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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

III-nitride m-plane violet narrow ridge edge-emitting laser diodes with sidewall passivation using atomic layer deposition

A novel deep-ridge laser structure with atomic-layer deposition (ALD) sidewall passivation was proposed that enhances the optical characteristics of 8-µm ridge width III-nitride violet lasers on freestanding m -plane GaN substrates. The internal loss was determined using the variable stripe length method, where the laser structure with ALD sidewall passivation showed lower internal loss compared to the conventional shallow-ridge laser design. ALD sidewall passivation plays a critical role in device improvements; compared to the lasers without ALD sidewall passivation, the lasers with ALD sidewall passivation yield improved optoelectrical performance and longer lifetime under continuous-wave operation at high current density. This work demonstrates the importance of ALD sidewall passivation to laser performance, which enables high energy efficiency.

Wong, Matthew S.↗
DOE OSTI2024

Photoluminescence and Raman spectroscopy of wide bandgap semiconductors damaged by deep-UV laser irradiation

The effects of a pulsed, focused, deep-UV (4.66 eV) laser on wide and ultra-wide bandgap semiconductors were investigated with photoluminescence (PL) and Raman spectroscopy. Three semiconductor single crystals were studied: silicon carbide (6H-SiC), gallium nitride (GaN), and gallium oxide (β-Ga 2 O 3 ). Atomic emission lines from neutral Ga or Si were observed during the laser-damage process. For all three semiconductors, PL mapping (3.49 eV laser excitation) of the damaged material revealed visible emission bands in the 2.6–2.8 eV range, attributed to point defects. Raman spectra (2.33 eV excitation) showed a reduction in the Raman peak intensities in the damaged region, along with weak PL bands around 1.9–2.1 eV.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spectral signatures of transverse optical modes in semiconductor nanowires

In order to build nanophotonic devices, it is important to understand and ultimately control the optical mode structure within potential components such as nanoscale waveguides. However, experimental characterization of such modes in the optical regime is difficult due to the nanoscale dimensions of such components and the perturbations that would be induced by a near-field probe. Here, we demonstrate a probe-free, far-field method to characterize the optical modes within GaN nanowires (NWs) based on a novel off-axis scanning confocal microscope system. Using this microscope, we observe spectral signatures resulting from lateral leakage of waveguide modes when they exceed their respective cutoff limits. We identify these modes within hyperspectral images using an analytical model coupled with finite element simulations. The model can also be used to predict the spectral signatures for given geometrical and dielectric parameters, which enabled us to deduce the transverse dimension of the NW from hyperspectral images with an accuracy of ∼ <#comment/> 30 nm .

Wang, Maoji↗
DOE OSTI2024

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Here, we present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 1a Raw Data

10 hours chronoamperometry (CA) testing under 1 sun illumination and constant bias at -0.6 V vs RHE, the corresponding Faradaic efficiency reveals a self-improving nature of GaN, inset: CA testing on bare Si for 5 hours under 1 sun illumination and -0.6 V vs RHE, this bare Si photocathode rapidly drops down to < 0.05 mA/cm2 within an hour. Both CA testing performed in 0.5 M H2SO4 (pH=0.4).

photocathode↗
DOE OSTI2021

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 3c Raw Data

XPS after 0hr of chronoampometry. Surface chemical composition and valence band structure of GaN were obtained by X-ray photoemission spectroscopy (XPS) on a Kratos Axis Ultra DLD system at a takeoff angle of 0° relative to the surface normal. An Al Kα source (hν = 1486.6 eV) was used to excite the core level electrons. Pass energy of 20 eV was used for the narrow scan of core levels and valence band spectra, and step size of 0.05 eV and 0.025 eV, respectively. The Spectral fitting was conducted using CasaXPS analysis software. The binding energy scales of all core levels were corrected to the N 1s of Ga – N bond at 397.8 eV. XPS O1s core level spectra from as-received Si/GaN sample, and deconvolution shows O - Ga bond and OH - H2O bond.

