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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Micrometer-sized Magnetite Synthesis using Fe(OH)2(s) as a Precursor for Technetium Sequestration from Liquid Nuclear Waste Streams

Systematic batch experiments under variable adjusted physicochemical conditions were conducted to explore optimization of micrometer-sized magnetite synthesis for Tc sequestration from radionuclide waste streams using Fe(OH)2(s) as the precursor. Extensive solid characterization using x-ray diffraction and spectroscopic methods was performed to assess changes in particle morphology and size distribution, as well as Tc speciation and incorporation, in the produced mineral phases. The results show that the solution pH, temperature, and oxidation kinetics play key roles in the final mineral products. Micrometer-sized magnetite crystals (0.62-0.96 µm on average) with well-defined dodecahedral or octahedral structures were synthesized under near neutral (~pH 8) or alkaline (~pH13) conditions at 75 °C, respectively; whereas goethite dominated the end products at room temperature. An increase in pH at 75 °C improved Tc removal from 27% (near neutral pH) to 42% (alkaline pH), but the removal process remained inhibited by redox competitive Cr(VI) present in the waste streams. By adding additional Fe(II) to the system, Tc sequestration was dramatically improved to up to 87% without observable changes in the solid product. The sequestrated Tc existed as TcO2·2H2O and/or Tc(IV) incorporated into magnetite, where extended X-ray absorption fine structure (EXAFS) spectroscopy showed that more Tc was incorporated into magnetite at elevated temperatures and pH conditions, with complete Tc(IV) incorporation into magnetite occurring under 75 °C-pH 13 conditions. Our results indicate that optimal micrometer-sized magnetite can be produced for Tc sequestration by reacting Fe(OH)2(s) with a waste stream simulant under elevated pH (~13) and temperature (75 °C) conditions. The incorporation of reduced Tc(IV) into stable micrometer-sized magnetite provides a viable supplemental immobilizing technology that may be used to improve nuclear waste treatment and disposal needs.

Wang, Guohui↗
DOE OSTI2024

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating the impact of preparation routes on the properties of copper-decorated silicon particles as anode materials for lithium-ion batteries

In recent years, the calendar life of Si has been recognized as a significant issue that must be addressed prior to technology deployment: The carbon conductive additive is a potential source of parasitic side reactions. However, carbon remains essential due to the low electronic conductivity of Si. In this study, we investigate the use of Cu as a conductive additive and potential alternative to carbon. Some Cu-decorated silicon particles (Si Cu ) were prepared using physical vapor deposition (PVD) via sputtering and high-energy milling. Other Si Cu particles were prepared by using a solution method and examined briefly. The milling method caused Cu to appear as island-like features on the Si surface, whereas the PVD method initially produced similar island-like features that gradually developed into a continuous coating around the Si as sputtering time increased. Electrodes fabricated from Si Cu exhibited lower overall resistivity, demonstrating the beneficial effect of Cu in improving electronic percolation through the electrode. Electrochemical tests showed that the milled SiCu exhibited higher capacity retention, improved rate capability, and lower overpotential. Furthermore, Si Cu coupled with an NMC811 cathode exhibited lower leakage currents compared with the baseline silicon, indicating that incorporating Cu provided an additional advantage of minimizing parasitic currents in the cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Flash-X: A multiphysics simulation software instrument

Flash-X is a highly composable multiphysics software system that can be used to simulate physical phenomena in several scientific domains. It derives some of its solvers from FLASH, which was first released in 2000. Flash-X has a new framework that relies on abstractions and asynchronous communications for performance portability across a range of increasingly heterogeneous hardware platforms. Flash-X is meant primarily for solving Eulerian formulations of applications with compressible and/or incompressible reactive flows. It also has a built-in, versatile Lagrangian framework that can be used in many different ways, including implementing tracers, particle-in-cell simulations, and immersed boundary methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A direct recycling case study from a lithium-ion battery recall

