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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Theoretical search for possible Li–Ni–B crystal structures using an adaptive genetic algorithm

The structural diversity of rare-earth and transition metal borides indicates that alkali-transition metal borides (A-T-B) show tremendous promise in exhibiting a variety of crystal structures with different dimensionalities of T-B frameworks. On the other hand, the A-T-B ternary systems are severely underexplored because of the synthetic challenges associated with their preparation. Accurate and efficient computational predictions of low-energy stable and metastable phases can identify the optimal compositions of the hypothetical compounds in the A-T-B systems to guide the synthesis. As such, in this work, we have computationally discovered several new phases in the Li–Ni–B ternary system. The newly discovered LiNiB, Li 2 Ni 3 B, and Li 2 NiB phases expand the existing theoretical database, and the convex-hull surface of Li–Ni–B has been re-constructed. The lowest energy structure of the LiNiB compound has been found by an adaptive genetic algorithm with layered motif, which matches with the experimentally determined structure. According to our electrochemical calculations, LiNiB and another predicted layered Li 2 NiB compounds have great potential as anode materials for lithium batteries. The Li 2 Ni 3 B compound with the space group P4 3 32 was predicted to crystallize in a cubic structure composed of distorted octahedral units of BNi 6 , which is isostructural to two noncentrosymmetric superconductors Li 2 Pd 3 B and Li 2 Pt 3 B. While we were unable to experimentally confirm the Li 2 Ni 3 B compound utilizing the hydride synthetic route, attempts to synthesize this compound by alternate methods remain highly desirable, considering its potential superconducting properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning

Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.

Lipo-chitooligosaccharides↗
DOE OSTI2023

Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody

Since November 2021, Omicron has been the dominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant that causes the coronavirus disease 2019 (COVID-19) and has continuously impacted human health. Omicron sublineages are still increasing and cause increased transmission and infection rates. The additional 15 mutations on the receptor binding domain (RBD) of Omicron spike proteins change the protein conformation, enabling the Omicron variant to evade neutralizing antibodies. For this reason, many efforts have been made to design new antigenic variants to induce effective antibodies in SARS-CoV-2 vaccine development. However, understanding the different states of Omicron spike proteins with and without external molecules has not yet been addressed. In this review, we analyze the structures of the spike protein in the presence and absence of angiotensin-converting enzyme 2 (ACE2) and antibodies. Compared to previously determined structures for the wildtype spike protein and other variants such as alpha, beta, delta, and gamma, the Omicron spike protein adopts a partially open form. The open-form spike protein with one RBD up is dominant, followed by the open-form spike protein with two RBD up, and the closed-form spike protein with the RBD down. It is suggested that the competition between antibodies and ACE2 induces interactions between adjacent RBDs of the spike protein, which lead to a partially open form of the Omicron spike protein. The comprehensive structural information of Omicron spike proteins could be helpful for the efficient design of vaccines against the Omicron variant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, in this study, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets, and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM and cryo-ET as a high-throughput tool for structural biology and discovery.

Levy, Axel [Stanford Univ., CA (United States); SL↗
DOE OSTI2025

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof of principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. In conclusion, our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

HIV-1 dimerization initiation site↗
DOE OSTI2022

PDB‐101: Molecular Explorations through Biology and Medicine

PDB‐101 is an online portal for teachers, students, and the general public to promote exploration of the structural biology of proteins and nucleic acids ( pdb101.rcsb.org ). Learning about the diverse shapes and functions of these biological macromolecules helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy. Why PDB‐101? Researchers around the world are studying these molecules at the atomic level. These 3D structures are freely available at the Protein Data Bank (PDB), the central storehouse of biomolecular structures. This website builds introductory materials to help beginners get started in the basics of biomolecular structure and function (“101”, as in an entry level course) as well as resources for extended learning. Since 2011, PDB‐101 has been developed by the RCSB PDB , a global resource for the advancement of research and education in biology and medicine. Along with our Worldwide PDB collaborators, RCSB PDB curates, annotates, and makes publicly available the PDB data deposited by scientists around the globe. The RCSB PDB then provides a window to these data through a rich online resource with powerful searching, reporting, and visualization tools for researchers. This information is then streamlined for students and teachers at PDB‐101. Features include the ongoing Molecule of the Month series, educational materials such as paper models, posters, molecular animations, educational curricula and more. The section “Guide to Understanding PDB Data” is a primer for detailed PDB‐specific information: PDB Data, Visualizing Structures, Reading Coordinate Files, scientific methods for structure determination, and more. PDB‐101 also runs annual Video Challenges for high school students. Participants create short videos that tell molecular stories that connect structural biology and medicine. Previous topics have included HIV/AIDS, diabetes, and antimicrobial resistance. The 2022 challenge will focus on Molecular Mechanisms of Cancer. PDB‐101 activities are evaluated using user surveys, feedback from in‐person activities, and website analytics. In 2020, PDB‐101 hosted >850,000 users and >2.6 million page views.

