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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Identification and correction of temporal and spatial distortions in scanning transmission electron microscopy

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. Here, the analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Suppressing Molecular Charging, Nanochemistry, and Optical Rectification in the Tip-Enhanced Raman Geometry

Classical vs quantum plasmons are operative in tapping vs contact mode tip-enhanced Raman scattering (TERS) and lead to distinct observables. Under otherwise identical experimental conditions, we illustrate the concept through tapping and contact mode TERS mapping of chemically functionalized silver nanocubes. Whereas molecular charging, chemical transformations, and optical rectification are prominent observables in contact mode TERS, the same effects are suppressed using tapping mode feedback. In effect, this work demonstrates that nanoscale physical and chemical processes can be accessed/suppressed on-demand in the TERS geometry. Further, the advantages of tapping mode TERS are otherwise highlighted with the latter in mind.

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

Investigation of the end-gas autoignition process in natural gas engines and evaluation of the methane number index

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the wide range of chemical reactivity in pipeline quality NG, represented by the Methane Number (MN) (65< MN<95). Understanding the underlying phenomena responsible for engine knock can support the development of predictive tools capable of identifying knock onset/intensity as well as a fuel’s propensity to knock, allowing engine manufacturers to expand the knock envelope and design more efficient/robust SI NG engines. Additionally, there is an opportunity for increased efficiency by controlling levels of end-gas autoignition if this can be predicted and controlled. This work focuses on the development of a novel methodology to understand/predict a fuel’s propensity to knock. This methodology is based on the charge fraction undergoing autoignition, namely fractional end-gas autoignition (F-EGAI), and was developed based on first order laminar flame speeds and ignition delay analysis combined with a 0-D homogeneous batch reactor model. This methodology proved to be suitable to predict a fuel’s propensity to knock, even under conditions when light knock was observed. The simple modeling approach was used to explain the results from a series of MN tests with multiple NG compositions exhibiting a wide range of reactivity compositions and providing insight on why fuels of very different chemical compositions can have the same MN. Finally, a CFD model was developed was used to confirm the methodology capability and provide further insights in the physical and chemical phenomena behind end gas autoignition.

42 ENGINEERING↗
DOE OSTI2020

Dissociative electron attachment to amide bond containing molecules: N-ethylformamide and N-ethylacetamide

To advance our quest to understand the role of low energy electrons in biomolecular systems, we performed investigations on dissociative electron attachment (DEA) to gas-phase N-ethylformamide (NEF) and N-ethylacetamide (NEA) molecules. Both molecules contain the amide bond, which is the linkage between two consecutive amino acid residues in proteins. Thus, their electron-induced dissociation can imitate the resonant behavior of the DEA process in more complex biostructures. Our experimental results indicate that in these two molecules, the dissociation of the amide bond results in a double resonant structure with peaks at ~5 eV and 9 eV. We also determined the energy position of resonant states for several negative ions, i.e., the other dissociation products from NEF and NEA. Our predictions of dissociation channels were supported by density functional theory calculations of the corresponding threshold energies. Our results and those previously reported for small amides and peptides imply the fundamental nature for breakage of the amide bond through the DEA process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Revisiting W–ZrC interfaces: A first principles study

We investigate W–ZrC interfaces using first-principles calculations based on the density functional theory. There have been theoretical and experimental studies exploring W–ZrC interfaces, however, the debate regarding the most stable interface continues to persist. In this study, we systematically simulated various W–ZrC interfaces merging W and ZrC surfaces with different orientations. Subsequently, we evaluated their stabilities and explained the corresponding stabilities in terms of the nature of bonding and charge-transfer processes at the interface. We find ZrC(111)–W(110) is the most stable interface with higher adhesive energy than the other interfaces. The additional stability associated with the ZrC(111)–W(110) results from significant interface reconstruction. Three layers of W and ZrC adjacent to the interface are involved in the charge-transfer process leading to stronger ionic bonds in ZrC(111)–W(110) as compared to the other potential candidate: ZrC(100)–W(100). The C and W atoms are found to be displaced from their symmetric position during the reconstruction process at the interface to facilitate stronger bonds with shorter W–C and W–Zr bonds in ZrC(111)–W(110) as compared to ZrC(100)–W(100). This leads to stronger covalent bonds in ZrC(111)–W(110) than that in ZrC(100)–W(100). Therefore, we conclude that the stronger covalent and ionic forces in ZrC(111)–W(110) than those in ZrC(100)–W(100) are responsible for making ZrC(111)–W(110) to be the most stable interface. In conclusion, this study addresses the long-standing question of the most stable W–ZrC interface and derives a number of implications for other W-transition metal carbide interfaces which are potential candidates for improving the mechanical properties of plasma facing materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu–Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Atom Probe Tomography for understanding microstructural and chemical changes in nuclear materials

