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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Understanding the evolution of liquid and solid microjets from grooved Sn and Cu samples using radiography

Experiments were performed on grooved Sn and Cu samples to study the temporal evolution of microjets. Jets were generated by the impact of gun-launched flyer plates against the back of grooved targets made from either Cu or Sn (groove depth of ~250 μm). The Hugoniot states in the various Sn targets encompassed conditions where solid phases are maintained throughout (7 and 16 GPa) and also conditions where melting occurs upon the release of compression (25 and 34 GPa); the transition occurs near a Hugoniot pressure of 23 GPa. Cu targets at 27 and 56 GPa provide comparisons in which the jets move at similar speeds but remain solid. In all cases, the spatial distribution of mass within the microjets was measured using high-speed synchrotron radiography. The result is a time history of the jet thickness profile from which quantities like total jet mass and jet velocity can be derived. In both the solid and liquid states, we generally observe that an increase in the shock strength leads to an increase in jet mass. However, this trend breaks down for Hugoniot states near the transition from continuously solid to melted-on-release. This is evidenced by the observation that there was no difference in the rate of mass flow in Sn jets at 16 and 25 GPa, while similar pressure jumps on either side of this range caused substantial changes in the jet mass. This contrasts with the behavior of smaller polishing defects that were present on the same samples (~1 μm deep). From these, no ejecta mass was detected below the melt boundary, but obvious microjets were generated once melting occurred. This indicates that crossing the bulk melt-on-release threshold can alternately promote or inhibit the flow of mass into microjets based on the amplitude of the initial perturbation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Chemical evolution in nitrogen shocked beyond the molecular stability limit

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. Finally, this suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Modeling Intermolecular Interactions in Nanotubes, Fullerenes and Graphite using a New Long-Range Potential

The cohesive energy and compressibility of strands of a single-wall nanotube rope has been computed using a new long-range potential energy function derived from accurate ab initio quantum chemistry calculations of the benzene dimer and calibrated for energetic and mechanical properties of graphite (at pressures up to 12 GPa). We also use this potential to calculate a variety of properties of carbon nanotubes (both single- and multi-wall) and fullerenes. Extensive comparisons are made with previously published potentials.

Jaffe, Richard↗
DOE OSTI2025

High Energy Density Physics of Inertial Confinement Fusion Ablator Materials (Final Technical Report)

The goal of this project was to conduct dynamic compression experiments and predictive simulations to reveal the fundamental high-energy-density (HED) physics of amorphous carbon. These results are essential for assessing amorphous carbon as a potential ablator material for next generation of inertial confinement fusion (ICF) capsules. We made significant progress in exploring the HED properties of amorphous carbon through experiments at Omega EP Laser and the European XFEL, in addition to billion-atom, quantum-accurate molecular dynamics (MD) simulations. Through our joint experimental and simulation program, we mapped the phase diagram of amorphous carbon, uncovering its range of metastability and identifying phase transitions to diamond and liquid carbon along the Hugoniot and at higher pressures using double shock compression pathways. Our findings indicate that amorphous carbon melts at significantly lower shock pressures than high-density carbon (diamond). However, nanocrystalline diamond nucleates across a broad range of pressures and temperatures. This emergence of the nanocrystalline microstructure during compression can negatively impact the planarity of the shock front and potentially trigger ablator/fuel mixing during Inertial Fusion Energy (IFE) applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Direct growth of crystalline SiGe nanowires on superconducting NbTiN thin films

Novel heterostructures created by coupling one-dimensional semiconductor nanowires with a superconducting thin film show great potential toward next-generation quantum computing. Here, by growing high-crystalline SiGe nanowires on a NbTiN thin film, the resulting heterostructure exhibits Ohmic characteristics as well as a shift of the superconducting transition temperature (T c ). The structure was characterized at atomic resolution showing a sharp SiGe/NbTiN interface without atomic interdiffusion. Lattice spacing, as calculated from large-area x-ray diffraction experiments, suggests a potential preferred d-spacing matching between (200) NbTiN and (110) SiGe grains. The observed out-of-plane compressive strain within the NbTiN films coupled with SiGe nanowires explains the downward shift of the superconductivity behavior. The presented results post scientific insights toward functional heterostructures by coupling multi-dimensional materials, which could enable tunable superconductivity that benefits the quantum science applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Near-optimal combination of high performance and insensitivity in a shock compressed high explosive single crystal

Achieving the desired combination of superior detonation performance and insensitivity to shock initiation has been a long-standing goal in high explosive (HE) science and technology. Having previously established the shock insensitivity of 1,1-diamino-2,2-dinitroethene (also known as DADNE or FOX-7) single crystals to 20 GPa (extended to 25 GPa in this work), the FOX-7 detonation response was determined through wave profile measurements in ~250 μm thick single crystals shock compressed to 64 GPa. Quite unexpectedly, FOX-7 demonstrated the classic Chapman–Jouguet (C–J) detonation response—reaction completion in the detonation front (<0.7 ns) at pressures of 44 GPa and higher—not observed in other insensitive high explosives. Here, the experimentally determined C–J pressure (35 GPa), detonation wave velocities and the detonation products equation of state—together with shock insensitivity to 25 GPa—demonstrate that FOX-7 single crystals display a near-optimal combination of high performance and shock insensitivity, not observed in another HE crystal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A conveyor-belt magneto-optical trap of CaF