Direct recycling of lithium-ion is a promising method for manufacturing sustainability. It is more efficient than classical methods because it recovers the functional cathode particle without decomposition into substituent elements or dissolution and precipitation of the whole particle. This case study of cathode-healing™ applied to a battery recall demonstrates an industrial model for recycling of lithium-ion, be it consumer electronic or electric vehicle (EV) batteries. The comprehensive process includes extraction of electrolyte with carbon dioxide, industrial shredding, electrode harvesting, froth flotation, cathode-healing™ and finally, building new cells with recycled cathode and anode. The final products demonstrated useful capability in the first full cells made from direct recycled cathodes and anodes from an industrial source. The lessons learned on recycling the prototypical chemistry are preliminarily applied to EV relevant chemistries.

Sloop, Steve↗
DOE OSTI2020

Transient Lattice Response upon Photoexcitation in CuInSe 2 Nanocrystals with Organic or Inorganic Surface Passivation

CuInSe 2 nanocrystals offer promise for optoelectronics including thin-film photovoltaics and printed electronics. Additive manufacturing methods such as photonic curing controllably sinter particles into quasi-continuous films and offer improved device performance. To gain understanding of nanocrystal response under such processing conditions, we investigate impacts of photoexcitation on colloidal nanocrystal lattices via time-resolved X-ray diffraction. We probe three sizes of particles and two capping ligands (oleylamine and inorganic S 2- ) to evaluate resultant crystal lattice temperature, phase stability, and thermal dissipation. Elevated fluences produce heating and loss of crystallinity, the onset of which exhibits particle size dependence. We find size-dependent recrystallization and cooling lifetimes ranging from 90 to 200 ps with additional slower cooling on the nanosecond time scale. Sulfide-capped nanocrystals show faster recrystallization and cooling compared to oleylamine-capped nanocrystals. Using these lifetimes, we find interfacial thermal conductivities from 3 to 28 MW/(m 2 K), demonstrating that ligand identity strongly influences thermal dissipation.

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

LoTToR: An Algorithm for Missing-Wedge Correction of the Low-Tilt Tomographic 3D Reconstruction of a Single-Molecule Structure

A single-molecule three-dimensional (3D) structure is essential for understanding the thermal vibrations and dynamics as well as the conformational changes during the chemical reaction of macromolecules. Individual-particle electron tomography (IPET) is an approach for obtaining a snap-shot 3D structure of an individual macromolecule particle by aligning the tilt series of electron tomographic (ET) images of a targeted particle through a focused iterative 3D reconstruction method. The method can reduce the influence on the 3D reconstruction from large-scale image distortion and deformation. Due to the mechanical tilt limitation, 3D reconstruction often contains missing-wedge artifacts, presented as elongation and an anisotropic resolution. Here, we report a post-processing method to correct the missing-wedge artifact. This low-tilt tomographic reconstruction (LoTToR) method contains a model-free iteration process under a set of constraints in real and reciprocal spaces. A proof of concept is conducted by using the LoTToR on a phantom, i.e., a simulated 3D reconstruction from a low-tilt series of images, including that within a tilt range of ±15°. The method is validated by using both negative-staining (NS) and cryo-electron tomography (cryo-ET) experimental data. A significantly reduced missing-wedge artifact verifies the capability of LoTToR, suggesting a new tool to support the future study of macromolecular dynamics, fluctuation and chemical activity from the viewpoint of single-molecule 3D structure determination.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Symmetry-specific characterization of bond orientation order in DNA-assembled nanoparticle lattices

Bond-orientational order in DNA-assembled nanoparticles lattices is explored with the help of recently introduced Symmetry-specific Bond Order Parameters (SymBOPs). Here, this approach provides a more sensitive analysis of local order than traditional scalar BOPs, facilitating the identification of coherent domains at the single bond level. The present study expands the method initially developed for assemblies of anisotropic particles to the isotropic ones or cases where particle orientation information is unavailable. The SymBOP analysis was applied to experiments on DNA-frame-based assembly of nanoparticle lattices. It proved highly sensitive in identifying coherent crystalline domains with different orientations, as well as detecting topological defects, such as dislocations. Furthermore, the analysis distinguishes individual sublattices within a single crystalline domain, such as pair of interpenetrating FCC lattices within a cubic diamond. The results underscore the versatility and robustness of SymBOPs in characterizing ordering phenomena, making them valuable tools for investigating structural properties in various systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Eulerian simulation of complex suspensions and biolocomotion in three dimensions

