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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Optimization of sputtering NiOx films for perovskite solar cell applications

Highlights: • NiO{sub x} HTLs were fabricated by three kinds of magnetron sputtering techniques. • Chemical composition, lattice strain and energy levels of NiO{sub x} films were studied and compared. • PSCs based on sputtered NiO{sub x} HTL achieved highest PCE of 16.3% with negligible hysteresis. • Compared with PEDOT:PSS HTL, PSCs based on NiO{sub x} HTL exhibited enhanced stability in air. - Abstract: In this work, we systematically investigated the properties of three kinds of NiO{sub x} films prepared by magnetron sputtering either NiO or Ni target under direct current (DC) or radio frequency (RF) power without any preheating. Those NiO{sub x} films were found to be different in composition ratios, lattice strain and energy levels, thus showing different optical and carrier transport properties. By RF power source, sputtering Ni target in pure O{sub 2} atmosphere led to NiO{sub x} film with relatively good crystallization and surface properties compared with other two types of NiO{sub x} films. These NiO{sub x} films were further introduced into CH{sub 3}NH{sub 3}PbI{sub 3} based planar p-i-n heterojunction perovskite solar cells (PSCs) as the hole transport layers (HTL). It is revealed that PSCs based on NiO{sub x} HTL prepared by sputtering Ni target in pure O{sub 2} atmosphere achieved a highest power conversion efficiency of 16.3% with negligible hysteresis. Compared with devices with PEDOT:PSS HTL, PSCs based on magnetron sputtered NiO{sub x} HTL presented substantially improved stability in air. The results provide theoretical and experimental guidance on selecting NiO{sub x} HTLs fabricated by magnetron sputtering techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Novel chemoresistive CH{sub 4} sensor with 10 ppm sensitivity based on multiwalled carbon nanotubes functionalized with SnO{sub 2} nanocrystals

Chemoresistive sensors based on multiwalled carbon nanotubes (MWCNTs) functionalized with SnO{sub 2} nanocrystals (NCs) have great potential for detecting trace gases at low concentrations (single ppm levels) at room temperature, because the SnO{sub 2} nanocrystals act as active sites for the chemisorption of gas molecules, and carbon nanotubes (CNTs) act as an excellent current carrying platform, allowing the adsorption of gas on SnO{sub 2} to modulate the resistance of the CNTs. However, uniform conjugation of SnO{sub 2} NCs with MWCNTs is challenging. An effective atomic layer deposition based approach to functionalize the surface of MWCNTs with SnO{sub 2} NCs, resulting in a novel CH{sub 4} sensor with 10 ppm sensitivity, is presented in this paper. Scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy, and Raman spectroscopy were implemented to study the morphology, elemental composition, and the crystal quality of SnO{sub 2} functionalized MWCNTs. High resolution TEM images showed that the crystal quality of the functionalizing SnO{sub 2} NCs was of high quality with clear lattice fringes and the dimension almost three times smaller than shown thus far in literature. A lift-off based photolithography technique comprising bilayer photoresists was optimized to fabricate SnO{sub 2} functionalized MWCNTs-based chemoresistor sensor, which at room temperature can reliably sense below 10 ppm of CH{sub 4} in air. Such low level gas sensitivity, with significant reversible relative resistance change, is believed to be the direct result of the successful functionalization of the MWCNT surface by SnO{sub 2} NCs.

