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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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31 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Atomistic modeling of a superconductor–transition metal dichalcogenide–superconductor Josephson junction

Using an atomistic tight-binding model, we investigate the characteristics of a Josephson junction formed by monolayers of MoS 2 sandwiched between Pb superconducting electrodes. We derive and apply Green's function–based formulation to compute the Josephson current as well as the local density of states in the junction. Our analysis of diagonal and off-diagonal components of the local density of states reveals the presence of triplet superconducting correlations in the MoS 2 monolayers and spin-polarized subgap (Andreev bound) states. Our formulation can be extended to other systems where atomistic details and large scales are needed to obtain accurate modeling of Josephson junction physics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Junctions and Superconducting Symmetry in Twisted Bilayer Graphene

Junctions provide a wealth of information on the symmetry of the order parameter of superconductors. Here, we analyze junctions between a scanning tunneling microscope (STM) tip and superconducting twisted bilayer graphene (TBG) and TBG Josephson junctions (JJs). We compare superconducting phases that are even or odd under valley exchange (s- or ƒ-wave). The critical current in mixed (s and ƒ) JJs strongly depends on the angle between the junction and the lattice. In STM-TBG junctions, due to Andreev reflection, the ƒ-wave leads to a prominent peak in subgap conductance, as seen in experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Logical Majorana zero modes in a nanowire network

We present a scheme to use physical Majorana quasizero modes at each junction of a two-dimensional nanowire network to build a logical Majorana zero mode, the location of which is controllable through gate voltages. The wire network is a way to realize a recent proposal to imprint a Kekulé vortex pattern on a honeycomb lattice via gate voltages. We show that a specific type of junction, other than a naive Y or T junction, is needed to realize, without breaking time-reversal symmetry, an artificial “graphene” system with Majorana fermions instead of complex ones at each site. The junction we propose (i) traps exactly one physical Majorana (quasi)zero mode at each site of either a brick wall or honeycomb lattice and (ii) allows this mode to hybridize with all three neighboring sites. Using a lattice of these junctions and starting from an electronic, tight-binding model for the wires, we imprint the voltage patterns corresponding to the Kekulé vortex and observe the emergence of the logical Majorana zero mode at the vortex core. By suitable adiabatic modulations of gate voltages as a function of time, the positions of individual Kekulé vortices can be moved, and in this manner, non-Abelian braiding of the logical Majoranas bound to the vortex cores can be performed. We provide the range of parameters where these excitations could be realized experimentally.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Single-Molecule Conductance through Hybrid Radially and Linearly π-Conjugated Macromolecules Reveals an Unusual Intramolecular π-Interaction

We describe the design, synthesis, and single-molecule junction conductance of π-electron molecules bearing both radial and linear π-conjugation pathways, whereby cycloparaphenylene (CPP) radial cores are π-extended linearly with aryl alkyne substituents as models for previously reported CPP-arylene ethynylene conjugated polymers. Although radially and linearly conjugated molecules have been studied previously in isolation as junction-bridging molecular electronic units, this is the first study to examine molecules where both topologies are operative. Our results reveal that the presence of radial CPP components within the junction-spanning pathway leads to a reduction in the conductance of the backbone compared to model linear phenyl substituents. Through tight-binding and DFT-based calculations, we attribute this conductance change to intramolecular van der Waals (vdW) interactions between the CPP ring and the junction-spanning arylene-ethynylene molecular backbone. These interactions induce changes in the dihedral angles of the backbone, leading to a reduced overlap of π orbitals within the molecular junction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Roadrunner Trap: A QSCOUT Device

The Roadrunner ion trap is a micro-fabricated surface-electrode ion trap based on silicon technology. This trap has one long linear section and a junction to allow for chain storage and reconfiguration. It uses a symmetric rf-rail design with segmented inner and outer control electrodes and independent control in the junction arms. The trap is fabricated on Sandia’s High Optical Access (HOA) platform to provide good optical access for tightly focused laser beams skimming the trap surface. It is packaged on our custom Bowtie-102 ceramic pin or land grid array packages using a 2.54 mm pitch for backside pins or pads. This trap also includes an rf sensing capacitive divider and tungsten wires for heating or temperature monitoring. The Roadrunner builds on the knowledge gained from previous surface traps fabricated at Sandia while improving ion control capabilities.