Significance Fluid–structure interactions are ubiquitous in many natural and man-made environments. They are difficult to study analytically, and therefore accurate and flexible computational methods are an indispensable tool in the field. Typically, fluids are simulated with a fixed background computational mesh, whereas a solid is simulated with a mesh that moves with it, making it challenging to couple the two. Here we develop a three-dimensional computational method where both fluid and solid can be represented on a fixed computational grid, which simplifies the coupling between the two phases considerably. Our method can simulate scenarios that are difficult to do with other methods, such as complex suspensions containing soft particles that are both heavier and lighter than the fluid.

3D fluid-structure interaction↗
DOE OSTI2025

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model

Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These properties can be useful for defining relevant signatures for probabilistically identifying the different types of events that occurred, based on the soot that is produced from these events. However, it is rare to observe samples of soot from a detonation or combustion that are not contaminated by outside particles. In this paper, we present a method for resolving mixtures of soot to determine the contributions of sources that may be present in samples of recovered soot. We use Latent Dirichlet Allocation to describe the generative process for a sample of recovered soot, and use Variational Bayesian Inference to learn about the parameters associated with the generative model. We demonstrate the utility of this method by considering real samples of mixtures of soot under various frameworks to show that the model is able to identify the different components present in a sample of soot as well as their mixing proportions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High-energy ballistic electrons in low-pressure radio-frequency plasmas

This study demonstrates the presence of a small number of high-energy ballistic electrons (HEBEs) that originate from secondary electrons in low-pressure radio-frequency (rf) plasmas. The kinetic behaviors of the HEBEs are illustrated through electron energy probability functions from the fully kinetic particle-in-cell simulations, showing two wavy high-energy tails and two bifurcations during one rf cycle. Test-particle simulations and a semi-analytical method associated with nonlocal electron kinetics are performed to characterize the HEBE trajectories, which reveal the ballistic nature of the HEBEs and their typical bouncing features between the rf sheaths. Parameter dependence of the HEBEs on the discharge conditions (e.g., gas pressure, gap distance, and rf frequency) are identified, which is relevant to the plasma collisionality. With a pronounced presence of HEBEs, the overall impacts of the secondary electron emission on discharge parameters, such as electron power absorption and ionization rate, are also illustrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Autodifferentiable Ensemble Kalman Filters

Data assimilation is concerned with sequentially estimating a temporally evolving state. This task, which arises in a wide range of scientific and engineering applications, is particularly challenging when the state is high-dimensional and the state-space dynamics are unknown. This paper introduces a machine learning framework for learning dynamical systems in data assimilation. Here, our auto-differentiable ensemble Kalman filters (AD-EnKFs) blend ensemble Kalman filters for state recovery with machine learning tools for learning the dynamics. In doing so, AD-EnKFs leverage the ability of ensemble Kalman filters to scale to high-dimensional states and the power of automatic differentiation to train high-dimensional surrogate models for the dynamics. Numerical results using the Lorenz-96 model show that AD-EnKFs outperform existing methods that use expectation-maximization or particle filters to merge data assimilation and machine learning. In addition, AD-EnKFs are easy to implement and require minimal tuning.

autodifferentiation↗
DOE OSTI2024

Multiscale approaches for optimizing the impact of strain on Na-ion battery cycle life

