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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Crosstalk-robust quantum control in multimode bosonic systems

High-coherence superconducting cavities offer a hardware-efficient platform for quantum information processing. To achieve universal operations of these bosonic modes, the requisite nonlinearity is realized by coupling them to a transmon ancilla. However, this configuration is susceptible to crosstalk errors in the dispersive regime, where the ancilla frequency is Stark shifted by the state of each coupled bosonic mode. This leads to a frequency mismatch of the ancilla drive, lowering the gate fidelities. To mitigate such coherent errors, we employ quantum optimal control to engineer ancilla pulses that are robust to the frequency shifts. These optimized pulses are subsequently integrated into a recently developed echoed conditional displacement protocol for executing single- and two-mode operations. Through numerical simulations, we examine two representative scenarios: the preparation of single-mode Fock states in the presence of spectator modes and the generation of two-mode entangled Bell-cat states. Our approach markedly suppresses crosstalk errors, outperforming conventional ancilla control methods by orders of magnitude. These results provide guidance for experimentally achieving high-fidelity multimode operations and pave the way for developing high-performance bosonic quantum information processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Modelling of a Flow-Induced Oscillation, Two-Cylinder, Hydrokinetic Energy Converter Based on Experimental Data

The VIVACE Converter consists of cylindrical oscillators in tandem subjected to transverse flow-induced oscillations (FIOs) that can be improved by varying the system parameters for a given in-flow velocity: damping, stiffness, and in-flow center-to-center spacing. Compared to a single isolated cylinder, tandem cylinders can harness more hydrokinetic energy due to synergy in FIO. Experimental and numerical methods have been utilized to analyze the FIO and energy harnessing of VIVACE. A surrogate-based model of two tandem cylinders is developed to predict the power harvesting and corresponding efficiency by introducing a backpropagation neural network. It is then utilized to reduce excessive experimental or computational testing. The effects of spacing, damping, and stiffness on harvested power and efficiency of the established prediction-model are analyzed. At each selected flow velocity, optimization results of power harvesting using the prediction-model are calculated under different combinations of damping and stiffness. The main conclusions are: (1) The surrogate model, built on extensive experimental data for tandem cylinders, can predict the cylinder oscillatory response accurately. (2) Increasing the damping ratio range from 0–0.24 to 0–0.30 is beneficial for improving power efficiency, but has no significant effect on power harvesting. (3) In galloping, a spacing ratio of 1.57 has the highest optimal harnessed power and efficiency compared with other spacing values. (4) Two tandem cylinders can harness 2.01–4.67 times the optimal power of an isolated cylinder. In addition, the former can achieve 1.46–4.01 times the efficiency of the latter. (5) The surrogate model is an efficient predictive tool defining parameters of the Converter for improved energy acquisition.

backpropagation neural network↗
DOE OSTI2017

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Computational and Experimental Evaluation of Peroxide Oxidants for Amine-Peroxide Redox Polymerization

Amine–peroxide redox polymerization (APRP) is the prevalent method for producing radical-based polymers in the many industrial and medical applications where light or heat activation is impractical. We recently developed a detailed description of the APRP initiation process through a combined computational and experimental effort to show that APRP proceeds through SN2 attack by the amine on the peroxide, followed by the rate-determining homolysis of the resulting intermediate. Using this new mechanistic understanding, a variety of peroxides were computationally predicted to initiate APRP with fast kinetics. In particular, the rate of APRP initiation can be improved by radical and anion stabilization through increased p-electron conjugation or by increasing the electrophilicity of the peroxy bond through the addition of electron-withdrawing groups. On the other hand, the addition of electron-donating groups lowered the initiation rate. These design principles enabled the computational prediction of several new peroxides that exhibited improved initiation rates over the commonly used benzoyl peroxide. For example, the addition of nitro groups (NO2) to the para positions of benzoyl peroxide resulted in a theoretical radical generation rate of 1.9 × 10–9 s–1, which is ~150 times faster than the 1.3 × 10–11 s–1 radical generation rate observed with unsubstituted benzoyl peroxide. These accelerated kinetics enabled the development of a redox-based direct-writing process that exploited the extremely rapid reactivity of an optimized redox pair with a custom inkjet printer, capable of printing custom shapes from polymerizing resins without heat or light. Furthermore, the application of more rapid APRP kinetics could enable the acceleration of existing industrial processes, make new industrial manufacturing methods possible, and improve APRP compatibility with biomedical applications through reduced initiator concentrations that still produce rapid polymerization rates.

