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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Perovskite Quantum Dot Solar Cells: Mapping Interfacial Energetics for Improving Charge Separation

Colloidal halide perovskite nanocrystals or quantum dots (QDs) show similar defect tolerance as thin film perovskite materials with added nanoscale phenomena. Perovskite QD solar cells have demonstrated efficiencies of 16.6%, greater than that of any other QD material system. While the efficiency lags behind the best thin-film perovskite devices, these solar cells could have advantages over the thin-film versions in terms of processability, phase stability, and high open-circuit voltages. However, some operating principles behind perovskite quantum dot device stacks and the associated electric field properties are still unknown. Here, we characterize the junction structure within perovskite QD solar cells, by exposing functioning cross-sections and using nanometer-scale Kelvin probe force microscopy to offer insight into the selection and performance of charge selective contacts. We also evaluated various solar cell device architectures with different selective contacts to isolate the role of each junction in device performance. We show that in high-performance n-i-p architectures, both electron- and hole-transport layer (HTL) interfaces possess a strong electric field, but in the case of the inverted p-i-n architecture, we find that high interfacial recombination at the HTL/QD junction is responsible for subpar device performance. Perovskite QD and thin film materials can synergistically be combined to offer more design flexibility in PV devices, and here we demonstrate that the interface between perovskite thin films and QDs are relatively benign and amenable for synergistic device design.

14 SOLAR ENERGY↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Random Quantum Circuits

Quantum circuits—built from local unitary gates and local measurements—are a new playground for quantum many-body physics and a tractable setting to explore universal collective phenomena far from equilibrium. These models have shed light on longstanding questions about thermalization and chaos, and on the underlying universal dynamics of quantum information and entanglement. In addition, such models generate new sets of questions and give rise to phenomena with no traditional analog, such as dynamical phase transitions in quantum systems that are monitored by an external observer. Quantum circuit dynamics is also topical in view of experimental progress in building digital quantum simulators that allow control of precisely these ingredients. Randomness in the circuit elements allows a high level of theoretical control, with a key theme being mappings between real-time quantum dynamics and effective classical lattice models or dynamical processes. Many of the universal phenomena that can be identified in this tractable setting apply to much wider classes of more structured many-body dynamics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Model-informed, Adaptive Physical Vapor Deposition to Fabricate Hierarchical Binary-alloy Thin-films

Designing next generation thin films, tailor-made for specific applications, relies on the availability of robust processing-structure-property relationships. Traditional structure zone diagrams are limited to low-dimensional mappings, with machine-learning methods only recently attempting to relate multiple processing parameters to the final microstructure. Despite this progress, structure-processing relationships are unknown for processing conditions that vary during thin-film deposition, limiting the range of microstructures and properties achievable. In this project, we employed a phase-field computational model combined with a genetic algorithm (GA) to identify and design time-dependent processing protocols that achieve tailor-made microstructures. We simulate the physical vapor deposition of a binary-alloy thin film by employing a phase-field model, where deposition rates and diffusivities are controlled via the genetic algorithm. Our GA-guided protocols achieve targeted microstructures with lateral and vertical concentration modulations, as well as more complex, hierarchical microstructures previously not described in simple structure zone diagrams. Our algorithm provides insight to experimentalists looking for additional avenues to design novel thin-film microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A two-loop four-point form factor at function level

Recently, the maximally-helicity-violating four-point form factor for the chiral stress-energy tensor in planar $\mathcal{N}$ = 4 super Yang-Mills was computed to three loops at the level of the symbol associated with multiple polylogarithms. It exhibits antipodal self-duality, or invariance under the combined action of a kinematic map and reversing the ordering of letters in the symbol. Here we lift the two-loop form factor from symbol level to function level. We provide an iterated representation of the function’s derivatives (coproducts). In order to do so, we find a three-parameter limit of the five-parameter phase space where the symbol’s letters are all rational. We also use function-level information about dihedral symmetries and the soft, collinear, and factorization limits, as well as limits governed by the form-factor operator product expansion (FFOPE). We provide plots of the remainder function on several kinematic slices, and show that the result is compatible with the FFOPE data. We further verify that antipodal self-duality is valid at two loops beyond the level of the symbol.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Parameter space mapping of the Princeton magnetorotational instability experiment

