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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Enhancing nanoscale charged colloid crystallization near a metastable liquid binodal

Achieving predictive control over crystallization using non-classical nucleation while avoiding kinetic traps would be a step towards designing materials with new functionalities. We address these challenges by inducing the bottom-up assembly of nanocrystals into ordered arrays, or superlattices. Using electrostatics—rather than density—to tune the interactions between particles, we watch self-assembly proceed through a metastable liquid phase. Here, we systematically investigate the phase behaviour as a function of quench conditions in situ and in real time using small-angle X-ray scattering. By fitting to colloid, liquid and superlattice models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the predictive power of the phase diagram, we establish control of the self-assembly rate over three orders of magnitude, and we identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. The presence of the metastable liquid increases the superlattice formation rate relative to the equivalent one-step pathway, and the superlattice order increases with the rate, revealing a generalizable kinetic strategy for promoting and enhancing ordered assembly.

Tanner, Christian P. N. [University of California,↗
DOE OSTI2022

Virtual diffraction simulations using the quasi-coarse-grained dynamics method to understand and interpret plasticity contributions during in situ shock experiments

The dynamic deformation response of metallic materials has contributions from dislocations, deformation twinning, and plastic deformation. The current state-of-art techniques can detail the complex mechanistic history of deformation modes under shock loading in real-time using in situ X-ray diffraction (XRD). However, the capability of these experiments to unravel plasticity contributions is challenging due to limitations in interpreting results and the lack of validation from atomistic simulations. Molecular dynamics (MD) simulations can successfully capture various deformation modes in metals and complement experiments using simulated diffractograms at various stages of evolution. However, the difference in length and time scales of MD simulations and experiments is a substantial obstacle in completing and interpreting in situ diffractograms. Therefore, the existing modeling methods require various approximations to model defect evolution and interaction at the mesoscales. However, while using approximations to correlate the peak broadening behavior to the density of dislocations or the shifts/splitting due to the presence of twins, the interpretations of the plasticity contributions from diffractograms are non-trivial, especially when multiple modes of deformation may be operating. This viewpoint discusses combining a mesoscale modeling method called quasi-coarse-grained dynamics and virtual XRD to characterize the plasticity contributions in BCC metals from slip, twinning, and phase transformation behavior. Furthermore, the combined approach shows promise in bridging the mesoscale gap between the capabilities of atomic-scale simulations and in situ experiments to characterize the dynamic deformation of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Building envelope anomaly characterization and simulation using drone time-lapse thermography

Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Metal-Organic Framework (MOF)-Based Multifunctional Cargo Vehicle for Reactive-Gas Delivery and Catalysis

The efficient delivery of reactive and toxic gaseous reagents to organic reactions was studied using metal-organic frameworks (MOFs). The simultaneous cargo vehicle and catalytic capabilities of several MOFs were probed for the first time using the examples of aromatization, aminocarbonylation, and carbonylative Suzuki–Miyaura coupling reactions. These reactions highlight that MOFs can serve a dual role as a gas cargo vehicle and a catalyst, leading to product formation with yields similar to reactions employing pure gases. Furthermore, the MOFs can be recycled without sacrificing product yield, while simultaneously maintaining crystallinity. Our reported findings were supported crystallographically and spectroscopically (e.g., diffuse reflectance infrared Fourier transform spectroscopy), foreshadowing a pathway for the development of multifunctional MOF-based reagent-catalyst cargo vessels for reactive gas reagents as an attractive alternative to the use of toxic pure gases or gas generators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multi-modal characterization of the B2 phase in the Ta-Re binary system

The energy and transportation industries demand materials that retain their mechanical property at high temperatures. Refractory complex concentrated alloys (RCCAs) with a BCC + B2 microstructure offer a potential solution, where maintaining the high temperature mechanical properties can be achieved by precipitation strengthening. This depends on the B2 phase in RCCAs being thermodynamically stable with a high solvus temperature. Recently, we predicted the high temperature stability of the B2 structure in the Ta-Re binary system, using density functional theory. Here, in this study, we provide experimental evidence for the existence of this phase for the first time, using a Ta 65 Re 35 alloy. Despite Ta-Re binary phase diagrams predicting a single-phase BCC microstructure for Ta 65 Re 35 , we show that a high Z nanoscale secondary phase appears after heat treatment at 1550 °C and 1100 °C. Scanning transmission electron microscopy (STEM) revealed that this phase has a cubic structure and is equiatomic TaRe though B2 superlattice reflections were absent in fast Fourier transforms (FFT) and diffraction patterns (DPs). DP simulations indicate that the B2 TaRe superlattice reflections are up to two orders of magnitude weaker than their fundamental reflections, making their detection challenging via electron microscopy. Neutron diffraction confirmed the second phase had a B2 structure. This study identified a previously unobserved high temperature stable B2 phase in the Ta-Re system, enabling the development of new high temperature BCC + B2 RCCAs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution Systems

