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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

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

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. Here, we make the method available to the scientific community to speed biological research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dilute Paramagnetism and Non-Trivial Topology in Quasicrystal Approximant Fe4Al13

A very fundamental property of both weakly and strongly interacting materials is the nature of their magnetic response. In this work, we detail the growth of crystals of the quasicrystal approximant Fe4Al13 with an Al flux solvent method. We characterize our samples using electrical transport and heat capacity, yielding results consistent with a simple non-magnetic metal. However, magnetization measurements portray an extremely unusual response for a dilute paramagnet and do not exhibit the characteristic Curie behavior expected for a weakly interacting material at high temperature. Electronic structure calculations confirm metallic behavior but also indicate that each isolated band near the Fermi energy hosts non-trivial topologies, including strong, weak, and nodal components, with resultant topological surface states distinguishable from bulk states on the (001) surface. With half-filled flat bands apparent in the calculation, but an absence of long-range magnetic order, the unusual quasi-paramagnetic response suggests the dilute paramagnetic behavior in this quasicrystal approximant is surprising and may serve as a test of the fundamental assumptions that are taken for granted for the magnetic response of weakly interacting systems.

Avers, Keenan E. (ORCID:0000000223441939)↗
NASA NTRS1977

Effect of temperature on the electronic instability and the crystalline phase change at low temperature of V3Si type compounds

Equations assuming a Jahn-Teller type effect for the d band electrons in V3Si compounds are given, and the results of free-energy change calculations by using some approximations based on these equations are depicted. The tetragonal structure is converted to cubic as the temperature rises past T sub m which is calculated as 13 K. by the Batterman-Barrett method and is measured to be 20-5 K. Other parameters such as change of C sub p with temperature are predicted better.

Labbe, J.↗
NASA NTRS1994

Hot accretion disks with pairs: Effects of magnetic field and thermal cyclocsynchrotron radiation

We show the effects of thermal cyclosynchrotron radiation and magnetic viscosity on the structure of hot, two-temperature accretion disks. Magnetic field, B, is assumed to be randomly oriented and the ratio of magnetic pressure to either gas pressure, alpha = P(sub mag)/P(sub gas), or the sum of the gas and radiation pressures, alpha = (P(sub mag)/P(sub gas) + P(sub rad)), is fixed. We find those effects do not change the qualitative properties of the disks, i.e., there are still two critical accretion rates related to production of e(sup +/-) pairs, (M dot)((sup U)(sub cr)) and (M dot)((sup L)(sub cr)), that affect the number of local and global disk solutions, as recently found by Bjoernsson and Svensson for the case with B = 0. However, a critical value of the alpha-viscosity parameter above which those critical accretion rates disappear becomes smaller than alpha(sub cr) = 1 found in the case of B = 0, for P(sub mag) = alpha(P(sub gas) + P(sub rad)). If P(sub mag) = alpha P(sub gas), on the other hand, alpha(sub cr) is still about unity. Moreover, when Comptonized cyclosynchrotron radiation dominates Comptonized bremsstrahlung, radiation from the disk obeys a power law with the energy spectral index of approximately 0.5, in a qualitative agreement with X-ray observations of active galactic nuclei (AGNS) and Galactic black hole candidates. We also extend the hot disk solutions for P(sub mag) = alpha(P(sub gas) + P(sub rad)) to the effectively optically thick region, where they merge with the standard cold disk solutions. We find that the mapping method by Bjoernsson and Svensson gives a good approximation to the disk structure in the hot region and show where it breaks in the transition region. Finally, we find a region in the disk parameter space with no solutions due to the inability of Coulomb heating to supply enough energy to electrons.

Kusunose, Masaaki↗
NASA NTRS1995

Superconductivity in Russia: Update and prospects

The research projects and new technological developments that have occured in Russia are highlighted in this document. Some of the research discussed includes: x-ray structure analysis of YBCO superconducting single crystals and accompanying phase transformations; the role of electron-electron interaction in High Temperature Superconductors (HTSC); the formation of Cooper pairs in crystals; the synthesis and research on a new family of superconductors based on complex copper and mercury oxides (HgBa2CuO4 + alpha and HgBa2CaCu2O6 + alpha); methods for the extraction of higher (up to C200) fullerenes and metalfullerenides has been developed; and process of production of Josephson junctions and development of SQUID's.