photocathode↗
DOE OSTI2021

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 3d Raw Data

XPS of Si/GaN photocathode after 1 hour chronoamperometry (CA) testing. Surface chemical composition and valence band structure of GaN were obtained by X-ray photoemission spectroscopy (XPS) on a Kratos Axis Ultra DLD system at a takeoff angle of 0° relative to the surface normal. An Al Kα source (hν = 1486.6 eV) was used to excite the core level electrons. Pass energy of 20 eV was used for the narrow scan of core levels and valence band spectra, and step size of 0.05 eV and 0.025 eV, respectively. The Spectral fitting was conducted using CasaXPS analysis software. The binding energy scales of all core levels were corrected to the N 1s of Ga – N bond at 397.8 eV. X-ray photoemission spectroscopy (XPS) O1s core level spectra from 1 hour chronoamperometry (CA) tested Si/GaN sample, and deconvolution shows O - Ga bond, O - N - Ga bond, and OH - H2O bond.

photocathode↗
DOE OSTI2021

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 3f Raw Data

XPS of Si/GaN photocathode after 10 hour chronoamperometry (CA) testing. Surface chemical composition and valence band structure of GaN were obtained by X-ray photoemission spectroscopy (XPS) on a Kratos Axis Ultra DLD system at a takeoff angle of 0° relative to the surface normal. An Al Kα source (hν = 1486.6 eV) was used to excite the core level electrons. Pass energy of 20 eV was used for the narrow scan of core levels and valence band spectra, and step size of 0.05 eV and 0.025 eV, respectively. The Spectral fitting was conducted using CasaXPS analysis software. The binding energy scales of all core levels were corrected to the N 1s of Ga – N bond at 397.8 eV. XPS O1s core level spectra from 10 hours CA tested Si/GaN sample, and deconvolution shows O - Ga bond, O - N - Ga bond and OH - H2O bond.

photocathode↗
DOE OSTI2021

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 3e Raw Data

XPS of Si/GaN photocathode after 4 hour chronoamperometry (CA) testing. Surface chemical composition and valence band structure of GaN were obtained by X-ray photoemission spectroscopy (XPS) on a Kratos Axis Ultra DLD system at a takeoff angle of 0° relative to the surface normal. An Al Kα source (hν = 1486.6 eV) was used to excite the core level electrons. Pass energy of 20 eV was used for the narrow scan of core levels and valence band spectra, and step size of 0.05 eV and 0.025 eV, respectively. The Spectral fitting was conducted using CasaXPS analysis software. The binding energy scales of all core levels were corrected to the N 1s of Ga – N bond at 397.8 eV. XPS O1s core level spectra from 4 hour CA tested Si/GaN sample, and deconvolution shows O - Ga bond, O - N - Ga bond and OH - H2O bond.

photocathode↗
DOE OSTI2020

Materials Data on GaSiCN by Materials Project

GaN(SiC) is Stannite-like structured and crystallizes in the orthorhombic Pmn2_1 space group. The structure is three-dimensional. Ga3+ is bonded to three equivalent C4- and one N3- atom to form GaC3N tetrahedra that share corners with six equivalent GaC3N tetrahedra and corners with six equivalent SiCN3 tetrahedra. There is two shorter (1.94 Å) and one longer (1.95 Å) Ga–C bond length. The Ga–N bond length is 2.26 Å. Si4+ is bonded to one C4- and three equivalent N3- atoms to form SiCN3 tetrahedra that share corners with six equivalent GaC3N tetrahedra and corners with six equivalent SiCN3 tetrahedra. The Si–C bond length is 1.77 Å. There is one shorter (1.87 Å) and two longer (1.90 Å) Si–N bond length. C4- is bonded to three equivalent Ga3+ and one Si4+ atom to form CGa3Si tetrahedra that share corners with six equivalent CGa3Si tetrahedra and corners with six equivalent NGaSi3 tetrahedra. N3- is bonded to one Ga3+ and three equivalent Si4+ atoms to form NGaSi3 tetrahedra that share corners with six equivalent CGa3Si tetrahedra and corners with six equivalent NGaSi3 tetrahedra.