Extensive simulations of the Princeton Magnetorotational Instability (MRI) Experiment with the Spectral/Finite Element code for Maxwell and Navier-Stokes Equations (SFEMaNS) have been performed to map the MRI-unstable region as a function of inner cylinder angular velocity and applied vertical magnetic field. The angular velocities of the outer cylinder and the end-cap rings follow the inner cylinder in fixed ratios optimized for MRI. Here, we first confirm the exponential growth of the MRI linear phase using idealized conducting vertical boundaries (end caps) rotating differentially with a Taylor-Couette profile. Subsequently, we run a multitude of simulations to scan the experimental parameter space and find that the normalized volume-averaged mean-square radial magnetic field, our main instability indicator, rises significantly where MRI is expected. At various locations, the local radial components of fluid velocity and generated magnetic field are well correlated with the volume-averaged indicator. Based on this correlation, a diagnostic system that will measure the radial magnetic field at several locations on the inner cylinder is proposed as the main comparison between simulation and experiment. A detailed analysis of poloidal mode structures in the SFEMaNS code indicates that MRI, rather than Ekman circulation or Rayleigh instability, dominates the fluid behavior in the region where MRI is expected.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Prepare Ground States of Highly Frustrated Magnetic Clusters on Quantum Computers

Solving challenging problems in physical, chemical, and materials sciences is one of the most promising applications of quantum utility that can be realized on current noisy hardware, considering (i) the direct map (encoding) from the quantum particles and their interactions to the qubits and their entangling gates and (ii) the rapidly improved quantum hardware and advanced error-mitigation techniques. Understanding quantum spin liquid in frustrated magnetic materials is a longstanding challenge in condensed matter physics and the nature of the ground-state phases is highly debated among researchers. Using IBM quantum computers with superconducting qubits, we implemented a variational quantum eigensolver (VQE) algorithm to prepare the ground states of two 12-site cluster approximations of these highly frustrated magnetic materials. The interaction graphs of the two corresponding Hamiltonians are (a) the six-pointed star graph (a unit cell of the kagome lattice) and (b) the cuboctahedral graph (the kagome on a sphere). These are also two instances of Quantum Max Cut problem. With the VQE based on the Hamiltonian variational ansatz acting on a valence bond solid initial trial state, we prepared the ground states and obtained the exact ground energy on simulator and high accuracy on noisy hardware. The deep ansatz necessary to reach the ground state of the cuboctahedral graph indicates that it is a hard instance of Quantum Max Cut.

Wang, Yan↗
DOE OSTI2020

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Big Adaptive Rotor Phase I Final Report

The Big Adaptive Rotor (BAR) project was initiated by the U.S. Department of Energy (DOE) in 2018 with the goal of identifying novel technologies that can enable large (>100 meter [m]) blades for low-specific-power wind turbines. Five distinct tasks were completed to achieve this goal: 1. Assessed the trends, impacts, and value of low-specific-power wind turbines; 2. Developed a wind turbine blade cost-reduction road map study; 3. Completed research-and-development opportunity screening; 4. Performed detailed design and analysis; and, 5. Assessed low-cost carbon fiber. These tasks were completed by the national laboratory team consisting of Sandia National Laboratories (Sandia), the National Renewable Energy Laboratory (NREL), and Lawrence Berkeley National Laboratory.

