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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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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Multidimensional resonance raman spectroscopy by six-wave mixing in the deep UV

Two-dimensional (2D) resonance Raman spectroscopies hold great potential for uncovering photoinduced relaxation processes in molecules but are not yet widely applied because of technical challenges. Here, we describe a newly developed 2D resonance Raman experiment operational at the third-harmonic of a Titanium-Sapphire laser. High-sensitivity and rapid data acquisition are achieved by combining spectral interferometry with a background-free (six-pulse) laser beam geometry. The third-harmonic laser pulses are generated in a filament produced by the fundamental and second-harmonic pulses in neon gas at pressures up to 35 atm. The capabilities of the setup are demonstrated by probing ground-state wavepacket motions in triiodide. The information provided by the experiment is explored with two different representations of the signal. In one representation, Fourier transforms are carried out with respect to the two experimentally controlled delay times to obtain a 2D Raman spectrum. Further insights are derived in a second representation by dispersing the signal pulse in a spectrometer. It is shown that, as in traditional pump-probe experiments, the six-wave mixing signal spectrum encodes the wavepacket's position by way of the (time-evolving) emission frequency. Anharmonicity additionally induces dynamics in the vibrational resonance frequency. In all cases, the experimental signals are compared to model calculations based on a cumulant expansion approach. This study suggests that multi-dimensional resonance Raman spectroscopies conducted on systems with Franck-Condon active modes are fairly immune to many of the technical issues that challenge off-resonant 2D Raman spectroscopies (e.g., third-order cascades) and photon-echo experiments in the deep UV (e.g., coherence spikes). The development of higher-order nonlinear spectroscopies operational in the deep UV is motivated by studies of biological systems and elementary organic photochemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Electrical, luminescent, and deep trap properties of Si doped n-GaN grown by pendeo epitaxy

Electrical and luminescent properties and deep trap spectra of Si doped GaN films grown by maskless epitaxial lateral overgrowth (MELO) are reported. The dislocation density in the wing region of the structure was 10{sup 6 }cm{sup −2}, while in the seed region it was 10{sup 8 }cm{sup −2}. The major electron traps present had activation energy of 0.56 eV and concentrations in the high 10{sup 15 }cm{sup −3} range. A comparison of diffusion length values and 0.56 eV trap concentration in MELO GaN and epitaxial lateral overgrowth (ELOG) GaN showed a good correlation, suggesting these traps could be effective in carrier recombination. The doped MELO films were more uniform in their electrical properties than either ELOG films or undoped MELO films. We also discuss the differences in deep trap spectra and luminescence spectra of low-dislocation-density MELO, ELOG, and bulk n-GaN samples grown by hydride vapor phase epitaxy. It is suggested that the observed differences could be caused by the differences in oxygen and carbon contamination levels.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Iterative diagonalization of the non-Hermitian transcorrelated Hamiltonian using a plane-wave basis set: Application to sp-electron systems with deep core states

We develop an iterative diagonalization scheme in solving a one-body self-consistent-field equation in the transcorrelated (TC) method using a plane-wave basis set. Non-Hermiticity in the TC method is well handled with a block-Davidson algorithm. We verify that the required computational cost is efficiently reduced by our algorithm. In addition, we apply our plane-wave-basis TC calculation to some simple sp-electron systems with deep core states to elucidate an impact of the pseudopotential approximation to the calculated band structures. We find that a position of the deep valence bands is improved by an explicit inclusion of core states, but an overall band structure is consistent with a regular setup that includes core states into the pseudopotentials. This study offers an important understanding for the future application of the TC method to strongly correlated solids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Subgap time of flight: A spectroscopic study of deep levels in semi-insulating CdTe:Cl

We report on a study of deep levels in semi-insulating CdTe:Cl by means of a time-of-flight spectral approach. By varying the wavelength of a pulsed optical source within the CdTe energy gap, transitions to/from localized levels generate free carriers which are analysed through the induced photocurrent transients. Both acceptor-like centers, related to the A-center, and a midgap level, 0.725 eV from the valence band, have been detected. The midgap level is close to the Fermi level and is possibly a recombination center responsible for the compensation mechanism. When the irradiance is varied, either linear or quadratic dependence of the electron and hole collected charge are observed, depending on the dominant optical transitions. The analysis discloses the potentiality of such a novel approach exploitable in the field of photorefractive materials as well as for deep levels spectroscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Collective dynamic dipole moment and orientation fluctuations, cooperative hydrogen bond relaxations, and their connections to dielectric relaxation in ionic acetamide deep eutectics: Microscopic insight from simulations