36 MATERIALS SCIENCE↗
DOE OSTI2022

NEWTS USGS Produced Waters Database

Data from the USGS Produced Waters Database on produced water compositions from across the US. Data has been filtered for completeness of data attributes. Data has been transposed and converted for ease of input into aqueous chemistry modeling software including OLI Systems and Geochemist's Workbench. Original data from: Blondes, M. S., Gans, K. D., Engle, M. A., Kharaka, Y. K., Reidy, M. E., Saraswathula, V., Thordsen, J. J., Rowan, E. L., & Morrissey, E. A. (2019). U.S. Geological Survey National Produced Waters Geochemical Database v2.3 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7J964W8

Geochemists Workbench↗
DOE OSTI2022

NEWTS USGS Produced Waters Case Studies

Case studies from the USGS Produced Waters Database. Includes OLI Studio and Geochemist's Workbench files. Original data from: Blondes, M. S., Gans, K. D., Engle, M. A., Kharaka, Y. K., Reidy, M. E., Saraswathula, V., Thordsen, J. J., Rowan, E. L., & Morrissey, E. A. (2019). U.S. Geological Survey National Produced Waters Geochemical Database v2.3 [Data set]. U.S. Geological Survey. https://doi.org/10.5066/F7J964W8

Case Studies↗
DOE OSTI2020

Quantum Theory of Semiconductor Photo-Catalysis and Solar Water Splitting / Theory and Computation for Semiconductor Catalysis of Solar Water Splitting / Static and Dynamic Thermal Effects in Solids. Final Report

This is the final report. The finished research was published in 27 journal papers listed below. The topics fall roughly into seven categories: Photochemistry of water at the GaN ($10\bar{1}0$) surface; Quasiparticles: Band electrons and Anharmonic phonons; Quasiharmonic theory; Anharmonic lattice thermal conductivity; Water and Ice; Electron-phonon effects; and, Alloy theory.

36 MATERIALS SCIENCE↗
DOE OSTI2020

AI Denoising to Accelerate Detector Simulation

Detector simulation is critical to experimental HEP; however this simulation (commonly done through toolkits such as Geant4) is computationally intensive. Performance can be improved somewhat through technical optimization, but more is needed. Using machine learning (ML) to accelerate simulation is a promising field, however efforts to use generative adversarial networks (GANs) or optimized autoencoders have faced issues. Using convolutional neural networks (CNNs) for denoising has been successful in non-HEP applications such as image processing. This poster investigates the efficacy of using CNNs to denoise Geant4 simulations. This could increase the accuracy of simulations performed under settings designed to increase computational efficiency.

Franklin, Lena↗
DOE OSTI2020

Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics

Machine learning (ML), including deep learning (DL), has become increasingly popular in the last few years due to its continually outstanding performance. In this context, we apply machine learning techniques to "learn" the microstructure using both supervised and unsupervised DL techniques. In particular, we focus (1) on the localization problem bridging (micro)structure (localized) property using supervised DL and (2) on the microstructure reconstruction problem in latent space using unsupervised DL. The goal of supervised and semi-supervised DL is to replace crystal plasticity finite element model (CPFEM) that maps from (micro)structure (localized) property, and implicitly the (micro)structure (homogenized) property relationships, while the goal of unsupervised DL is (1) to represent high-dimensional microstructure images in a non-linear low-dimensional manifold, and (2) to discover a way to interpolate microstructures via latent space associating with latent microstructure variables. At the heart of this report is the applications of several common DL architectures, including convolutional neural networks (CNN), autoencoder (AE), and generative adversarial network (GAN), to multiple microstructure datasets, and the quest of neural architecture search for optimal DL architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning [Slides]

We present a novel machine learning CNN architecture that can learn from limited data combined appropriately with physics and statistical priors (e.g., forward models and noise models). To address the limited availability of training data we adopt a 3D patch-based approach for our models. Patch-based learning is central to several image reconstruction methods and demands fewer training data than DL approaches, as a single data volume can be broken into several millions of overlapping 3D sub-volumes or patches. This creates a very large number of training sub-volumes from a limited number of overall image volumes. A 3D Generative Adversarial Networks (GAN) is then trained to remove artifacts at the sub-volume level. The combination of a sub-volume-based approach with DL allows us to exploit the richness of the latter in extracting and representing image features, while avoiding risks associated with overfitting due to limited training data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Compact Solid-State High-Voltage Switch

Researchers at Sandia have developed a semiconductor-based high-voltage switch, with experimental results showing potential for enhanced radiation hardness, for use in multiple power conversion applications. Gallium nitride (GaN) metal-oxide semiconductor field effect transistors (MOSFETs) were modeled using commercial and Sandia CHARON simulation software to understand their performance and for future prediction of device operation in radiation environments.

42 ENGINEERING↗
DOE OSTI2021

GANpiler

Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.

54 ENVIRONMENTAL SCIENCES↗