17 WIND ENERGY↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2026

Flow pattern and void fraction characterization in nitrogen/water flows through a diamond-type triply periodic minimal surface lattice

Here, this study presents, to the authors knowledge, the first experimental investigation on the void fraction and flow patterns in two-phase flows through a diamond-type Triply Periodic Minimal Surface (TPMS) lattice. An additively manufactured TPMS structure was tested under upward co-current flow of a water/nitrogen mixture. Superficial velocities varied for a total of 42 test conditions (gas: 0.01–2.4 m/s; liquid: 0.01–2.4 m/s; mass flux: 20–2370 kg/m 2 ·s). High-speed video and X-ray imaging enabled time-averaged void fraction measurements and identified six distinct flow regimes which were used to develop a flow pattern map. Comparison of the void fraction data with correlations from literature demonstrated the Rouhani and Axelsson (1970) [49] correlation modified by Steiner (1993) [52] provided the best agreement, which was improved with empirically fit coefficients. This approach predicted the void fraction with errors < ±20% for 64% of the data, with a mean absolute percent deviation (MAPD) of 23%. The measured frictional pressure drop was compared to correlations from literature which captured the observed trends but did not provide good accuracy. The best agreement was found after optimizing the empirical coefficients of the Muller-Steinhagen & Heck (1986) [56] correlation; this approach captured 33% of the data within ±20% with a MAPD of 46% over the full range and captured 71% of the data within ±20% a MAPD of 13.3% at mass fluxes >1100 kg/m 2 -s. These results provide foundational insight into TPMS two-phase flow behavior and inform modeling and design of advanced heat exchange components incorporating TPMS geometries.

42 - ENGINEERING↗
DOE OSTI2024

Tunable Growth of Layered Double Hydroxide Nanosheets through Hydrothermal Conversion of Atomic Layer Deposition Seed Layers

To enable the design and manufacturing of hierarchical nanomaterial architectures, there is a need for synthesis and processing methods that can enable tunable geometric control at the nanoscale while maintaining conformality on complex 3-D templates. Here, in this study, we explore the programmable control of vertically oriented Zn-Al layered double hydroxide (LDH) nanosheet arrays using atomic layer deposition (ALD) to deposit a seed layer of Al 2 O 3 , which is subsequently consumed and converted into the LDH phase under hydrothermal growth conditions. We demonstrate tunable control over the spacing and length of the nanosheets by varying the thickness of the initial ALD seed layer with subnanometer precision. This can be viewed as a nanoscale titration reaction, where Al acts as the limiting reagent during the hydrothermal synthesis of the nanosheets. Elemental mapping demonstrates the dynamic evolution of the resulting morphology, which is driven by surface diffusion and nucleation processes. The conformal nature of ALD allows for hierarchical growth of nanosheets on the surface of a variety of nonplanar substrate geometries, including microposts, paper fibers, and porous ceramic supports. This illustrates the power of ALD to enable bottom-up growth of 3-D nanoarchitectures with tunable geometries by controlling nucleation and growth in subsequent solution reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

SIGNAL Game Manual

SIGNAL is a first of its kind experimental wargame developed as part of the Project on Nuclear Gaming (PoNG). In this document we describe the rules and game mechanics associated with the online version of SIGNAL created by team members from the University of California, Berkeley, Sandia National Laboratories, and Lawrence Livermore National Laboratory and sponsored by the Carnegie Corporation of New York. The game was developed as part of a larger research project to develop the experimental wargaming methodology and explore its use on a model scenario: the impact of various military capabilities on conflict escalation dynamics. We discuss the results of this research in a forthcoming paper that will include this manual as an appendix. It is our hope that this manual will both contribute to our players' understanding of the game prior to play and that it will allow for replication of the SIGNAL game environment for future research purposes. The manual begins by introducing the terminology used throughout the document. It then outlines the technical requirements required to run SIGNAL. The following section provides a description of the map, resources, infrastructure, tokens, and action cards used in the game environment. The manual then describes the user interface including the chat functions, trade mechanism, currency and population counts necessary for players to plan their actions. It then turns to the sequence of player actions in the game describing the signaling, action, and upkeep phases that comprise each round of play. It then outlines the use of diplomacy including alliances with minor states and trade between players. The manual also describes the process for scoring the game and determining the winner. The manual concludes with tips for players to remember as they embark upon playing the game.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Impact of Boron-Containing Lubricant Additive on Three-Way Catalyst Reactivity and Physicochemical Properties

