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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A decay database of coincident γ–γ and γ–X -ray branching ratios for in-field spectroscopy applications

Current fieldable spectroscopy techniques often use single detector systems heavily impacted by interferences from intense background radiation fields. These effects result in low-confidence measurements that can lead to misinterpretation of the collected spectrum. To help improve interpretation of the fission products and short-lived radionuclides produced in a composite sample, a coincidence-database is being developed in support of a robust portable and X-ray coincidence detector system concurrently under development at the Pacific Northwest National Laboratory for in-field deployment. Hitherto, no database exists containing coincident γ–γ and γ–X-ray branching-ratio intensities on an absolute scale that will greatly enhance isotopic identification for in-field applications. As part of this project, software has been developed to parse all radioactive-decay data sets from the Evaluated Nuclear Structure Data File (ENSDF) archive to enable translation into a more useful JavaScript Object Notation (JSON) formats that more readily supports query-based data manipulation. The coincident database described in this work is the first of its kind and contains coincidence γ–γ and γ–X-ray intensities and their corresponding uncertainties, together with auxiliary metadata associated with each decay data set. The new JSON format provides a convenient and portable means of data storage that can be imported into analysis frameworks with relatively low overhead allowing for meaningful comparison with measured data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Intrinsic glassy-metallic transport in an amorphous coordination polymer

Conducting organic materials, such as doped organic polymers, molecular conductors and emerging coordination polymers4, underpin technologies ranging from displays to flexible electronics. Realizing high electrical conductivity in traditionally insulating organic materials necessitates tuning their electronic structure through chemical doping6. Furthermore, even organic materials that are intrinsically conductive, such as single-component molecular conductors, require crystallinity for metallic behavior. However, conducting polymers are often amorphous to aid durability and processability. Using molecular design to produce high conductivity in undoped amorphous materials would enable tunable and robust conductivity in many applications, but there are no intrinsically conducting organic materials that maintain high conductivity when disordered. Here we report an amorphous coordination polymer, Ni tetrathiafulvalene tetrathiolate, which displays markedly high electronic conductivity (up to 1,200 S cm –1 ) and intrinsic glassy-metallic behavior. Theory shows that these properties are enabled by molecular overlap that is robust to structural perturbations. This unusual set of features results in high conductivity that is stable to humid air for weeks, pH 0–14 and temperatures up to 140 °C. Furthermore, these findings demonstrate that molecular design can enable metallic conductivity even in heavily disordered materials, raising fundamental questions about how metallic transport can exist without periodic structure and indicating exciting new applications for these materials.

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

QuantumScents: Quantum-Mechanical Properties for 3.5k Olfactory Molecules

Quantitative structure–odor relationships are critically important for studies related to the function of olfaction. Current literature data sets contain expert-labeled molecules but lack feature data. This paper introduces QuantumScents, a quantum mechanics augmented derivative of the Leffingwell data set. QuantumScents contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with chemprop (Heid, E.; et al. ChemRxiv, 2023, DOI: 10.26434/chemrxiv-2023-3zcfl) to predict scent labels. Finally, the QuantumScents data set is freely available on Zenodo along with the authors’ code, example models, and data set generation workflow (https://zenodo.org/doi/10.5281/zenodo.8239853).

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

Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein–ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA–small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand–protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein–target docking illustrates a need for further methods refinement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural Characterization and Ligand-Induced Conformational Changes of SenB, a Se-Glycosyltransferase Involved in Selenoneine Biosynthesis

Selenium (Se) is an essential micronutrient that is found naturally in proteins, nucleic acids, and natural products. Unlike selenoproteins and selenonucleic acids, little is known about the structures of biosynthetic enzymes that incorporate Se into small molecules. Here, we report the X-ray crystal structure of SenB, the first known Se-glycosyltransferase that was recently found to be involved in the biosynthesis of the Se-containing metabolite selenoneine. SenB catalyzes C–Se bond formation using selenophosphate and an activated uridine diphosphate sugar as a Se and glycosyl donor, respectively, making it the first known selenosugar synthase and one of only four bona fide C–Se bond-forming enzymes discovered to date. Our crystal structure, determined to 2.25 Å resolution, reveals that SenB is a type B glycosyltransferase, displaying the prototypical fold with two globular Rossmann-like domains and a catalytic interdomain cleft. By employing complementary structural biology techniques, we find that SenB undergoes both local and global substrate-induced conformational changes, demonstrating a significant increase in α-helicity and a transition to a more compact conformation. Our results provide the first structure of SenB and set the stage for further biochemical characterization in the future.