functional polymers↗
DOE OSTI2020

Comparative investigations of multi-fidelity modeling on performance of electrostatically-actuated cracked micro-beams

Silicon is a commonly used material for the fabrication of beams for use in micro-electrical-mechanical systems (MEMS). Although silicon is a brittle material, it has been shown to accumulate fatigue damage at the micro-scale. Understanding the effect this has on the overall device performance is critical to the design of reliable devices. Analytical methods for modeling damage provide expedient results but are limited by broad modeling assumptions. Numerical models account for more detailed physical phenomena but can be computationally intensive. In this work, two different crack scenarios are modeled using both analytical techniques and 3D computational simulations. First, the effects of a single surface crack on the static deflection and natural frequency of an electrostatically actuated micro-beam are formulated and compared. Then, a new method for approximating damage associated with realistic distributed crack networks is formulated for use in an analytical model and numerical simulations. A method for utilizing experimentally derived crack statistics to inform the analytical and numerical distributed crack models is developed. Good agreement between the analytical and numerical models is obtained for both crack scenarios. Altogether, these models can be used to effectively simulate a variety of damage and fatigue behaviors in silicon-based MEMS devices.

42 ENGINEERING↗
DOE OSTI2019

Monte Carlo calculation of the average neutron depolarization for the NPDGamma experiment

The NPDGamma experiment measures the asymmetry in γ-ray emission in the capture of polarized neutrons on liquid parahydrogen. The beam polarization is measured using 3 He spin analysis, but this measurement does not account for the contribution of depolarization from spin–flip scattering primarily due to orthohydrogen in the bulk liquid. This is a systematic effect that dilutes the experimental asymmetry and is modeled using Monte Carlo. Finally, methods for tracking neutron spin in MCNPX were developed in order to calculate the average neutron polarization upon capture for use as a multiplicative correction to the measured beam polarization for the NPDGamma experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Microstructural evolution of an aluminide coating on alloy 625 during wet air exposure at 900 °C and 1000 °C

The microstructural changes of the aluminized alloy 625 during cyclic oxidation in air + 6% H 2 O at 900 °C and 1000 °C were analyzed using optical metallography (OM), scanning electron microscopy (SEM) with energy and wave length dispersive X-ray analysis (EDX/WDX) as well as electron backscatter diffraction (EBSD). An in-house developed thermodynamic-kinetic procedure was employed to predict the microstructural evolution of aluminized alloy 625 during high temperature exposure by considering simultaneously occurring surface oxidation and interdiffusion processes. Because of the lack of mobility data for the relevant alloying elements in the σ-phase, assumptions for the mobilities were made based on the value of the mobilities in α-Cr. Despite these assumptions, the calculated results were found to be in good agreement with experimental observations. The complete depletion of β-NiAl in the coating observed during exposure at 1000 °C was correctly predicted by the model. The model was also able to infer dissolution of the precipitate phases α-Cr and σ in the interdiffusion zone during exposures at 900 °C and 1000 °C. The model was yet unable to predict the formation of the μ-phase in the alloy after 1000 h of exposure at 1000 °C. The developed modelling method offers the potential to predict microstructural changes of aluminized nickel base alloys thus reducing cost and time consuming experimental efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗
DOE OSTI2023

