The recent discovery of bright, room-temperature, single photon emitters in GaN leads to an appealing alternative to diamond best single photon emitters given the widespread use and technological maturity of III-nitrides for optoelectronics (e.g. blue LEDs, lasers) and high-speed, high-power electronics. This discovery opens the door to on-chip and on-demand single photon sources integrated with detectors and electronics. Currently, little is known about the underlying defect structure nor is there a sense of how such an emitter might be controllably created. A detailed understanding of the origin of the SPEs in GaN and a path to deterministically introduce them is required. In this project, we develop new experimental capabilities to then investigate single photon emission from GaN nanowires and both GAN and AlN wafers. We ion implant our wafers with the ion implanted with our focused ion beam nanoimplantation capabilities at Sandia, to go beyond typical broad beam implantation and create single photon emitting defects with nanometer precision. We've created light emitting sources using Li+ and He+, but single photon emission has yet to be demonstrated. In parallel, we calculate the energy levels of defects and transition metal substitutions in GaN to gain a better understanding of the sources of single photon emission in GaN and AlN. The combined experimental and theoretical capabilities developed throughout this project will enable further investigation into the origins of single photon emission from defects in GaN, AlN, and other wide bandgap semiconductors.
Single particle aerosol mass spectrometry (SPAMS), an analytical technique for measuring the size and composition of individual micron-scale particles, is capable of analyzing atmospheric pollutants and bioaerosols much more efficiently and with more detail than conventional methods which require the collection of particles onto filters for analysis in the laboratory. Despite SPAMS’ demonstrated capabilities, the primary mechanisms of ionization are not fully understood, which creates challenges in optimizing and interpreting SPAMS signals. In this paper, we present a well-stirred reactor model for the reactions involved with the laser-induced vaporization and ionization of an individual particle. The SPAMS conditions modeled in this paper include a 248 nm laser which is pulsed for 8 ns to vaporize and ionize each particle in vacuum. The ionization of 1 μm, spherical Al particles was studied by approximating them with a 0-dimensional plasma chemistry model. The primary mechanism of absorption of the 248 nm photons was pressure-broadened direct photoexcitation to Al(y2D). Atoms in this highly excited state then undergo superelastic collisions with electrons, heating the electrons and populating the lower energy excited states. We found that the primary ionization mechanism is electron impact ionization of various excited state Al atoms, especially Al(y2D). Because the gas expands rapidly into vacuum, its temperature decreases rapidly. The rate of three-body recombination (e− + e− + Al+ → Al + e−) increases at low temperature, and most of the electrons and ions produced recombine within several μs of the laser pulse. The importance of the direct photoexcitation indicates that the relative peak heights of different elements in SPAMS mass spectra may be sensitive to the available photoexcitation transitions. The effects of laser intensity, particle diameter, and expansion dynamics are also discussed.
This document contains the design and operation principles for the wind turbine emulator (WTE) located in the Distributed Energy Technologies Laboratory (DETL) at Sandia National Laboratories (Sandia). The wind turbine emulator is a power hardware -in-the-loop (PHIL) representation of the research wind turbines located in Lubbock, Texas at the Sandia Scaled Wind Farm Technology (SWiFT) facility. This document describes installation and commissioning steps, and it provides references to component manuals and specifications.
For 2D-temperature monitoring applications, a variant of EIT (Electrical Impedance Tomography) is evaluated computationally in this work. Literature examples of poor sensor performance in the center of the 2D domains away from the side electrodes motivated this study which seeks to overcome some of the previously noted shortcomings. In particular, the use of ‘sensing skins’ with novel tailored baseline conductivities were examined using the EIDORS package for EIT. It was found that the best approach for detecting a hot spot depends on several factors such as the current injection (stimulation) patterns, the measurement patterns, and the reconstruction algorithms. For a well-performing combination of these factors, tailored baseline conductivities were assessed and compared to the baseline uniform conductivity. It was discovered that for some EIT applications, a tailored distribution needs to be smooth and that sudden changes in the conductivity gradients should be avoided. Still, the benefits in terms of improved EIT performance were small for conditions for which the EIT measurements had been ‘optimized’ for the uniform baseline case. Within the limited scope of this study, only two specific cases showed benefits from tailored distributions. For one case, a smooth tailored distribution with increased baseline conductivity in the center provided a better separation of two centrally located hot spots. For another case, a smooth tailored distribution with reduced conductivity in the center provided better estimates of the magnitudes of two hot spots near the center of the sensing skin.