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

Substitution defect enhancing thermoelectric properties in CuInTe2

Highlights: • Pure phase of Cu1-xAgxInTe2 solid solutions is synthesized successfully via solid state reaction. • Solid solution is an effective way to lower lattice thermal conductivity. • A zT larger than 1.0 is obtained in CuInTe2 based compounds. - Abstract: Solid solution is an effective way to optimize thermoelectric performance because of the enhanced phonon scattering from point defect. In this article we describe the synthesis and discuss thermoelectric properties of Cu{sub 1-x}Ag{sub x}InTe{sub 2} solid solutions (x = 0, 0.05, 0.15, 0.30, 0.50). The effect of the substitution of Ag for Cu site on the microstructure and thermoelectric performance in CuInTe{sub 2} is investigated. The enlarged Seebeck coefficient coupled with reduced electrical conductivity is observed due to the diminished carrier concentration upon Ag substitution. Meanwhile, the lattice thermal conductivity is significantly depressed in solid solutions due to the strong phonon scattering. As a result, a zT value of 1.07 is achieved at 823 K with x = 0.05, which is a 90% improvement as compared with that of the pristine CuInTe{sub 2}. These results demonstrate a valid approach to improve the thermoelectric properties of the compounds in the same family.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Dopant site-dependent luminescence from rare-earth doped dibarium octafluorohafnate Ba 2 HfF 8 nanocubes for radiation detection

Development of new host materials containing heavy elements for radiation detection is highly desirable. In this work, dibarium octafluorohafnate, Ba 2 HfF 8 , doped with rare-earth ions, was synthesized as cube-shaped nanocrystals via a facile hydrothermal method. The host lattice contains two Ba 2+ crystallographic sites, and dopants on these sites exhibit site-dependent photoluminescence (PL), cathodoluminescence (CL) and X-ray excited radioluminescence (RL) characteristics. Single doping contents were optimized as 25 mol% Tb 3+ and 5 mol% Eu 3+ . In Ba 2 HfF 8 :Tb 3+ –Eu 3+ codoped nanocrystals, preferrable occupation of Eu 3+ and Tb 3+ at two different Ba 2+ sites in the host lattice was observed. The nanocubes exhibited enhanced emissions over micron sized particles. In PL, the presence of Tb3+ ions significantly enhanced the emission intensity of Eu 3+ ions due to energy transfer from the Tb 3+ to Eu 3+ ions, while under high-energy irradiation in CL or RL, Tb 3+ emission was intensified. X-ray induced RL with afterglow in seconds was observed. Finally, it was found that the codoped sample showed higher sensitivity than the singly doped sample, indicating that codoping is an effective strategy to develop a scintillator with this host structure for high-energy radiation detection.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2015

Analysis of Advanced Thermoelectric Materials and Their Functional Limits

The world's demand for energy is increasing dramatically, but the best energy conversion systems operate at approximately 30% efficiency. One way to decrease energy loss is in the recovery of waste heat using thermoelectric (TE) generators. A TE generator is device that generates electricity by exploiting heat flow across a thermal gradient. The efficiency of a TE material for power generation and cooling is determined by the dimensionless Figure of Merit (ZT): ZT = S(exp. 2)sigmaT/κ: where S is the Seebeck coefficient, sigma is the electrical conductivity, T is the absolute temperature, and κ is the thermal conductivity. The parameters are not physically independent, but intrinsically coupled since they are a function of the transport properties of electrons. Traditional research on TE materials has focused on synthesizing bulk semiconductor-type materials that have low thermal conductivity and high electrical conductivity affording ZT values of 1. The optimization of the σ/κ ratio is difficult to achieve using current material formats, as these material constants are complementary. Recent areas of research are focusing on using nanostructural artifacts that introduce specific dislocations and boundary conditions that scatter the phonons. This disrupts the physical link between thermal (phonon) and electrical (electron) transport. The result is that κ is decreased without decreasing σ. These material formats give ZT values of up to 2 which represent approximately 18% energy gain from waste heat recovery. The next challenge in developing the next generation of TE materials with superior performance is to tailor the interconnected thermoelectric physical parameters of the material system. In order to approach this problem, the fundamental physics of each parameter S, sigma, and κ need to be physically understood in their context of electron/phonon interaction for the construction of new high ZT thermoelectric devices. Is it possible to overcome the physical limit imposed by of the effect of phonon lattice oscillation and energetic electrons towards thermal conductivity? Is the Seebeck coefficient, based on the difference in voltage over temperature gradient ( deltaV/deltaT), an intrinsic parameter of each material? All these parameters were manipulated using nano-bridge and twin-lattice structural concepts at the NASA Langley Research Center. This talk will review the current trend of TE research to optimize the ZT and discuss about new approaches on increasing ZT within functional limits of each parameter.