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

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗
DOE OSTI2024

Automated analysis of lattice structures using computed tomography

Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Schiefelbein, Bryan E.↗
DOE OSTI2020

Computational study of TATB

We have performed a computational study of TATB. The study is composed of two parts, one where we perform static T = 0 K calculations with several different DFT functionals, to investigate structural properties and obtain a cold curve. Even though the functionals used in this study give poor results at ambient pressure, they perform much better at compression, when the dominance of the van der Waals’ forces in the binding is replaced by more normal interactions. In particular AM05 gives good results, mainly because it doesn’t include any “faulty” van der Waals’. Indeed the van der Waals’ corrected functional (PBE+D2) results shows the worst performance on structural properties. For the cold curve the PBE+D2 functional gives the best results compared to experiments (room temperature) but since the structural properties were bad with this functional we cannot for sure say that this is in any way a validation. It is shown that scaling the AM05 results with a common factor reproduces the experimental data. We leave the question on why to a follow up study but speculate that maybe the experiment and our calculations do not represent the same system set-up. From the structural properties we identify a site in the TATB lattice that contains a high concentration of oxygen and hydrogen. The consequences of this finding, if any, are left to a future project. In the second part of the study we perform extensive high-quality DFT-MD (molecular dynamics with DFT forces) calculations to obtain points on the Hugoniot. This part of the study did not get finished within this project and a more extensive summary of this part will be given in a follow up report at a future date. However, we show that obtaining calculated data is essential for equations of state development since the existing experimental Hugoniot data barely deviates from room temperature isotherm data and thus give no information of the high temperature dependency of materials properties. Our conclusion so far in the project is that being able to calculate materials properties is essential for high explosives since experiments are unable to give information in the high temperature part of phase space. While blind use of DFT cannot (yet) give unambiguous answers, we can still explore qualitatively and use such information to guide us in EOS development. However, the main conclusion is that we need better functionals in DFT to easily extract needed properties from calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fate of Internal Waves on a Shallow Shelf

Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a distributed temperature sensing (DTS) system that tracked the transformation of internal waves from the shelf break to the surf zone over a narrow shelf slope region in the South China Sea. The spatially continuous view of temperature fields provides a perspective of physical processes commonly available only in laboratory settings or numerical models, including internal wave reflection off a natural slope, shoreward transport of dense fluid within trapped cores, and observations of internal rundown (near-bed, offshore-directed jets of water preceding a breaking internal wave). Analysis shows that the fate of internal waves on this shelf—whether transmitted into shallow waters or reflected back offshore—is mediated by local water column density structure and background currents set by the previous shoaling internal waves, highlighting the importance of wave-wave interactions in nearshore internal wave dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Planar Defect Layers Template a High-Pressure InBi Polymorph

The short- and long-range order of III–V materials under high pressure has long been the subject of debate, with advancements in structural characterization leading to significant revisions to the accepted structural models. Despite these revisions, previous high-pressure structural assignments in the In–Bi system include the site-disordered β-Sn structure type, a structure type demonstrated to be nonexistent in analogous III–V systems. While X-ray diffraction is consistent with site disordering in InBi at high pressure, cluster expansion calculations indicate that disordering requires temperatures above 3000 K. Here, we propose InBi as a model material for studying unique high-pressure planar defects due to its highly anisotropic stress-dependent properties and structure. Specifically, we identify two sets of planar defects that mimic the diffraction pattern of a site disordered β-Sn structure type and are compatible with the calculated disorder barrier. We derive these defects by symmetry relations over crystallographic transitions. Density functional theory calculations of the proposed defects suggest that these defects are stabilized by diminishing interlayer separations with pressure. Further, we find that one of the proposed defects closely resembles a bulk high-pressure phase of InBi, InBi-ϵ, and we assert that the proposed defects order upon heating, acting as a template for InBi-ϵ growth. The proposed defects and their electronic structure provide a basis for the trend of superconducting critical temperature with increasing pressure. These methods for identifying defects are generalizable to other materials with reports of site disorder at high pressure, prompting a broader search for related high-pressure defects.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Socio-Structural and Neighborhood Predictors of Incident Criminal Justice Involvement in a Population-Based Cohort of Young Black MSM and Transgender Women