The DOE Office of Electricity views sodium batteries as a priority in pursuing a safe, resilient, and reliable grid. Improvements in solid-state electrolytes are key to realizing the potential of these large-scale batteries. NaSICON structure consists of SiO4 or PO4 tetrahedra sharing common corners with ZrO6 octahedra. Structure forms “tunnels” in three dimensions that can transport interstitial sodium ion. 3D structure provides higher ionic conductivity than other conductors (β’’-alumina), particularly at low temperature. Lower temperature (cheaper) processing compared to β’’-alumina. Our objective was to identify fundamental structure-processing-property relationships in NaSICON solid electrolytes to inform design for use in sodium batteries. In this work, the mechanical properties of NaSICON sodium ion conductors are affected by sodium conduction. Electrochemical cycling can alter modulus and hardness in NaSICON. Excessive cycling can lead to secondary phases and/or dendrite formation that change mechanical properties in NaSICON. Mechanical and electrochemical properties can be correlated with topographical features to further inform design decisions
Sanz-Matias, Ana; Roychoudhury, Subhayan; Feng, Xuefei; Yang, Feipeng; Cheng, Kao L.; Zavadil, Kevin R.; Guo, Jinghua; Prendergast, David
Given their natural abundance and thermodynamic stability, fluoride salts may appear as evolving components of electrochemical interfaces in Li-ion batteries and emergent multivalent ion cells. This is due to the practice of employing electrolytes with fluorine-containing species (salt, solvent, or additives) that electrochemically decompose and deposit on the electrodes. Operando X-ray absorption spectroscopy (XAS) can probe the electrode-electrolyte interface with a single-digit nanometer depth resolution and offers a wealth of insights into the evolution and Coulombic efficiency or degradation of prototype cells, provided that the spectra can be reliably interpreted in terms of local oxidation state, atomic coordination, and electronic structure about the excited atoms. To this end, we explore fluorine K-edge XAS of mono- (Li, Na, and K) and di-valent (Mg, Ca, and Zn) fluoride salts from a theoretical standpoint and discover a surprising level of detailed electronic structure information about these materials despite the relatively predictable oxidation state and ionicity of the fluoride anion and the metal cation. Utilizing a recently developed many-body approach based on the ΔSCF method, we calculate the XAS using density functional theory and experimental spectral profiles are well reproduced despite some experimental discrepancies in energy alignment within the literature, which we can correct for in our simulations. We outline a general methodology to explain shifts in the main XAS peak energies in terms of a simple exciton model and explain line-shape differences resulting from the mixing of core-excited states with metal d character (for K and Ca specifically). Given ultimate applications to evolving interfaces, some understanding of the role of surfaces and their terminations in defining new spectral features is provided to indicate the sensitivity of such measurements to changes in interfacial chemistry.
The solution processability of ionogel solid electrolytes has recently garnered attention in the Li-ion battery community as a means to address the interface and fabrication issues commonly associated with most solid electrolytes. However, the trapped ionic liquid (ILE) component has hindered the electrochemical performance. Herein, we present a process to tune the properties by replacing the ILE in a silica-based ionogel after fabrication with a liquid component befitting the desired application. Electrochemical cycling under various conditions showcases gels containing different liquid components incorporated into LiFePO4 (LFP)/gel/Li cells: high power (455 W kg-1 at a 1 C discharge) systems using carbonates, low temperatures (-40 °C) using ethers, or high temperatures (100 °C) using ionic liquids. Fabrication of additive-manufactured cells utilizing the exchanged carbonate-based system is demonstrated in a planar LFP/Li4Ti5O12 (LTO) system, where a marked improvement over an ionogel is found in terms of rate capability, capacity, and cycle stability (118 vs 41 mA h g-1 at C/4). This process represents a promising route to create a separator-less cell, potentially in complex architectures, where the electrolyte properties can be facilely tuned to meet the required conditions for a wide range of battery chemistries while maintaining a uniform electrolyte access throughout cast electrodes.
The prevalence of COVID-19 is shaped by behavioral responses to recommendations and warnings. Available information on the disease determines the population’s perception of danger and thus its behavior; this information changes dynamically, and different sources may report conflicting information. We study the feedback between disease, information, and stay-at-home behavior using a hybrid agent-based-system dynamics model that incorporates evolving trust in sources of information. We use this model to investigate how divergent reporting and conflicting information can alter the trajectory of a public health crisis. The model shows that divergent reporting not only alters disease prevalence over time, but also increases polarization of the population’s behaviors and trust in different sources of information.
Grignard reagents of the general formula RMgX (X = Cl-, Br-, I-) have been utilized in various chemistries for over 100 years. We report that replacing the halide in a Grignard reagent with a reactive borohydride anion adds a new synthetic dimension for these influential compounds. We synthesized the series RMgBH4 (R = Et, n-Bu, Ph, Bn) and characterized the reactivity toward both organic and inorganic molecules. Using butylmagnesium borohydride (BuMgBH4) as an exemplar, we demonstrate that these compounds possess unique reactivity due to the presence of reducing borohydride groups, resulting in tandem reactivity with organic amides/esters to generate secondary and primary alcohols. Molecular dynamics simulations indicate the stability of BuMgBH4 is comparable to that of Mg(BH4)2 + MgBu2, validating the Schlenk equilibrium in borohydride Grignard compounds. Metadynamics simulations confirm that the equilibrium is kinetically accessible through solvent-mediated processes. BuMgBH4 also reacts with CO2 and NH3, revealing potential uses for CO2 utilization and as a mixed-anion metal borohydride/amide precursor.
The long-standing problem of predicting the electronic structure of matter on ultra-large scales (beyond 100,000 atoms) is solved with machine learning.