- and bi-layer flakes due to \textasciitilde 15 minutes air exposure during sample transfer. These observations provide important information towards the fundamental understanding of CrBr$_{\mathrm{3}}$. [Preview Abstract] |
Saturday, October 24, 2020 11:48AM - 12:00PM Live |
L05.00005: The Analysis of Magnetite Nanoparticles Allowed to Warm through Superparamagnetic Transition Daniel McPherson Magnetic nanoparticles (NPs) have a wide range of applications, from engineering to medicine. Understanding the properties of magnetic nanoparticles provides insight that enhance and expand their use. There are a number of computational methods that are employed to study magnetic NPs in order to determine their magnetic behavior in various field and temperature environments, such as the dynamics of the magnetic fluctuations throughout the superparamagnetic blocking transition. I will present on analysis of these magnetic dynamics for magnetite (Fe\textunderscore 3O\textunderscore 4) nanoparticles. These NPs have been previously characterized through: electron imaging and magnetonetry, x-ray magnetic circular dichroism (XMCD), and x-ray resonant magnetic scattering (XRMS). Here we are using coherent x-ray resonant magnetic scattering (C-XRMS) to access the dynamics of magnetic fluctuations. The data was collected at the SLAC synchrotron facility from assemblies of 11 nm magnetite NPs. In this experiment, series of C-XRMS speckle patterns are collected at subsequent times and cross-correlated, to follow the dynamics of fluctuations. I will compare two separate methods of correlation: the standard punctual photon correlation method, and a two-dimensional spatial correlation method using spatial features in the speckle patterns. [Preview Abstract] |
Saturday, October 24, 2020 12:00PM - 12:12PM Live |
L05.00006: Developing an easy to install application for intuitively analyzing data from muon spin spectroscopy. Kevin Petersen, Benjamin Frandsen Muon spin spectroscopy is a method of investigating the magnetic properties of various types of condensed matter by implanting a beam of spin polarized muons in a small sample of the material and analyzing the precession or relaxation of the muon spin in the local magnetic field. The data we get from these experiments and the programs currently used to visualize or analyze the date can be cumbersome to download and work with. Our project was to create a smaller program that was easier to install but still provided the necessary features and a more intuitive interface with which to analyze the data. [Preview Abstract] |
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