42 ENGINEERING↗
DOE OSTI2023

Superconducting Hyperdimensional Associative Memory Circuit for Scalable Machine Learning

Here we propose a generalized architecture for the first rapid-single-flux-quantum (RSFQ) associative memory circuit. The circuit employs hyperdimensional computing (HDC), a machine learning (ML) paradigm utilizing vectors with dimensionality in the thousands to represent information. HDC designs have small memory footprints, simple computations, and simple training algorithms compared to superconducting neural network accelerators (SNNAs), making them a better option for scalable SFQ machine learning (ML) solutions. The proposed superconducting HDC (SHDC) circuit uses entirely on-chip RSFQ memory which is tightly integrated with logic, operates at 33.3 GHz, is applicable to general ML tasks, and is manufacturable at practically useful scales given current SFQ fabrication limits. Tailored to a language recognition task, SHDC consists of ~ 2-20 M Josephson junctions (JJs) and consumes up to three times less power than an analogous CMOS HDC circuit while achieving 78-84% higher throughput. SHDC is capable of outperforming the state of the art RSFQ SNNA, SuperNPU, by 48-99% for all benchmark NN architectures tested while occupying up to 90% less area and consuming up to nine times less power. To the best of the authors' knowledge, SHDC is currently the only superconducting ML approach feasible at practically useful scales for real-world ML tasks and capable of online learning.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

First-Principles Quantum Transport Modeling of Spin-Transfer and Spin-Orbit Torques in Magnetic Multilayers

A unified approach for computing (i) spin-transfer torque in magnetic trilayers like spin valve and magnetic tunnel junction, where injected charge current flows perpendicularly to interfaces, and (ii) spin-orbit torque in magnetic bilayers of the type ferromagnet/spin-orbit-coupled material, where injected charge current flows parallel to the interface, was reviewed. The experimentally explored and technologically relevant spin-orbit-coupled materials include 5d heavy metals, topological insulators, Weyl semimetals, and transition metal dichalcogenides. This approach requires to construct the torque operator for a given Hamiltonian of the device and the steady-state nonequilibrium density matrix, where the latter is expressed in terms of the nonequilibrium Green’s functions and split into three contributions. Tracing these contributions with the torque operator automatically yields field-like and damping-like components of spin-transfer torque or spin-orbit torque vector, which is particularly advantageous for spin-orbit torque where the direction of these components depends on the unknown-in-advance orientation of the current-driven nonequilibrium spin density in the presence of spin-orbit coupling. Illustrative examples are provided by computing spin-transfer torque in a one-dimensional toy model of a magnetic tunnel junction and realistic Co/Cu/Co spin valve, both of which are described by first-principles Hamiltonians obtained from noncollinear density functional theory calculations, as well as by computing spin-orbit torque in a ferromagnetic layer described by a tight-binding Hamiltonian which includes spin-orbit proximity effect within ferromagnetic monolayers assumed to be generated by the adjacent monolayer transition metal dichalcogenide. In addition, it is shown here how spin-orbit proximity effect, quantified by computing (via first-principles retarded Green’s function) spectral functions and spin textures on monolayers of realistic ferromagnetic material like Co in contact with heavy metal or monolayer transition metal dichalcogenide, can be tailored to enhance the magnitude of spin-orbit torque. Furthermore, errors made in the calculation of spin-transfer torque are quantified when using Hamiltonian from collinear density functional theory, with rigidly rotated magnetic moments to create noncollinear magnetization configurations, instead of proper (but computationally more expensive) self-consistent Hamiltonian obtained from noncollinear density functional theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Modeling Surface-Enhanced Raman Scattering of Au-Pyrazine and Au-Pyrazine-Au Nanorod Dimer Systems with the TD-DFTB Method

This study investigates Raman scattering from a pyrazine molecule adsorbed on gold nanorod surfaces using the time-dependent density functional tight-binding (TD-DFTB) method. We analyze surface enhanced Raman scattering (SERS) of the molecule for two configurations: one where the molecule is adsorbed on a single gold nanorod forming Au-pyrazine complex, and another where it forms a nano-junction Au-pyrazine-Au dimer system. These two configurations offer distinct chemical environments for the molecule and different local field enhancements, with the dimer systems generating enhanced hotspot regions at the junctions. We present results for nanojunction, Au-pyrazine-Au dimer structures, for variable nanogap sizes that lead to shift in the plasmon energy. The study identifies contributions to the SERS enhancements from chemical, electromagnetic, and resonance charge transfer mechanisms. Our results show that the chemical mechanism provides enhancement factors in the range of 10 2 –10 3 , while the electromagnetic enhancements are on the order of 10 3 –10 5 for monomer systems and 10 6 –10 8 for dimer structures. We present results for a nanorod with a length of 5.3 nm and a width of 0.52 nm, consisting of 121 gold atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A computational approach to complex junctions and interfaces