Achieving high-density samples of laser-cooled molecules is a critical step toward advancing applications in precision measurements, ultracold chemistry and quantum science. We report the experimental realization of a high-density conveyor-belt magneto-optical trap for calcium monofluoride (CaF) molecules. The obtained highly-compressed cloud has a mean radius of 64(5) μm and a peak number density of 3.6(5) × 10 10 cm −3 , a 600-fold increase over the conventional red-detuned MOTs of CaF, and the densest molecular MOT observed to date. Subsequent loading of these molecules into an optical dipole trap yields up to 2.6 × 10 4 trapped molecules at a temperature of 14(2) μK with a peak phase-space density of ~ 2.4 × 10 −6 . This opens new possibilities for a range of applications utilizing high-density, optically trapped ultracold molecules.

Yu, Scarlett S. [Harvard Univ., Cambridge, MA (Uni↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Shock compression experiments using the DiPOLE 100-X laser on the high energy density instrument at the European x-ray free electron laser: Quantitative structural analysis of liquid Sn

X-ray free electron laser (XFEL) sources coupled to high-power laser systems offer an avenue to study the structural dynamics of materials at extreme pressures and temperatures. The recent commissioning of the DiPOLE 100-X laser on the high energy density (HED) instrument at the European XFEL represents the state-of-the-art in combining x-ray diffraction with laser compression, allowing for compressed materials to be probed in unprecedented detail. Here, we report quantitative structural measurements of molten Sn compressed to 85(5) GPa and ~3500 K. The capabilities of the HED instrument enable liquid density measurements with an uncertainty of ~1% at conditions which are extremely challenging to reach via static compression methods. We discuss best practices for conducting liquid diffraction dynamic compression experiments and the necessary intensity corrections which allow for accurate quantitative analysis. We also provide a polyimide ablation pressure vs input laser energy for the DiPOLE 100-X drive laser which will serve future users of the HED instrument.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Shubnikov–de Haas oscillations of biaxial-strain-tuned superconductors in pulsed magnetic field up to 60 T

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful method to probe the electronic structure and, for metallic 2D materials, quantum oscillations superimposed on the transport signals encode Fermi surface parameters. In this manuscript, we utilize biaxial strain as an external tuning parameter and investigate the effects of strain on the electronic properties of two quasi-2D superconductors, MoTe 2 and RbV 3 Sb 5 , by measuring their magnetoresistance in pulsed magnetic fields up to 60 T. With a careful selection of insulating substrates, we demonstrate the possibility of both the compressive and tensile biaxial strains imposed on MoTe 2 and RbV 3 Sb 5 , respectively. For both systems, the applied strain has led to superconducting critical temperature enhancement compared to their free-standing counterparts, proving the effectiveness of this biaxial strain method at cryogenic temperatures. Clear quantum oscillations in the magnetoresistance—the Shubnikov–de Haas (SdH) effect—are obtained in both samples. In strained MoTe 2 , the magnetoresistance exhibits a nearly quadratic dependence on the magnetic field and remains non-saturating even at the highest field, whereas in strained RbV 3 Sb 5 , two SdH frequencies showed a substantial enhancement in effective mass values, hinting at a possible enhancement of charge fluctuations. Our results demonstrate that combining biaxial strain and pulsed magnetic field paves the way for studying 2D materials under unprecedented conditions.

2D materials↗
DOE OSTI2024

Shubnikov-de Haas oscillations of biaxial-strain-tuned superconductors in pulsed magnetic field up to 60 T

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful method to probe the electronic structure and, for metallic 2D materials, quantum oscillations superimposed on the transport signals encode Fermi surface parameters. In this manuscript, we utilize biaxial strain as an external tuning parameter and investigate the effects of strain on the electronic properties of two quasi-2D superconductors, MoTe 2 and RbV 3 Sb 5 , by measuring their magnetoresistance in pulsed magnetic fields up to 60 T. With a careful selection of insulating substrates, we demonstrate the possibility of both the compressive and tensile biaxial strains imposed on MoTe 2 and RbV 3 Sb 5 , respectively. For both systems, the applied strain has led to superconducting critical temperature enhancement compared to their free-standing counterparts, proving the effectiveness of this biaxial strain method at cryogenic temperatures. Clear quantum oscillations in the magnetoresistance—the Shubnikov–de Haas (SdH) effect—are obtained in both samples. In strained MoTe 2 , the magnetoresistance exhibits a nearly quadratic dependence on the magnetic field and remains non-saturating even at the highest field, whereas in strained RbV 3 Sb 5 , two SdH frequencies showed a substantial enhancement in effective mass values, hinting at a possible enhancement of charge fluctuations. Our results demonstrate that combining biaxial strain and pulsed magnetic field paves the way for studying 2D materials under unprecedented conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Electron subband degeneracy heat pump for cryogenic cooling

An unconventional method of solid-state cryogenic cooling utilizing the electron subband degeneracy of semiconductor heterostructures is discussed in this Letter. The working principle of using the electrons as the working medium to perform cyclic heat pumping under electrostatic subband "expansion" and "compression" is discussed. The calculated base temperature can reach below dilution refrigeration temperatures with the fundamental limit set by electron-phonon interaction. Using an ultra-wide GaAs quantum well as an example, the cooling power per unit volume is estimated to reach $0.85\ \rm \mu W/cm^3$ at $25\ \rm mK$ with a hot-side temperature of $42.5\ \rm mK$, suitable for applications such as quantum computing.