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework's superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

cyber-physical system↗
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Inferring Heat Flow in Laser Absorption Regions using Diagnostic Magnetic Fields

This research was motivated by the desire to obtain a measurement of heat-flow in overdense regions of a laser ablated plasma for the first time using a high energy proton beam. For sufficiently high energies, the proton beam can pass through overdense regions and only be deflected by electric and magnetic fields. As the transport of magnetic fields in a plasma are intrinsically linked to the transport of thermal energy, it was posited that such a measurement could be used to infer a heat-flow. While the project did not prove the method to be infeasible, difficulties with understanding the proton radiographs without an imposed magnetic field made the final aim of this project unreached. Instead, however, a closer understanding of proton radiographs of laser-solid interactions without an imposed field has been obtained. 5 shot days on The OMEGA Laser Facility have been awarded (3 through ICF and 2 as ride-along) to further investigate some of these features. Extrapolating current results to hohlraum conditions has the magnetic energy being a significant (10%) fraction of the laser drive energy, which has the potential to explain the NIF hohlraum drive deficit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Inferring Heat Flow in Laser Absorption Regions using Diagnostic Magnetic Fields: Abstract

This research was motivated by the desire to obtain a measurement of heat-flow in overdense regions of a laser ablated plasma for the first time using a high energy proton beam. For sufficiently high energies, the proton beam can pass through overdense regions and only be deflected by electric and magnetic fields. As the transport of magnetic fields in a plasma are intrinsically linked to the transport of thermal energy, it was posited that such a measurement could be used to infer a heat-flow. While the project did not prove the method to be infeasible, difficulties with understanding the proton radiographs without an imposed magnetic field made the final aim of this project unreached. Instead, however, a closer understanding of proton radiographs of laser-solid interactions without an imposed field has been obtained. 5 shot days on The OMEGA Laser Facility have been awarded (3 through ICF and 2 as ride-along) to further investigate some of these features. Extrapolating current results to hohlraum conditions has the magnetic energy being a significant (10%) fraction of the laser drive energy, which has the potential to explain the NIF hohlraum drive deficit.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Single pot catalyst strategy to branched products via adhesive isomerization and hydrocracking of polyethylene over platinum tungstated zirconia

Hydrocracking is an insufficiently explored route for chemical recycling of plastics waste. Platinum tungstated zirconia (Pt-WZr) was used in a batch reactor at low temperatures of 250 °C and 30 bar H 2 pressure for 1-24 h reaction times using low-density polyethylene (LDPE, M w ~76 kDa). We find Pt-WZr is a bifunctional catalyst for LDPE hydrocracking leading to higher value branched fuel- and lubricant-ranged alkanes. Here we demonstrate that the catalyst metal-to-acid site molar ratio (MAB) shifts the product distribution to larger cracked products and increases the isomerization degree in the residual polymer. We propose a new adhesive isomerization mechanism between the metal and Brønsted acid sites in parallel with slow polymer chain cracking, caused by competitive adsorption of the polymer over the liquid products and stereochemical hindrance of methines. This study provides a blueprint on how to engineer effective catalysts for hydrocracking polyolefin plastic wastes using the MAB as a catalyst descriptor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis

The calibration and validation of scientific analysis in simulations is a fundamental tool to ensure unbiased and robust results in observational cosmology. In particular, mock galaxy catalogs are a crucial resource to achieve these goals in the measurement of baryon acoustic oscillation (BAO) in the clustering of galaxies. Here we present a set of 1952 galaxy mock catalogs designed to mimic the Dark Energy Survey Year 3 BAO sample over its full photometric redshift range 0.6 < zphoto < 1.1. The mocks are based upon 488 ICE-COLA fast N-body simulations of full-sky light cones and were created by populating halos with galaxies, using a hybrid halo occupation distribution – halo abundance matching model. This model has ten free parameters, which were determined, for the first time, using an automatic likelihood minimization procedure. We also introduced a novel technique to assign photometric redshift for simulated galaxies, following a two-dimensional probability distribution with VIMOS Public Extragalactic Redshift Survey data. The calibration was designed to match the observed abundance of galaxies as a function of photometric redshift, the distribution of photometric redshift errors, and the clustering amplitude on scales smaller than those used for BAO measurements. An exhaustive analysis was done to ensure that the mocks reproduce the input properties. Finally, mocks were tested by comparing the angular correlation function w(θ), angular power spectrum Cℓ, and projected clustering ξp(r⊥) to theoretical predictions and data. The impact of volume replication in the estimate of the covariance is also investigated. The success in accurately reproducing the photometric redshift uncertainties and the galaxy clustering as a function of redshift render this mock creation pipeline as a benchmark for future analyses of photometric galaxy surveys.Key words: catalogs / large-scale structure of Universe / galaxies: distances and redshifts / Galaxy: halo / methods: numerical