The paper reports a detailed simulation study on collective reorientational relaxation, cooperative hydrogen bond (H-bond) fluctuations, and their connections to dielectric relaxation (DR) in deep eutectic solvents made of acetamide and three uni-univalent electrolytes, lithium nitrate (LiNO{sub 3}), lithium bromide (LiBr), and lithium perchlorate (LiClO{sub 4}). Because cooperative H-bond fluctuations and ion migration complicate the straightforward interpretation of measured DR timescales in terms of molecular dipolar rotations for these conducting media which support extensive intra- and inter-species H-bonding, one needs to separate out the individual components from the overall relaxation for examining the microscopic origin of various timescales. The present study does so and finds that reorientation of ion-complexed acetamide molecules generates relaxation timescales that are in sub-nanosecond to nanosecond range. This explains in molecular terms the nanosecond timescales reported by recent giga-Hertz DR measurements. Interestingly, the simulated survival timescale for the acetamide-Li{sup +} complex has been found to be a few tens of nanosecond, suggesting such a cation-complexed species may be responsible for a similar timescale reported by mega-Hertz DR measurements of acetamide/potassium thiocyanate deep eutectics near room temperature. The issue of collective versus single particle relaxation is discussed, and jump waiting time distributions are determined. Dependence on anion-identity in each of the cases has been examined. In short, the present study demonstrates that assumption of nano-sized domain formation is not required for explaining the DR detected nanosecond and longer timescales in these media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Deep ultraviolet emission from ultra-thin GaN/AlN heterostructures

We present the theoretical and experimental results for the electronic and optical properties of atomically thin (1 and 2 monolayers) GaN quantum wells with AlN barriers. Strong quantum confinement increases the gap of GaN to as high as 5.44 eV and enables light emission in the deep-UV range. Luminescence occurs from the heavy and light hole bands of GaN yielding E ⊥ c polarized light emission. Strong confinement also increases the exciton binding energy up to 230 meV, preventing a thermal dissociation of excitons at room temperature. However, we did not observe excitons experimentally due to high excited free-carrier concentrations. Monolayer-thick GaN wells also exhibit a large electron-hole wave function overlap and negligible Stark shift, which is expected to enhance the radiative recombination efficiency. Our results indicate that atomically thin GaN/AlN heterostructures are promising for efficient deep-UV optoelectronic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

The effect of shallow vs. deep level doping on the performance of thermoelectric materials

It is well known that the efficiency of a good thermoelectric material should be optimized with respect to doping concentration. However, much less attention has been paid to the optimization of the dopant’s energy level. Thermoelectric materials doped with shallow levels may experience a dramatic reduction in their figures of merit at high temperatures due to the excitation of minority carriers that reduces the Seebeck coefficient and increases bipolar heat conduction. Doping with deep level impurities can delay the excitation of minority carriers as it requires a higher temperature to ionize all dopants. We find through modeling that, depending on the material type and temperature range of operation, different impurity levels (shallow or deep) will be desired to optimize the efficiency of a thermoelectric material. For different materials, we further clarify where the most preferable position of the impurity level within the bandgap falls. In this work, our research provides insight on why different dopants often affect thermoelectric transport properties differently and directions in searching for the most appropriate dopants for a thermoelectric material in order to maximize the device efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Proposed measurement of tagged deep inelastic scattering in Hall A of Jefferson lab

A tagged deep inelastic scattering (TDIS) experiment is planned for Hall A of Jefferson Lab, which will probe the mesonic content of the nucleon directly. Low momentum recoiling (and spectator) protons will be measured in coincidence with electrons scattered in a deep inelastic regime from hydrogen (and deuterium) targets, covering kinematics of 8 < W2 < 18 GeV2, 1 < Q2 < 3 (GeV/c)2 and 0:05 < x < 0:2. The tagging technique will help identify scattering from partons in the meson cloud and provide access to the pion structure function via the Sullivan process. The experiment will yield the first TDIS results in the valence regime, for both proton and neutron targets. We present here an overview of the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Deep donor state of the copper acceptor as a source of green luminescence in ZnO

Copper impurities have long been linked with green luminescence (GL) in ZnO. Copper is known to introduce an acceptor level close to the conduction band of ZnO, and the GL has conventionally been attributed to transitions involving an excited state which localizes holes on neighboring oxygen atoms. To date, a theoretical description of the optical properties of such deep centers has been difficult to achieve due to the limitations of functionals in the density functional theory. Here, we employ a screened hybrid density functional to calculate the properties of Cu in ZnO. In agreement with the experiment, we find that CuZn features an acceptor level near the conduction band of ZnO. However, we find that CuZn also gives rise to a deep donor level 0.46 eV above the valence band of ZnO; the calculated optical transitions involving this state agree well with the GL observed in ZnO:Cu.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Adaptive coupling of a deep neural network potential to a classical force field

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy, and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Impact of proton irradiation on conductivity and deep level defects in β-Ga 2 O 3