Kim, Hyun Jung↗
DOE OSTI2021

The continuum and leading twist limits of parton distribution functions in lattice QCD

In this study, we present continuum limit results for the unpolarized parton distribution function of the nucleon computed in lattice QCD. This study is the first continuum limit using the pseudo-PDF approach with Short Distance Factorization for factorizing lattice QCD calculable matrix elements. Our findings are also compared with the pertinent phenomenological determinations. Inter alia, we are employing the summation Generalized Eigenvalue Problem (sGEVP) technique in order to optimize our control over the excited state contamination which can be one of the most serious systematic errors in this type of calculations. A crucial novel ingredient of our analysis is the parameterization of systematic errors using Jacobi polynomials to characterize and remove both lattice spacing and higher twist contaminations, as well as the leading twist distribution. This method can be expanded in further studies to remove all other systematic errors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

Lattice-mismatched In(0.40)Al(0.60)As window layers for indium phosphide solar cells

The efficiency of indium phosphide (InP) solar cells is limited by its high surface recombination velocity (approximately 10(exp 7) cm/s). This might be reduced by a wide-bandgap window layer. The performance of InP solar cells with wide-bandgap (1.8 eV) lattice-mismatched In(0.40)Al(0.60)As as a window layer was calculated. Because the required window layer thickness is less than the critical layer thickness, growth of strained (pseudomorphic) layers without interfacial misfit dislocations should be possible. Calculations using the PC-lD numerical code showed that the efficiencies of baseline and optimized p(+)n (p-on-n) cells are increased to more than 22 and 24 percent, (air mass zero (AMO), 25 C), respectively for a lattice-mismatched In(0.40)Al(0.60)As window layer of 10-nm thickness. Currently, most cell development work has been focused on n(+)p (n-on-p) structures although comparatively little improvement has been found for n(+)p cells.

Jain, Raj K.↗
DOE OSTI2023

Constraint of pionless EFT using two-nucleon spectra from lattice QCD

Finite-volume pionless effective field theory (FVEFT π/ ) at next-to-leading order (NLO) is used to analyze the two-nucleon lattice QCD spectrum of Ref. [1], performed at quark masses corresponding to a pion mass of approximately 800 MeV. Specifically, the effective theory is formulated in finite volume, and variational sets of wave functions are optimized using differential programming. Using these wave functions projected to the appropriate finite-volume symmetry group, variational bounds from FVEFT π/ are obtained for the ground state, as well as excited states. By comparison with the lattice QCD GEVP spectrum, different low energy constants (LECs) are constrained. Furthermore, relativistic corrections are incorporated, allowing for the s-d-wave mixing term in the deuteron channel.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bayesian Learning of Adatom Interactions from Atomically Resolved Imaging Data

Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. In this work, we develop a workflow based on a machine-learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

In-plane selective area InSb–Al nanowire quantum networks

Abstract Strong spin–orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes. Exchange (braiding) operations of Majorana modes form the logical gates of a topological quantum computer and require a network of nanowires. Here, we utilize an in-plane selective area growth technique for InSb–Al semiconductor–superconductor nanowire networks. Transport channels, free from extended defects, in InSb nanowire networks are realized on insulating, but heavily mismatched InP (111)B substrates by full relaxation of the lattice mismatch at the nanowire/substrate interface and nucleation of a complete network from a single nucleation site by optimizing the surface diffusion length of the adatoms. Essential quantum transport phenomena for topological quantum computing are demonstrated in these structures including phase-coherence lengths exceeding several micrometers with Aharonov–Bohm oscillations up to five harmonics and a hard superconducting gap accompanied by 2 e -periodic Coulomb oscillations with an Al-based Cooper pair island integrated in the nanowire network.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging

We present a novel adaptive machine-learning based approach for reconstructing three-dimensional (3D) crystals from coherent diffraction imaging. We represent the crystals using spherical harmonics (SH) and generate the corresponding synthetic diffraction patterns. We utilize 3D convolutional neural networks (CNNs) to learn a mapping between 3D diffraction volumes and the SH, which describe the boundary of the physical volumes from which they were generated. We use the 3D CNN-predicted SH coefficients as the initial guesses, which are then fine-tuned using adaptive model-independent feedback for improved accuracy. We also adaptively tune the locations, intensities, and decay rates of collections of radial basis functions in order to reproduce the non-uniform internal structure of 3D objects and demonstrate the method for a synthetic volume that has an internal void and a density ramp.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine learning changes the rules for flux limiters

Learning to integrate non-linear equations from highly resolved direct numerical simulations has seen recent interest for reducing the computational load for fluid simulations. Here, we focus on determining a flux-limiter for shock capturing methods. Focusing on flux limiters provides a specific plug-and-play component for existing numerical methods. Since their introduction, an array of flux limiters has been designed. Using the coarse-grained Burgers' equation, we show that flux-limiters may be rank-ordered in terms of their log-error relative to high-resolution data. We then develop a theory to find an optimal flux-limiter and present flux-limiters that outperform others tested for integrating Burgers' equation on lattices with [Formula: see text], and 2x, 3x, 4x, and 8x coarse-grainings. We train a continuous piecewise linear limiter by minimizing the mean-squared misfit to six-grid point segments of high-resolution data, averaged over all segments. While flux limiters are generally designed to have an output of φ(r) = 1 at a flux ratio of r = 1, our limiters are not bound by this rule and yet produce a smaller error than standard limiters. Here we find that our machine learned limiters have distinctive features that may provide new rules-of-thumb for the development of improved limiters. Additionally, we use our theory to learn flux-limiters that outperform standard limiters across a range of values (as opposed to at a specific fixed value) of coarse-graining, number of discretized bins, and diffusion parameter. This demonstrates the ability to produce flux limiters that should be more broadly useful than standard limiters for general applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Ab initio calculations of exciton effects in optical spectra of an α-B{sub 12} crystal

The dielectric functions and energy loss spectrum of electrons of a rhombohedral α-B{sub 12} crystal are studied both in the single-particle and many-particle approximations using Bethe–Salpeter equations. The opposite roles of different contributions to exciton effects are discussed. The anisotropy of dielectric functions is shown, which demonstrates their high sensitivity with respect to the three-dimensional packing of icosahedrons. The influence of the coherent mixing of electronic and exciton states on the redistribution of oscillator strengths is found. The position of the plasmon of valence electrons and the high-frequency permittivity are found to be consistent with experimental data. The correlation between the distribution of oscillator strengths and features of the density of electronic states is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Imaging Sequences for Hyperpolarized Solids

Hyperpolarization is one of the approaches to enhance Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) signal by increasing the population difference between the nuclear spin states. Imaging hyperpolarized solids opens up extensive possibilities, yet is challenging to perform. The highly populated state is normally not replenishable to the initial polarization level by spin-lattice relaxation, which regular MRI sequences rely on. This makes it necessary to carefully “budget” the polarization to optimize the image quality. In this paper, we present a theoretical framework to address such challenge under the assumption of either variable flip angles or a constant flip angle. In addition, we analyze the gradient arrangement to perform fast imaging to overcome intrinsic short decoherence in solids. Hyperpolarized diamonds imaging is demonstrated as a prototypical platform to test the theory.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Update on the JLEIC Electron Collider Ring Design

We present an update on the lattice design of the electron ring of the Jefferson Lab Electron-Ion Collider (JLEIC). The electron and ion collider rings feature a unique figure-8 layout providing optimal conditions for preservation of beam polarization. The rings include two arcs and two intersecting long straight sections containing a low-beta interaction region (IR) with special optics for detector polarimetry, electron beam spin rotator sections, ion beam cooling sections, and RF-cavity sections. Recent development of the electron ring lattice has been focused on minimizing the beam emittance while providing an efficient non-linear chromaticity correction and large dynamic aperture. We describe and compare three lattice designs, from which we determine the best option.

Nosochkov, Y.↗