The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery

Here, the advent of high-throughput Density Functional Theory (DFT) calculations has supported the creation of large databases containing the quantitative output necessary for constructing theoretical phase diagrams and predicting physical properties. In this Article, we present a complementary resource, the Intermetallic Reactivity Database (IRD), focused on the chemical bonding features of solid-state structures and indicators of potential structural transformations. Each IRD entry augments common features, such as band structures and density of states (DOS) distributions, with chemically motivated information including DFT-Chemical Pressure (CP) schemes and visualizable representations of the atomic charges. Together, these data types enable the rationalization and prediction of potential structural phenomena encountered in intermetallic chemistry, as we illustrate with four examples: the origins of the Y 2 Ni 2 Mg structure in terms of CP features of its parent structures, the anticipation of intergrowth phases from the net atomic CPs collected in Al-containing binary phases, the correlation between trends in the CP schemes of CaCu 5 -type phases and experimentally observed structural variations, and finally, the development of theoretical methodology with the testing of a streamlined method generating DFT-CP schemes. Altogether, these examples highlight how the IRD supports the creation of models of structural chemistry that extend beyond the bounds of its entries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Liquid–Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3 ) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl 3 molten salt across varied thermodynamic conditions (T = 473–613 K and P = 2.7–23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid–vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl 3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl 3 that are within 10 K (∼3%) and 0.03 g/cm 3 (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl 3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al 2 Cl 6 dimers, as validated using Raman spectra and neutron structure factor. Furthermore, the PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl 3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Ab initio molecular dynamics↗
DOE OSTI2018

First-Principles Investigations of the Temperature Dependence of Electronic Structure and Optical Properties of Rutile TiO 2

To gain additional insight into high-temperature functional material properties for applications in optical gas sensing, the temperature effects on the band gap and optical properties of rutile TiO 2 are investigated using ab initio methods. By analyzing the contributions from electron–phonon interaction and lattice thermal expansion, we show that the electron–phonon interaction is the dominant factor for temperature band-gap renormalization. As the temperature increases, the band gap increases until 300 K and then narrows above 300 K. This behavior results from the acoustic phonons, which widen the band gap, dominate below 300 K, while the optical phonons, which narrow the band gap, dominate above 300 K. Our study suggests that the band gap is narrowed by about 138 meV at 1000 K. We also investigated the temperature effects on the dielectric constants, the refractive index, as well as the extinction coefficient. Both the rate of decrease of the refractive index at 650 and 800 nm as well as the experimentally derived band gap agree with experimentally measured data as the temperature increases. Furthermore, our results and computational methods are of interest for developing high-temperature functional materials with applications toward gas sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuron-glia co-culture on a multi-electrode array

Abstract The brain’s extracellular matrix (ECM) is a macromolecular network composed of glycosaminoglycans, proteoglycans, glycoproteins, and fibrous proteins. In vitro studies often use purified ECM proteins for cell culture coatings, however these may not represent the molecular complexity and heterogeneity of the brain’s ECM. To address this, we compared neural network activity (over 30 days in vitro ) from primary neurons co-cultured with glia grown on ECM coatings from decellularized brain tissue (bECM) or MaxGel, a non-tissue-specific ECM. Cells were grown on a multi-electrode array (MEA) to enable noninvasive long-term interrogation of neuronal networks. In general, the presence of ECM accelerated the formation of networks without affecting the inherent network properties. However, specific features of network activity were dependent on the type of ECM: bECM enhanced network activity over a greater region of the MEA whereas MaxGel increased network burst rate associated with robust synaptophysin expression. These differences in network activity were not attributable to cellular composition, glial proliferation, or astrocyte phenotypes, which remained constant across experimental conditions. Collectively, the addition of ECM to neuronal cultures represents a reliable method to accelerate the development of mature neuronal networks, providing a means to enhance throughput for routine evaluation of neurotoxins and novel therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

The structural parameters of self-assembled quantum dots determined from the optical spectra

Structural parameters of InGaAs/GaAs self-assembled quantum dots (SAQDs), which were grown using In-flush technique, were deduced using optical spectroscopy combined with computer modeling. The results are in excellent agreement with the experimental data obtained from transmission electron microscopy. The developed approach suggests a promising alternative to structural characterization methods for SAQDs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