Molecular- and nano- junctions, surfaces, interfaces, and interfacial processes have been the subjects of this DOE supported program for about 20 years. We have investigated electronic and magnetic structure, spin-dependent charge transport, and emerging phenomena at surfaces and interfaces in a number of physical systems ranging from nano-clusters, to nanowires, to two-dimensional (2D) crystals and their interactions, to bulk matter. Eighty-four refereed papers were published [1-84], with a majority effort from the group led by the PI, and over a hundred presentations were made at international conferences. We use first-principles methods in the framework of density functional theory in conjunction with nano-equilibrium Green function techniques, semi-empirical Boltzmann transport theory, tight-binding models, and the Hubbard model and many-body wavefunction approaches. In pursuing our scientific goals, we have also developed a number of computational algorithms and computational packages.

2D materials↗
DOE OSTI2025

Competing conduction mechanisms in high performance carbon nanotube fibers

The performance of carbon nanotube (CNT) cables, a contender for copper-wire replacement, is tied to its metallic and semi-conducting-like conductivity responses with temperature; the origin of the semi-conducting-like response however is an underappreciated incongruity in literature. With controlled aspect-ratio and doping-degree, over 61 unique cryogenic experiments including anisotropy and Hall measurements, CNT cable performance is explored at extreme temperatures (65 mK) and magnetic-fields (60 T). A semi-conducting-like conductivity response with temperature becomes temperature-independent approaching absolute-zero, uniquely demonstrating for the first time the necessity of heterogeneous fluctuation induced tunneling; complete de-doping leads to localized hopping, contrasting graphite's pure metallic-like response. High-field magneto-resistance (including novel +22 % longitudinal magneto-resistance near room-temperature) is analyzed with hopping and classical two-band models, both yielding a similar parameter useful for conductor development. Varying field-orientation angle uncovers significant two- and four-fold symmetries that are shown to be from Aharonov-Bohm-like corrections to the curvature-induced bandgap, a first for macroscale CNT fibers. Tight-binding calculations using Green's Function formalism model the largest, coherent transport to-date in commensurate CNT bundles in magnetic-field, revealing non-uniform transmission across bundle cross-sections with doping restoring uniformity; independent of doping, transport in bundle-junction-bundle systems are predominantly from CNTs adjacent to the other bundle—demonstrating that smaller bundles are more efficient for electronic transport. The final impact is predicting the ultimate conductivity of heterogeneous CNT cables using temperature and field-dependent transport, surpassing conductivity of traditional metals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probabilistic Neural Computing with Stochastic Devices

Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

Misra, Shashank↗
DOE OSTI2024

Embedding of Optical Fibers with Electric Field Assisted Sintering

The Electric Field Assisted Sintering (EFAS) technique was used for embedding the fibers, involving rapid heating via an electric current and pressure, reducing fiber exposure to high temperatures. Stainless steel guide tubes were inserted at the fiber-matrix junctions to prevent fiber breakage during the sintering process. The study varied key EFAS parameters—temperature (800°C to 980°C), pressure (40-50 MPa), and hold time (5-10 minutes)—to assess how they influenced fiber embedding. After embedding, the fibers were inspected using optical frequency domain reflectometry (OFDR) to evaluate optical losses and strain along the fiber length. The OFDR scans confirmed that the fibers remained intact, and subsequent transmission tests demonstrated no macroscopic fractures. The embedded samples were also analyzed using scanning electron microscopy (SEM) and X-ray computed tomography (CT) scans. These tests confirmed good bonding between the fiber and matrix with no cracking, though some porosity was present in samples fabricated at lower temperatures. At higher temperatures (980°C), the fiber-matrix bonding was continuous and defect-free, with metal coatings aiding in the bonding process. Elemental analysis showed interdiffusion between the fiber coatings and matrix materials, particularly between the gold coating and stainless steel. Helium leak tests revealed that lower sintering temperatures resulted in matrix porosity, causing leaks, but samples fabricated at 980°C were leak-tight.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2023

Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy

Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. Here, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