Wang, Chulin↗
DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Carbon Nanotube Based Molecular Electronics and Motors: A View from Classical and Quantum Dynamics Simulations

The tubular forms of fullerenes popularly known as carbon nanotubes are experimentally produced as single-, multiwall, and rope configurations. The nanotubes and nanoropes have shown to exhibit unusual mechanical and electronic properties. The single wall nanotubes exhibit both semiconducting and metallic behavior. In short undefected lengths they are the known strongest fibers which are unbreakable even when bent in half. Grown in ropes their tensile strength is approximately 100 times greater than steel at only one sixth the weight. Employing large scale classical and quantum molecular dynamics simulations we will explore the use of carbon nanotubes and carbon nanotube junctions in 2-, 3-, and 4-point molecular electronic device components, dynamic strength characterization for compressive, bending and torsional strains, and chemical functionalization for possible use in a nanoscale molecular motor. The above is an unclassified material produced for non-competitive basic research in the nanotechnology area.

Srivastava, Deepak↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Micro pulse lidar

An eye safe, compact, solid state lidar for profiling atmospheric cloud and aerosol scattering has been demonstrated. The transmitter of the micropulse lidar is a diode pumped micro-J pulse energy, high repetition rate Nd:YLF laser. Eye safety is obtained through beam expansion. The receiver employs a photon counting solid state Geiger mode avalanche photodiode detector. Data acquisition is by a single card multichannel scaler. Daytime background induced quantum noise is controlled by a narrow receiver field-of-view and a narrow bandwidth temperature controlled interference filter. Dynamic range of the signal is limited by optical geometric signal compression. Signal simulations and initial atmospheric measurements indicate that systems built on the micropulse lidar concept are capable of detecting and profiling all significant cloud and aerosol scattering through the troposphere and into the stratosphere. The intended applications are scientific studies and environmental monitoring which require full time, unattended measurements of the cloud and aerosol height structure.

Spinhirne, James D.↗
NASA NTRS1993

Micro pulse laser radar

An eye safe, compact, solid state lidar for profiling atmospheric cloud and aerosol scattering is disclosed. The transmitter of the micro pulse lidar is a diode pumped micro-J pulse energy, high repetition rate Nd:YLF laser. Eye safety is obtained through beam expansion. The receiver employs a photon counting solid state Geiger mode avalanche photodiode detector. Data acquisition is by a single card multichannel scaler. Daytime background induced quantum noise is controlled by a narrow receiver field-of-view and a narrow bandwidth temperature controlled interference filter. Dynamic range of the signal is limited to optical geometric signal compression. Signal simulations and initial atmospheric measurements indicate that micropulse lider systems are capable of detecting and profiling all significant cloud and aerosol scattering through the troposphere and into the stratosphere. The intended applications are scientific studies and environmental monitoring which require full time, unattended measurements of the cloud and aerosol height structure.

Spinhirne, James D.↗
NASA NTRS2008

NASA Tech Briefs, March 2008

Topics covered include: WRATS Integrated Data Acquisition System; Breadboard Signal Processor for Arraying DSN Antennas; Digital Receiver Phase Meter; Split-Block Waveguide Polarization Twist for 220 to 325 GHz; Nano-Multiplication-Region Avalanche Photodiodes and Arrays; Tailored Asymmetry for Enhanced Coupling to WGM Resonators; Disabling CNT Electronic Devices by Use of Electron Beams; Conical Bearingless Motor/Generators; Integrated Force Method for Indeterminate Structures; Carbon-Nanotube-Based Electrodes for Biomedical Applications; Compact Directional Microwave Antenna for Localized Heating; Using Hyperspectral Imagery to Identify Turfgrass Stresses; Shaping Diffraction-Grating Grooves to Optimize Efficiency; Low-Light-Shift Cesium Fountain without Mechanical Shutters; Magnetic Compensation for Second-Order Doppler Shift in LITS; Nanostructures Exploit Hybrid-Polariton Resonances; Microfluidics, Chromatography, and Atomic-Force Microscopy; Model of Image Artifacts from Dust Particles; Pattern-Recognition System for Approaching a Known Target; Orchestrator Telemetry Processing Pipeline; Scheme for Quantum Computing Immune to Decoherence; Spin-Stabilized Microsatellites with Solar Concentrators; Phase Calibration of Antenna Arrays Aimed at Spacecraft; Ring Bus Architecture for a Solid-State Recorder; and Image Compression Algorithm Altered to Improve Stereo Ranging.

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