Single crystalline bulk and epitaxially grown gallium oxide (β–Ga 2 O 3 ) was irradiated by 0.6 and 1.9 MeV protons to doses ranging from 5 × 10 9 to 6 × 10 14 cm -2 in order to study the impact on charge carrier concentration and electrically active defects. Samples irradiated to doses at or above 2 × 10 13 cm -2 showed a complete removal of free charge carriers in their as-irradiated state, whereas little or no influence was observed below doses of 6 × 10 12 cm -2 . From measurements at elevated temperatures, a thermally activated recovery process is seen for the charge carriers, where the activation energy for recovery follow a second-order kinetics with an activation energy of ~1.2 eV. Combining the experimental results with hybrid functional calculations, we propose that the charge carrier removal can be explained by Fermi-level pinning far from the conduction band minimum (CBM) due to gallium interstitials (Ga i ), vacancies (V Ga ), and antisites (Ga O ), while migration and subsequent passivation of V Ga via hydrogen-derived or V O defects may be responsible for the recovery. Following the recovery, deep level transient spectroscopy (DLTS) reveals generation of two deep levels, with energy positions around 0.75 and 1.4 eV below the CBM. Of these two levels, the latter is observed to disappear after the initial DLTS measurements, while the concentration of the former increases. Here, we discuss candidate possibilities and suggest that the origins of these levels are more likely due to a defect complex than an isolated point defect.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Investigation of dry-etch-induced defects in >600 V regrown, vertical, GaN, p-n diodes using deep-level optical spectroscopy

The effect of dry-etch-induced defects on the electrical performance of regrown, c-plane, GaN p-n diodes where the p-GaN layer is formed by epitaxial regrowth using metal-organic, chemical-vapor deposition was investigated. Diode leakage increased greatly for etched-and-regrown diodes compared to continuously grown diodes, suggesting a defect-mediated leakage mechanism. Deep level optical spectroscopy (DLOS) methods were used to identify energy levels and densities of defect states to understand etch-induced damage in regrown devices. DLOS results showed the creation of an emergent, mid-gap defect state at 1.90 eV below the conduction band edge for etched-and-regrown diodes. Reduction in both the reverse leakage and the concentration of the 1.90 eV mid-gap state was achieved using a wet chemical treatment on the etched surface before regrowth, suggesting that the 1.90 eV deep level contributes to increased leakage and premature breakdown but can be mitigated with proper post-etch treatments to achieve >600 V reverse breakdown operation.

42 ENGINEERING↗
DOE OSTI2020

Defect suppression in wet-treated etched-and-regrown nonpolar m-plane GaN vertical Schottky diodes: A deep-level optical spectroscopy analysis

In this work, steady-state photocapacitance (SSPC) was conducted on nonpolar m-plane GaN n-type Schottky diodes to evaluate the defects induced by inductively coupled plasma (ICP) dry etching in etched-and-regrown unipolar structures. An ~10× increase in the near-midgap Ec – 1.9 eV level compared to an as-grown material was observed. Defect levels associated with regrowth without an etch were also investigated. The defects in the regrown structure (without an etch) are highly spatially localized to the regrowth interface. Subsequently, by depth profiling an etched-and-regrown sample, we show that the intensities of the defect-related SSPC features associated with dry etching depend strongly on the depth away from the regrowth interface, which is also reported previously. A photoelectrochemical etching (PEC) method and a wet AZ400K treatment are also introduced to reduce the etch-induced deep levels. A significant reduction in the density of deep levels is observed in the sample that was treated with PEC etching after dry etching and prior to regrowth. An ~2× reduction in the density of Ec – 1.9 eV level compared to a reference etched-and-regrown structure was observed upon the application of PEC etching treatment prior to the regrowth. The PEC etching method is promising for reducing defects in selective-area doping for vertical power switching structures with complex geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A deep potential model with long-range electrostatic interactions

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make molecular simulations with the accuracy of quantum mechanical density functional theory possible at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation, we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei + core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the DP long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples: the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Enhanced analysis of experimental x-ray spectra through deep learning

X-ray spectroscopic data from high-energy-density laser-produced plasmas has long required thorough, time-consuming analysis to extract meaningful source conditions. There are often confounding factors due to rapidly evolving states and finite spatial gradients (e.g., the existence of multi-temperature, multi-density, multi-ionization states, etc.) that make spectral measurements and analysis difficult. Here, in this paper, we demonstrate how deep learning can be applied to enhance x-ray spectral data analysis in both speed and intricacy. Neural networks (NNs) are trained on ensemble atomic physics simulations so that they can subsequently construct a model capable of extracting plasma parameters directly from experimental spectra. Through deep learning, the models can extract temperature distributions as opposed to single or dual temperature/density fits from standard trial-and-error atomic modeling at a significantly reduced computational cost compared to traditional trial-and-error methods. These NNs are envisioned to be deployed with high repetition rate x-ray spectrometers in order to provide detailed real-time analysis of experimental spectra.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Demonstration of object location, classification, and characterization by developed deep learning dust ablation trail analysis code package using plasma jets

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