Ozhogin, V.↗
NASA NTRS2005

Improved Charge-Transfer Fluorescent Dyes

Improved charge-transfer fluorescent dyes have been developed for use as molecular probes. These dyes are based on benzofuran nuclei with attached phenyl groups substituted with, variously, electron donors, electron acceptors, or combinations of donors and acceptors. Optionally, these dyes could be incorporated as parts of polymer backbones or as pendant groups or attached to certain surfaces via self-assembly-based methods. These dyes exhibit high fluorescence quantum yields -- ranging from 0.2 to 0.98, depending upon solvents and chemical structures. The wavelengths, quantum yields, intensities, and lifetimes of the fluorescence emitted by these dyes vary with (and, hence, can be used as indicators of) the polarities of solvents in which they are dissolved: In solvents of increasing polarity, fluorescence spectra shift to longer wavelengths, fluorescence quantum yields decrease, and fluorescence lifetimes increase. The wavelengths, quantum yields, intensities, and lifetimes are also expected to be sensitive to viscosities and/or glass-transition temperatures. Some chemical species -- especially amines, amino acids, and metal ions -- quench the fluorescence of these dyes, with consequent reductions in intensities, quantum yields, and lifetimes. As a result, the dyes can be used to detect these species. Another useful characteristic of these dyes is a capability for both two-photon and one-photon absorption. Typically, these dyes absorb single photons in the ultraviolet region of the spectrum (wavelengths < 400 nm) and emit photons in the long-wavelength ultraviolet, visible, and, when dissolved in some solvents, near-infrared regions. In addition, these dyes can be excited by two-photon absorption at near-infrared wavelengths (600 to 800 nm) to produce fluorescence spectra identical to those obtained in response to excitation by single photons at half the corresponding wavelengths (300 to 400 nm). While many prior fluorescent dyes exhibit high quantum yields, solvent-polarity- dependent fluorescence behavior, susceptibility to quenching by certain chemical species, and/or two-photon fluorescence, none of them has the combination of all of these attributes. Because the present dyes do have all of these attributes, they have potential utility as molecular probes in a variety of applications. Examples include (1) monitoring curing and deterioration of polymers; (2) monitoring protein expression; (3) high-throughput screening of drugs; (4) monitoring such chemical species as glucose, amines, amino acids, and metal ions; and (5) photodynamic therapy of cancers and other diseases.

Meador, Michael↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.

quantum computing↗
DOE OSTI2023

Mosaic Cu I –Cu II –In III 2D Perovskites: Pressure–Dependence of the Intervalence Charge Transfer and a Mechanochemical Alloying Method

The perovskite (BA) 4 [Cu II (Cu I In III ) 0.5 ]Cl 8 (1 BA ; BA + =butylammonium) allows us to study the high-pressure structural, optical, and transport properties of a mixed-valence 2D perovskite. Compressing 1 BA reduces the onset energy of Cu I/II intervalence charge transfer from 1.2 eV at ambient pressure to 0.2 eV at 21 GPa. The electronic conductivity of 1 BA increases by 4 orders of magnitude upon compression to 20 GPa, when the activation energy for conduction decreases to 0.16 eV. In contrast, Cu II perovskites achieve similar conductivity at ≈50 GPa. The solution-state synthesis of these perovskites is complicated, with more undesirable side products likely from the precursor mixtures containing three different metal ions. Finally, to circumvent this problem, we demonstrate an efficient mechanochemical synthesis to expand this family of halide perovskites with complex composition by simply pulverizing together powders of 2D Cu II single perovskites and Cu I In III double perovskites.

2D perovskite↗
DOE OSTI2024

Redox Gating for Colossal Carrier Modulation and Unique Phase Control

Redox gating, a novel approach distinct from conventional electrolyte gating, combines reversible redox functionalities with common ionic electrolyte moieties to engineer charge transport, enabling power-efficient electronic phase control. This study achieves a colossal sheet carrier density modulation beyond 10 16 cm -2 , sustainable over thousands of cycles, all within the sub-volt regime for functional oxide thin films. The key advantage of this method lies in the controlled injection of a large quantity of carriers from the electrolyte into the channel material without the deleterious effects associated with traditional electrolyte gating processes such as the production of ionic defects or intercalated species. The redox gating approach offers a simple and practical means of decoupling electrical and structural phase transitions, enabling the isostructural metal-insulator transition and improved device endurance. The versatility of redox gating extends across multiple materials, irrespective of their crystallinity, crystallographic orientation, or carrier type (n- or p-type). This inclusivity encompasses functional heterostructures and low-dimensional quantum materials composed of sustainable elements, highlighting the broad applicability and potential of the technique in electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Combining Spatial and Temporal Resolution in Cryo-TEM of Device Materials: Preprint

Cryogenic transmission electron microscopy (cryo-TEM) has been instrumental to reducing the effects of electron-beam sample interactions and enabling higher spatial resolution studies of beam sensitive materials. However, the process of thinning and freezing samples for cryo-TEM characterization is often complex and frequently involves removing materials from their environment or stimuli of interest for extended times. This can allow for structural relaxation, diffusion, and other dynamic processes to occur prior to characterization; for energy materials like battery electrodes, this makes it difficult to precisely correlate the imaged structure with native structures that evolve in-situ during device processing, operation, or aging. Here, we present methods to prepare cryo-TEM samples from energy materials during these critical processes in the relevant device lifetimes. This offers insights into improving the temporal resolution of cryo-TEM, while still exploiting its ability to achieve high spatial resolutions in characterizing beam sensitive materials.