Black men who have sex with men (MSM) and transgender women are disproportionately affected by criminal justice involvement (CJI) and HIV. This study recruited 618 young Black MSM and transgender women in Chicago, IL, using respondent-driven sampling between 2013 and 2014. Random effects logistic regression evaluated predictors of incident CJI over 18 months of follow-up. We report, controlling for respondent age, gender and sexual identity, spirituality (aOR 0.56, 95% CI 0.33-0.96), and presence of a mother figure (aOR 0.41, 95% CI 0.19-0.89) were protective against CJI. Economic hardship (financial or residential instability vs. neither aOR 2.23, 95% CI 1.10-4.51), two or more past episodes of CJI vs. none (aOR 2.66, 95% CI 1.40-5.66), and substance use (marijuana use vs. none aOR 2.79, 95% CI 1.23-6.34; other drug use vs. none aOR 4.49, 95% CI 1.66-12.16) were associated with CJI during follow-up. Research to identify and leverage resilience factors that can buffer the effects of socioeconomic marginalization may increase the effectiveness of interventions to address the socio-structural factors that increase the risk for CJI among Black MSM and transgender women. Given the intersection of incarceration, HIV and other STIs, and socio-structural stressors, criminal justice settings are important venues for interventions to reduce health inequities in these populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

NNL.Fe.qSNAP-ZBL.2024.1: A Fe Spectral Neighbor Analysis Potential for Radiation Damage Simulations

The NNL.Fe.qSNAP-ZBL.2024.1 machine-learned potential (MLP) has been generated to support the development of an elemental body-centered cubic (BCC) Fe athermal recombination corrected neutron damage model and simulations of primary recoil atom (PRA) cascades in BCC Fe. This MLP is a quadratic spectral neighbor analysis potential (qSNAP) hybridized with the universal Ziegler-Beirsack-Littmark (ZBL) potential at short-range and is named according to Naval Nuclear Laboratory MLP naming conventions (NNL.material-system.MLP-type.year.version). Training set calculations for Fe are presented along with the subsequent MLP fitting procedure. A key criterion of the fitting procedure is that ZBL describes the short-range interaction with minimal impact on the MLP. The MLP is compared to density functional theory (DFT) predicted properties relevant to radiation damage simulation, including threshold displacement energies, for validation. The NNL.Fe.qSNAP-ZBL.2024.1 potential is considered suitable for molecular dynamics (MD) simulations of radiation defects up to 800 K and PRA cascades in BCC Fe up to around 10 keV. The potential can additionally be used on a limited basis for recoils of 10–20 keV, within which range the emergence of structures outside the training set in cascade simulations may cause system instabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Eismann, Stephan↗
DOE OSTI2023

LANSCE CCL Performance Limits

This report summarizes the performance limits of the LANSCE Coupled-Cavity Linac (CCL). These results are captured or summarized directly from the references cited. This report was written in support of the LANSCE Modernization Project (LAMP). Many factors contribute to the performance limits of the CCL accelerator system. One performance limit is set by the CCL mechanical structure and available cooling. Heating of the structure is ultimately linked to the operating RF duty factor of the CCL. Another performance limit is set by the maximum RF and beam duty factors that can be supported by the 805-MHz klystrons and associated HVDC power supplies. The RF and HVDC systems also set limits for the maximum peak beam current that can be accelerated in the CCL. And finally, a maximum beam current limit is set by the beam dynamics determined by the details of the CCL physics design (number of cells per CCL tank, accelerating gradients, magnetic focusing lattice, beam losses, etc.). This limit can be informed by both simulation results and beam measurement data, if available. A detailed explanation of each performance limit is given in the sections below. The results are summarized in the table below. In all cases, it is assumed that the CCL and the RF system are operating in their nominal beam production configuration – nominal magnet set points and nominal cavity fields, unless otherwise specified.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Electronic structure of NdO via slow photoelectron velocity-map imaging spectroscopy of NdO –