Boron-containing compounds are one of the lubricant additive options due to their suitable properties for additives and have been used as commercial lubricant additives. In the present study, the impact of a boron-containing lubricant oil additive, AR9100 (BR), on Pd/Rh-based three-way catalyst (TWC) performance is investigated, and the results are compared with the baseline no-additive (NA) case and the industry standard zinc dialkyl-dithiophosphate (ZDDP) results. Accelerated engine aging is performed using a genset to expose the catalysts to lubricant additives at high temperatures. All aged TWC samples are investigated for reactivity in a bench-flow reactor and characterized using a variety of analytical techniques. Compared with the no-additive case, the temperatures of 90% conversion (T 90 ) of NO, CO, C 3 H 6 , and C 3 H 8 for the ZDDP-aged TWC sample increased by 34, 30, 37, and 48 °C. However, the T 90 of all gas species for the BR-aged TWC sample are similar to the NA-aged TWC sample. Additionally, a significant decrease in water–gas shift reactivity and oxygen storage capacity is observed in the ZDDP-aged sample, but not in the BR-aged sample. Inductively coupled plasma-optical emission spectrometry (ICP-OES) analysis and electron probe microanalysis (EPMA) maps of accelerated engine aging samples show the presence of phosphorus and boron in ZDDP- and BR-aged TWC samples, respectively. However, no boron-related peaks are observed in the X-ray diffraction (XRD) pattern of the BR-aged TWC sample, which may exist in the form of an amorphous phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Direct Mapping of Fluorine in Cation Disordered Rocksalt Cathodes

Cation-disordered rocksalt (DRX) oxides are promising candidates as next-generation cathodes for lithium-ion batteries. Partial fluorination of the DRX oxides enhances their cyclability. However, the lattice position, concentration, and spatial distribution of fluorine within DRX lattices remain elusive. Here, in this work, we use atom location by channeling-enhanced microanalysis, energy-dispersive X-ray spectroscopy, electron energy loss spectroscopy, and integrated differential phase contrast imaging in a scanning transmission electron microscope to gain atomic-level insights into DRX with nominal composition of Li 1.2 Mn 0.7 Ti 0.1 O 1.7 F 0.3 and Li 1.15 Ni 0.45 Ti 0.3 Mo 0.1 O 1.85 F 0.15 . We reveal that fluorine substitutes oxygen in the DRX lattices. The O/F ratio in terms of O+F = 2 is in the range from 1.92:0.08 to 1.82:0.18. Spatially, fluorine is distributed in the proximity of the Li-rich regions but distinct from lithium fluoride. Additionally, we observe that incorporation of fluorine in the DRX lattice induces a larger variation in cation–anion separation. These observations provide insight into the guided design of oxyfluoride DRX cathodes for high-performance batteries.

(S)TEM↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An integrated Imaging and Modeling Toolbox for Accelerated Development of Root-focused Crops at Field Scales

This project, led by Yuxin Wu at the LBNL, was proposed to develop an integrated imaging-modeling toolbox allowing for accelerated development of root-focused crops at field scales. Our approach is based on a novel root phenotyping method we termed Tomographic Electrical Rhizosphere Imaging (TERI), which provides advanced phenotyping of key root traits by sensing the electrical impedance response of roots and soil to external electrical excitations, and mapping these responses to root and soil properties. The role of Noble Research Institute in this project is to assist TERI model establishment and validation by collecting various above- and below-ground traits data from container- and field-grown plants (mainly wheat). Over the last three years, Noble Research Institute has conducted 3 phases of experiments as proposed including: (Phase 1) greenhouse and hoop-house experiments in wheat and pecan plants grown under controlled container-grown conditions; (Phase 2) space-planted individual plant field experiment using different wheat varieties; and (Phase 3) small-plot planted field experiment using different wheat varieties, as well as using different small grains species including wheat, rye, triticale, oat and barley for increasing plant variation.

60 APPLIED LIFE SCIENCES↗