Atom Probe Tomography (APT) is routinely employed to determine 3D microstructure and chemistry changes in structural materials used in reactor to understand irradiation effect. These findings help to correlate changes to mechanical and physical properties of nuclear materials which are subjected to extreme reactor conditions. With safety and economics of a reactor in mind, it is essential to be able to predict the changes in the fuel composition and microstructure as a function of burnup. Also, there is current need to develop advanced fuels with improved properties and performance which requires the experimental data on changes in microstructure, physical and chemical properties after irradiation for feedback process. A novel approach is presented to determine the local burnup in irradiated fuels using isotopic quantification obtained by Atom Probe Tomography (APT). Considering the volume of sample used (<100?µm3) for APT experiments using the lift-out process in a scanning electron microscope equipped with a Focused Ion Beam (FIB), the presented method determines the local burnup from a nuclear fuel, where a minimal amount of waste is produced. In this work, three samples were analyzed with different burnup conditions to quantify the isotopes of 235U, 236U, and 238U for burnup calculation in the irradiated metallic U–7Mo dispersion fuel. The calculated values were found in excellent agreement with MCNP calculations. With regard to structural materials, one of the primary factors limiting the utilization of fuel rods of Zr alloys is hydrogen pick up during oxidation. Past experimental studies have shown that the hydrogen pickup in Zirconium alloys is influenced by the alloy composition as well by irradiation temperature and neutron dose. Oftentimes, it is not feasible to carry out comprehensive studies on understanding the microstructural evolution in neutron irradiated materials since in reactor irradiation programs are extremely costly, lengthy and logistically complicated. The use of ion irradiation as a substitute has grown in recent years for several reasons including less radioactivity, faster irradiations and lower costs. In the framework of the MUZIC 3 project, samples of Zircaloy-4 have been proton and Zr2+ irradiated at the Michigan Ion Beam Lab (MIBL) facility with aim to create a microstructure that is similar to that observed after in-reactor service. The temperature of the environment is adjusted to compensate for the higher dose rate, as has been done previously. As a first step in this project the irradiated microstructures have been characterized using Transmission Electron Microscopy and Atom Probe Tomography to elucidate the microstructural changes, including precipitate amorphization and dissolution and the formation of dislocation loops. This presentation plans to highlight initial finding of this project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Novel inverse oxide/metal catalysts for methanol synthesis: impact of oxide–metal interactions and reversible morphological changes

Inverse oxide/metal catalysts have proved to be excellent systems for the generation of methanol by CO 2 hydrogenation or the partial oxidation of methane. These systems can exhibit unique structural and chemical properties due to the nano size of the oxide component and strong oxide–metal interactions. Recent studies for ZnO/Cu, CeO 2 /Cu, TiO 2/ Cu, MgO/Cu, In 2 O 3 /Cu, and In 2 O 3 /Au catalysts have shown large variations in the composition and morphology of the oxide overlayer as a function of temperature and chemical environment. These oxide–metal interfaces are able to react with CO 2 and CH 4 at room temperature, and both reactants have a strong influence on the physical and chemical properties of the catalysts. Under reaction conditions, switches between oxide–metal and metal–metal interfaces can take in the catalyst surface. A dynamic behavior that can be linked to a high selectivity for methanol production over systems such as ZnO/Cu, CeO 2 /Cu and In 2 O 3 /Au. Finally, this type of correlation deserves additional systematic studies since it could be a powerful tool for designing highly efficient catalysts for methanol synthesis.

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

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. In conclusion, our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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

Quantitative Low-Field 19 F Nuclear Magnetic Resonance Analysis of Carbonyl Groups in Pyrolysis Oils

Pyrolysis bio-oils, one of the products of lignocellulosic biomass pyrolysis, have the potential to be widely used as fuels. The chemical composition of bio-oils is very complicated as they contain hundreds, if not thousands, of different, mostly oxygen-containing, compounds with a wide distribution of physical properties, chemical structures, and concentrations. Detailed knowledge of bio-oil composition is crucial for optimizing both the pyrolysis processes and for any subsequent upgrading into a more viable fuel resource. Here we report the successful use of low-field, or benchtop, nuclear magnetic resonance (NMR) spectrometers in the analysis of pyrolysis oils. Pyrolysis oils from four different feedstocks were derivatized and analyzed using 19 F NMR techniques. The NMR results compare favorably with titrations for total carbonyl content. In addition, the benchtop NMR spectrometer proves able to reveal key spectral features, thus allowing the quantification of different carbonyl groups, such as aldehydes, ketones and quinones. Benchtop NMR spectrometers are typically compact, cheaper than their superconducting counterparts and do not require cryogens. Their use will make NMR analysis of pyrolysis oils easier and more accessible to a wide range of different potential users.