Electronically excited NdO is a possible product of the chemistry associated with the release of Nd into the ionosphere, and emission from these states may contribute to the observations following such experiments. To better characterize the energetics and spectroscopy of NdO, we report a combined experimental and theoretical study using slow photoelectron velocity-map imaging spectroscopy of cryogenically cooled NdO – anions (cryo-SEVI) supplemented by wave function-based quantum-chemical calculations. Using cryo-SEVI, we measure the electron affinity of NdO to be 1.0091(7) eV and resolve numerous transitions to low-lying electronic and vibrational states of NdO that are assigned with the aid of the electronic structure calculations. Additionally, temperature-dependent data suggest contributions from the (2)4.5 state of NdO – residing 2350 cm –1 above the ground anion state. Finally, photodetachment to higher-lying excited states of NdO is also reported, which may help to clarify observations from prior release experiments.

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

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Development of a Laser Ultrasonics-based Approach for Rapid Screening of High Entropy Alloys

This project utilized a laser ultrasonic technique to systematically study temperature-induced evolution of material properties in a set of interrelated binary alloys and a high entropy alloy (HEA) fabricated using arc-melting and spark plasma sintering processes. This technique involved the use of a nanosecond duration, high-intensity pulsed laser to thermo-elastically generate ultrasonic waves that propagate in the bulk of the metal alloy. Sub-nanometer-scale displacements associated with the propagating bulk ultrasonic waves were detected along the epicenter on the opposite surface of the sample using a 1 GHz bandwidth photorefractive interferometer. Phase transformations and microstructural changes were inferred from the temperature-dependent trends in the bulk acoustic velocities and features in the ultrasonic epicentral waveforms measured in the binary alloys. These inferences were then correlated with electron/optical microscopy observations and predictions using calculations of phase diagrams (CALPHAD). The results showed that the laser-generated ultrasonic pulses were strongly influenced by changes in material microstructure and could accurately track thermally driven phase transformations and detect the presence of microscale heterogeneities (grain boundaries, dendritic structures, etc.) in the set of binary alloy samples. The measurement approach was then applied to a quinary HEA sample for estimating phase transition temperature and determining microstructural heterogeneity. The rapid, non-contact and non-destructive ultrasonic testing approach demonstrated here is amenable to high throughput combinatorial investigations that can be applied to graded composition HEAs produced using advanced manufacturing methods. When paired with atomistic simulations and CALPHAD modeling, this approach can overcome the bottlenecks faced by current material characterization methods in efficiently screening the vast discovery space of HEAs that spans over a hundred million unique quinary alloy compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A diagrammatic approach for automatically deriving analytical gradients of tensor hyper-contracted electronic structure methods

In this work, we introduce a diagrammatic approach to facilitate the automatic derivation of analytical nuclear gradients for tensor hyper-contraction (THC) based electronic structure methods. The automatically derived gradients are guaranteed to have the same scaling in terms of both operation count and memory footprint as the underlying energy calculations, and the computation of a gradient is roughly three times as costly as the underlying energy. The new diagrammatic approach enables the first cubic scaling implementation of nuclear derivatives for THC tensors fitted in molecular orbital basis (MO-THC). Furthermore, application of this new approach to THC-MP2 analytical gradients leads to an implementation, which is at least four times faster than the previously reported, manually derived implementation. Finally, we apply the new approach to the 14 tensor contraction patterns appearing in the supporting subspace formulation of multireference perturbation theory, laying the foundation for developments of analytical nuclear gradients and nonadiabatic coupling vectors for multi-state CASPT2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