BASIC BIOLOGICAL SCIENCES,MATERIALS SCIENCE↗
DOE OSTI2024

Nanocomposite Materials for Radionuclide Sequestration from Groundwater Environments

The half-lives of radionuclides range from fractions of a second to billions of years. Since no practical method of altering radioactive decay exists, and since exposure to either the energy emitted from radioactive decay or chemical properties of radionuclides poses dire health risks, radioactive materials must be segregated and controlled. The capture, treatment, and disposition of radioactive materials remain an extraordinary challenge. In here, we focus our attention on the synthesis and characterization of a unique class of nanocomposite materials that have potential for removal of radionuclide contamination. Specifically, we report a simple approach to decorate the surface of iron-based (Fe/FexOy) material with various nano-catalysts. Specifically, copper (Cu), tin (Sn), and silver (Ag) nanoparticles were prepared through two different reduction approaches, namely, citrate and cetyltrimethylammonium bromide (CTAB) methods, on the iron-based material surface. All samples were characterized by a variety of analytical tools, which included scanning electron microscopy (SEM), electron-dispersive X-ray microanalysis (EDS), and EDS mapping to elucidate materials’ morphology as well as nano-catalysts’ loading and location on the iron-based structures.

Hunyadi Murph, Simona E.↗
DOE OSTI2020

Covalently Linked, Two-Dimensional Quantum Dot Assemblies

Using nanoscale building blocks to construct hierarchical materials is a radical new branch point in materials discovery that promises new structures and emergent functionality. Understanding the design principles that govern nanoparticle assembly is critical to moving this field forward. By exploiting mixed ligand environments to target patchy nanoparticle surfaces, we have demonstrated a novel method of colloidal quantum dot (QD) assembly that gives rise to 2D structures. The equilibration of solutions of spherical and quasispherical QDs, including CdS, CdSe, and InP, with 2,2'-bipyridine-5,5'-diacrylic acid resulted in the preferential formation of 2D assemblies over the course of days as determined by transmission electron microscopy analysis. Small-angle X-ray scattering confirms the existence of the QD assemblies in solution. The dependence of the assembly on linker properties (length and rigidity), linker concentration, and total concentration was investigated, together with the data point to a mechanism involving ligand redistribution to create a patchy surface that maximizes the steric repulsion of neighboring QDs. By operating in an underexchanged regime, the arising patchiness results in enthalpically preferred directions of cross-linking that can be accessed by thermal equilibration.

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

Revealing 3D Morphological and Chemical Evolution Mechanisms of Metals in Molten Salt by Multimodal Microscopy

Growing interest in molten salts as effective high-temperature heat-transfer fluids for sustainable energy systems drives a critical need to fundamentally understand the interactions between metals and molten salts. This work utilizes the multimodal microscopy methods of synchrotron X-ray nanotomography and electron microscopy to investigate the 3D morphological and chemical evolution of two-model systems, pure nickel metal and Ni-20Cr binary alloy, in a representative molten salt (KCl-MgCl2 50–50 mol %, 800 °C). In both systems, unexpected shell-like structures formed because of the presence of more noble tungsten, suggesting a potential route of using Ni–W alloys for enhanced molten-salt corrosion resistance. The binary alloy Ni-20Cr developed a bicontinuous porous structure, reassembling functional porous metals manufactured by dealloying. This work elucidates better mechanistic understanding of corrosion in molten salts, which can contribute to the design of more reliable alloys for molten salt applications including next-generation nuclear and solar power plants and opens the possibility of using molten salts to fabricate functional porous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Question-answering system extracts information on injection drug use from clinical notes

Background. Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods. Here, to address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data. Results. Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions. Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Effects of surface charge and cluster size on the electrochemical dissolution of platinum nanoparticles using COMB3 and continuum electrolyte models

We study the site-dependent dissolution of platinum nanoparticles under electrochemical conditions to assess their thermodynamic stability as a function of shape and size using empirical molecular dynamics and electronic-structure models. The third-generation charge optimized many-body potential is employed to determine the validity of uniform spherical representations of the nanoparticles in predicting dissolution potentials (the Kelvin model). To understand the early stages of catalyst dissolution, implicit solvation techniques based on the self-consistent continuum solvation method are applied. It is demonstrated that interfacial charge and polarization can shift the dissolution energies by amounts on the order of 0.74 eV depending on the surface site and nanoparticle shape, leading to the unexpected preferential removal of platinum cations from highly coordinated sites in some cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