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Comparison of DER Voltage Regulation Technologies Using Real-Time Simulations

Grid operators are now considering using distributed energy resources (DERs) to provide distribution voltage regulation rather than installing costly voltage regulation hardware. DER devices include multiple adjustable reactive power control functions, so grid operators have the difficult decision of selecting the best operating mode and settings for the DER. In this work, we develop a novel state estimation-based particle swarm optimization (PSO) for distribution voltage regulation using DER-reactive power setpoints and establish a methodology to validate and compare it against alternative DER control technologies (volt–VAR (VV), extremum seeking control (ESC)) in increasingly higher fidelity environments. Distribution system real-time simulations with virtualized and power hardware-in-the-loop (PHIL)-interfaced DER equipment were run to evaluate the implementations and select the best voltage regulation technique. Each method improved the distribution system voltage profile; VV did not reach the global optimum but the PSO and ESC methods optimized the reactive power contributions of multiple DER devices to approach the optimal solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A facile approach for site-selective and large-area growth of MoS 2 through heterogeneous nucleation

Heterogeneous nucleation could be an effective approach to producing an array of transition metal dichalco-genides (TMDs) at controlled locations on a substrate. In this study, we prepared Pt mesa structures on the substrate to induce heterogeneous nucleation of MoS 2 during chemical vapor deposition (CVD) and investigated the adsorption of nuclei using Monte Carlo (MC) simulations. The MC simulations suggest that the site-selectivity of MoS 2 dramatically drops when the CVD growth time goes beyond the critical time at which nuclei fully cover the Pt mesas, and such critical time can be delayed when the size of Pt mesas increases. Our experimental observations on a clean array of MoS 2 successfully synthesized on 10 um-width square Pt mesas but not on 4 um -width square Pt mesas support the MC simulation. We further designed a simple method employing dummy substrates to increase the yield of the site-selective MoS 2 array. Computational fluid dynamics (CFD) simulation reveals that such dummy substrates restrict the precursor concentration and provide a confined channel for the precursors to react slowly, which benefits producing large-area and site-selective MoS 2 . Furthermore, compared with conventional CVD, the total coverage of site-selective MoS 2 has enlarged by seven times using dummy substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
DOE OSTI2024

Immobilization of Urease for continuous flow conversion of waste urea

An efficient and robust system for the urease catalyzed conversion of urea to ammonia has been developed using urease immobilized on modified agarose beads. Two different immobilization strategies, adsorption and covalent binding were studies using six different types of modified agarose beads. The immobilization of urease on each of the beads was studied at different concentrations and times using immobilization efficiency as a measure of success. The data from these experiments was used to identify potential candidates for immobilization scale up and implementation into the continuous flow system. The enzyme was then immobilized on the candidates in a packed bed reactor and the optimal flow rate and storage stability was determined. Future work will utilize the data obtained from these experiments to expand to other resins and enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Astrophysical and Theoretical Physics Implications from Multimessenger Neutron Star Observations

Here, the Neutron Star Interior Composition Explorer (NICER) recently measured the mass and equatorial radius of the isolated neutron star PSR J0030+0451. We use these measurements to infer the moment of inertia, the quadrupole moment, and the surface eccentricity of an isolated neutron star for the first time, using relations between these quantities that are insensitive to the unknown equation of state of supranuclear matter. We also use these results to forecast the moment of inertia of neutron star A in the double pulsar binary J0737-3039, a quantity anticipated to be directly measured in the coming decade with radio observations. Combining this information with the measurement of the tidal Love number with LIGO/Virgo observations, we propose and implement the first theory-agnostic and equation-of-state-insensitive test of general relativity. Specializing these constraints to a particular modified theory, we find that consistency with general relativity places the most stringent constraint on gravitational parity violation to date, surpassing all other previously reported bounds by 7 orders of magnitude and opens the path for a future test of general relativity with multimessenger neutron star observations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Subsurface stress criticality associated with fluid injection and determined using machine learning

Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Johnson, Paul Allan↗