Abstract The high costs and geopolitical challenges inherent to the lithium-ion (Li-ion) battery supply chain have driven a rising interest in the development of sodium-ion (Na-ion) batteries as a potential alternative. Unfortunately, the larger ionic radius of Na limits the reversibility of cycling because of the extensive atomic rearrangements that accompany Na-ion insertion, which in turn limit diffusion and charging speed, and lead to rapid degradation of the electrodes. The Center for Strain Optimization for Renewable Energy (STORE) was established to address these challenges and develop new electrode materials for Na-ion cells. This article discusses the current state-of-the-art materials used in Na-ion cells and several directions that STORE believes are critical to understand and control the structural and volumetric changes during the reversible (de)insertion of large cations. Graphical abstract Highlights Understanding the fundamental way materials respond to localized strains at the atomic length-scale is a critical first step in the development of highly reversible, long cycle life, Na-ion insertion hosts. This perspective explores a variety of methods that can be employed to mitigate the detrimental effects of large strain. The insights gained from these investigations should help lay the foundation for the creation of more economical and sustainable batteries that could have immediate impact on global energy infrastructure. Discussion Although there is near universal agreement that electrochemical energy storage must be an integral part of a green-energy future, there is less agreement about how to reduce the cost of energy storage. Replacing high-cost lithium-ion cells with lower-cost sodium-ion batteries is one option frequently considered in future energy models, but the details of what can be achieve with optimized sodium cell performance remains unclear. Here we posit that developing methods to mitigating strain on the electrode particle length scale is a key factor for achieving long-cycle-life sodium-ion batteries. Mitigating strain on the atomic scale suppress electrode-level volume change. Allowing for fast cycling in materials without the problems of electrode cracking or delamination. We further posit that understanding volume change in sodium-ion electrodes at a fundamental level will lead to the designing new sodium-ion electrode materials that will allow for efficient, stable, lower-cost energy storage.

Brady, Michael J.↗
DOE OSTI2025

High-dimensional maximum-entropy phase space tomography

Reconstructing 4D or 6D phase space distributions from 1D or 2D measurements is a challenging inverse problem encountered in particle accelerators. Entropy maximization is an established method to incorporate prior information in the reconstruction, but it is typically infeasible in high-dimensional spaces. In this paper, I review two recent approaches to high-dimensional entropy maximization. The first approach utilizes differentiable simulations and a class of generative models known as normalizing flows, whereas the second approach employs the method of Lagrange multipliers and Markov Chain Monte Carlo (MCMC) sampling. My aim is to provide a short explanation of each method using a common notation. I conclude by mentioning several unsolved problems in phase space tomography.

Hoover, Austin [ORNL] (ORCID:0000000153136962)↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2023

X-Ray Absorption Spectroscopy (XAS): Surface Structural Determination of Alloy Nanoparticles

In catalysis since reactions occur on the surface of nanoparticles (NP), it is essential to determine the composition of this structure, rather than that of the nanoparticles, since the two may be, and often are, different. Conventional techniques including X-ray absorption spectroscopy (XAS) and X-ray diffraction (XRD) are powerful techniques, but these data reflect the average composition of the entire particle. In this chapter, we introduce the method of EXAFS analysis, which isolates the surface atoms of nanoparticles based on its sensitivity to chemical reactions, specifically surface oxidation. As shown in our case studies, if the surface of a Pt-based nanoparticle is contacted by air at room temperature, the surface will selectively oxidize, resulting in the loss of Pt-Pt and Pt-M bonds to the formation of Pt-O bonds. The difference between the completely reduced and surface-oxidized nanoparticle allows for the isolation of signal from the catalytic surface. Although these examples highlight Pt alloys, similar analysis is also possible for other group 8 and IB bimetallic catalysts.

Zhang, Guanghui↗
DOE OSTI2023

System and method for high dynamic range waveform digitization

Diverse applications from particle physics experiments to lidar are driving cost and current reduction in giga-hertz sampling rate high-resolution data conversion. Multiple imagers captures a single pixel of data and require processing at very high speed. High-bandwidth high-rate signal sampling, analog-to-digital conversion, and transfer of large amounts of data to a digital data acquisition block are required in such systems. Dynamic range, power consumption, and transfer of high-speed, high-bit width data are key implementation challenges. Data acquisition architectures optimized for specific requirements of such systems may facilitate system implementation and reduce overall system cost.

Mostafanezhad, Isar↗