An analytical model to predict curvature effects of the carbon nanotube on the overall behavior of nanocomposites

In this study, analytical expressions are introduced to provide a better understanding of carbon nanotubes (CNTs) curvature on the overall behavior of nanocomposites. The curviness of CNT is modeled as the wave geometries, and the transformed physical characteristics are applied to micromechanical framework. Since five independent elastic constants of CNTs are essential to derive the waviness effect, atomistic molecular statics simulations with varying nanotube radii are conducted. Influences of CNT curviness on the effective stiffness of the nanocomposites are analyzed, noting that the curvature effect is significantly influential on the effective stiffness of the nanocomposites, and it may improve or reduce the reinforcing effect depending on the orientation of CNTs. In addition, the predictions are compared with experimental data of the CNT-reinforced nanocomposites to assess the reliability of the proposed method. The developed constitutive model is expected to be used to determine the volume concentration of the reinforcing CNTs and mechanical responses of CNT-reinforced composites under various CNT curvature, radius, and orientation conditions.

42 ENGINEERING↗
DOE OSTI2019

Skin remittance kinetics in the spectral range of 550 – 790 nm

An experimental method for remitted photon time-of-flight (PTOF) estimation in human skin in vivo is developed and tested. Remitted light signals are obtained using a picosecond broadband laser and a set of narrowband interference filters centred at 550, 600, 650, 700, 750 and 790 nm. Four different distances of 8, 12, 16 and 20 mm between the source and detector fibres are used. Measurements are performed at different wavelengths and distance combinations. Direct kinetic measurements make it possible to assess the mean photon path length in skin at various spectral regions. Results generally correspond to theoretical expectations; however, no clear dependence on wavelengths in the spectral range 650–790 nm is observed. This study shows a promising opportunity to use this kind of measurement system in the future research of PTOF in human skin. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Rare isotope-containing diamond colour centres for fundamental symmetry tests

Detecting a non-zero electric dipole moment in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such ‘pear-shaped’ nuclei, capturing, detecting and manipulating them efficiently is a crucial prerequisite. Incorporating them into synthetic diamond optical crystals can produce defects with defined, molecule-like structures and isolated electronic states within the diamond band gap, increasing capture efficiency, enabling repeated probing of even a single atom and producing narrow optical linewidths. In this study, we used density functional theory to investigate the formation, structure and electronic properties of crystal defects in diamond containing 229 ⁢Pa, a rare isotope that is predicted to have an exceptionally strong nuclear octupole deformation. In addition, we identified and studied stable lanthanide-containing defects with similar electronic structures as non-radioactive proxies to aid in experimental methods. Our findings hold promise for the existence of such defects and can contribute to the development of a quantum information processing-inspired toolbox of techniques for studying rare isotopes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

Computational Nanophotonics: modeling optical interactions and transport in tailored nanosystem architectures

This report describes research by George Schatz and Mark Ratner that was done over the period 10/03-5/09 at Northwestern University. This research project was part of a larger research project with the same title led by Stephen Gray at Argonne. A significant amount of our work involved collaborations with Gray, and there were many joint publications as summarized later. In addition, a lot of this work involved collaborations with experimental groups at Northwestern, Argonne, and elsewhere. The research was primarily concerned with developing theory and computational methods that can be used to describe the interaction of light with noble metal nanoparticles (especially silver) that are capable of plasmon excitation. Classical electrodynamics provides a powerful approach for performing these studies, so much of this research project involved the development of methods for solving Maxwell’s equations, including both linear and nonlinear effects, and examining a wide range of nanostructures, including particles, particle arrays, metal films, films with holes, and combinations of metal nanostructures with polymers and other dielectrics. In addition, our work broke new ground in the development of quantum mechanical methods to describe plasmonic effects based on the use of time dependent density functional theory, and we developed new theory concerned with the coupling of plasmons to electrical transport in molecular wire structures. Applications of our technology were aimed at the development of plasmonic devices as components of optoelectronic circuits, plasmons for spectroscopy applications, and plasmons for energy-related applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