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

Interpretable machine learning models classify minerals via spectroscopy

Developing methods to identify mineral species confidently and rapidly from Raman spectral analysis is critical to numerous fields. Traditionally, analysis relies on pattern matching the Raman spectrum of an unknown dataset with a supporting library of well-characterized spectral data, which may prove difficult for environmental samples that are poorly crystalline or phase mixtures. Here, we developed interpretable machine learning models that can classify uranium minerals by secondary oxyanion chemistry and other physicochemical properties based solely on Raman spectra. This new ML method produces a mineral profile of physical and chemical properties for an unknown sample and can rapidly classify or identify unknown minerals from Raman data, without the need for an exact pattern match in a spectral library. Training models are validated by 1. Strong correlation of high confidence model regions with published spectroscopic assignments and 2. Correct classification of a mineral not present in training data. Training data are from the Compendium of Uranium Raman and Infrared Experimental Spectra and available crystallographic information files within the open-source Smart Spectral Matching scientific framework. Physically meaningful classifier models can rapidly identify key structural and chemical information about unknown uranium minerals and the overall methodology is broadly applicable for mineral phases.

Machine learning↗
DOE OSTI2021

Sulfurized Polyacrylonitrile (SPAN): Changes in Mechanical Properties during Electrochemical Lithiation

Sulfurized polyacrylonitrile (SPAN) is a promising material for stable lithium-sulfur (Li-S) batteries that can potentially satisfy the demand for high-density energy storage devices for electric vehicles (EVs). However, important physical and chemical properties of the SPAN cathode material are not yet well understood. For example, the SPAN mechanical behavior that depends on the structure and chemistry of the material generated during synthesis, and the mechanical response evolution during battery discharge have been scarcely investigated. This work addresses the effects of electrochemical lithiation on the SPAN mechanical integrity via uniaxial tensile loading tests using molecular dynamics with the ReaxFF potential. Here, we evaluate the volume expansion, Young’s modulus, yield strength, and ultimate tensile strength with increasing lithium contents. Our results show how the degree of graphitization of the carbonized skeleton impacts the SPAN ability to withstand the volume expansion-induced structural stresses upon lithiation. We describe the fracture mechanisms and find out a ductility loss with increasing lithium contents

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

Multidimensional four-wave-mixing spectroscopy with squeezed light

A set of nonlinear spectroscopic measurements with quantum light is presented here. It involves a strong pump with frequency ω pu and a weak probe atω pr interacting with a solid state target to generate a conjugated beam ω c =2ω pu -ω pr via nondegenerate four-wave mixing. The $\chi^{(3)}$ susceptibility can be measured by the noise spectra of the intensity difference of the squeezed beams. We discuss three spectroscopic setups based on squeezed light: first, single four-wave mixing in a single crystal; second, cascading scheme involving two crystals; third, an $\textit{SU}$(1, 1) interferometer based on two separate four-wave mixing processes. We further investigate the microscopic noise and optical losses in all three setups. Simulations are presented for silicon-vacancy color centers in diamond.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Massively parallel GPU enabled third-order cluster perturbation excitation energies for cost-effective large scale excitation energy calculations

We present here a massively parallel implementation of the recently developed CPS(D-3) excitation energy model that is based on cluster perturbation theory. The new algorithm extends the one developed in Baudin et al. [J. Chem. Phys., 150, 134110 (2019)] to leverage multiple nodes and utilize graphical processing units for the acceleration of heavy tensor contractions. Furthermore, we show that the extended algorithm scales efficiently with increasing amounts of computational resources and that the developed code enables CPS(D-3) excitation energy calculations on large molecular systems with a low time-to-solution. More specifically, calculations on systems with over 100 atoms and 1000 basis functions are possible in a few hours of wall clock time. This establishes CPS(D-3) excitation energies as a computationally efficient alternative to those obtained from the coupled-cluster singles and doubles model.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