Zardecki, Christine↗
DOE OSTI2022

Humble planar defects in SiGe nanopillars

We report a {001} planar defect found in SiGe nanopillars. The defect structure, determined by atomic-resolution electron microscopy, matches the Humble defect model proposed for diamond. We also investigate several possible variants of the Humble structure using first-principles calculations and find that the one lowest in energy is in agreement with the scanning transmission electron microscope images. The pillar composition has been analyzed with electron energy loss spectroscopy, which hints at how the defect is formed. Our results show that the structure and formation process of defects in nanostructured group IV semiconductors can be different from their bulk counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structure constants in $ \mathcal{N} $ = 4 SYM at finite coupling as worldsheet g-function

We develop a novel nonperturbative approach to a class of three-point functions in planar $ \mathcal{N} $ N = 4 SYM based on Thermodynamic Bethe Ansatz (TBA). More specifically, we study three-point functions of a non-BPS single-trace operator and two determinant operators dual to maximal Giant Graviton D-branes in AdS 5 × S 5 . They correspond to disk one-point functions on the worldsheet and admit a simpler and more powerful integrability description than the standard single-trace three-point functions. We first introduce two new methods to efficiently compute such correlators at weak coupling; one based on large N collective fields, which provides an example of open-closed-open duality discussed by Gopakumar, and the other based on combinatorics. The results so obtained exhibit a simple determinant structure and indicate that the correlator can be interpreted as a generalization of g -functions in 2d QFT; an overlap between an integrable boundary state and a state corresponding to the single-trace operator. We then determine the boundary state at finite coupling using the symmetry, the crossing equation and the boundary Yang-Baxter equation. With the resulting boundary state, we derive the ground-state g -function based on TBA and conjecture its generalization to other states. This is the first fully nonperturbative proposal for the structure constants of operators of finite length. The results are tested extensively at weak and strong couplings. Finally, we point out that determinant operators can provide better probes of sub-AdS locality than single-trace operators and discuss possible applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Synthesis, structural characterization, and electronic structure of the novel Zintl phase Ba 2 ZnP 2

The novel Zintl phase dibarium zinc diphosphide (Ba 2 ZnP 2 ) was synthesized for the first time. This was accom­plished using the Pb flux technique, which allowed for the growth of crystals of adequate size for structural determination via single-crystal X-ray diffraction methods. The Ba 2 ZnP 2 com­pound was determined to crystallize in a body-centered ortho­rhom­bic space group, Ibam (No. 72). Formally, this crystallographic arrangement belongs to the K 2 SiP 2 structure type. Therefore, the structure can be best described as infinite [ZnP 2 ] 4– polyanionic chains with divalent Ba 2+ cations located between the chains. All valence electrons are partitioned, which conforms to the Zintl–Klemm concept and suggests that Ba 2 ZnP 2 is a valence-precise com­position. In conclusion, the electronic band structure of this new com­pound, com­puted with the aid of the TB–LMTO–ASA code, shows that Ba 2 ZnP 2 is an intrinsic semiconductor with a band gap of ca 0.6 eV.

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

Distinguishing isotropic and anisotropic signals for X-ray total scattering using machine learning

Understanding structure–property relationships is essential for advancing technologies based on thin films. X-ray pair distribution function (PDF) analysis can access relevant atomic structure details spanning local-, mid- and long-range structure. While X-ray PDF has been adapted for thin films on amorphous substrates, measurements on single-crystal substrates are necessary to accurately determine structure origins for some thin film materials, especially those for which the substrate changes the accessible structure and properties. However, when measuring films on single-crystal substrates, high-intensity anisotropic Bragg spots saturate 2D detector images, overshadowing the thin films' isotropic scattering signal. This renders previous data processing methods for films on amorphous substrates unsuitable for films on single-crystal substrates. To address this measurement need, we developed IsoDAT2D, an innovative data processing approach using unsupervised machine learning algorithms. The program combines dimensionality reduction and clustering algorithms to separate thin film and single-crystal substrate X-ray scattering signals. We use SimDAT2D , a program we developed to generate simulated thin film data, to validate IsoDAT2D . Here we also use IsoDAT2D to isolate X-ray total scattering signal from a thin film on a single-crystal substrate. The resulting PDF data are compared with similar data processed using previous methods, especially substrate subtraction for single-crystal and amorphous substrates. PDF data from IsoDAT2D -identified X-ray total scattering data are significantly better than from single-crystal substrate subtraction, but not as reliable as PDF data from amorphous substrate subtraction. With IsoDAT2D , there are new opportunities to expand PDF to a wider variety of thin films, including those on single-crystal substrates, with which new structure–property relationships can be elucidated to enable fundamental understanding and technological advances.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Highly Ordered Nitroxide Side Chain for Distance Mapping and Monitoring Slow Structural Fluctuations in Proteins

Abstract Site-directed spin labeling electron paramagnetic resonance (SDSL-EPR) is an established tool for exploring protein structure and dynamics. Although nitroxide side chains attached to a single cysteine via a disulfide linkage are commonly employed in SDSL-EPR, their internal flexibility complicates applications to monitor slow internal motions in proteins and to structure determination by distance mapping. Moreover, the labile disulfide linkage prohibits the use of reducing agents often needed for protein stability. To enable the application of SDSL-EPR to the measurement of slow internal dynamics, new spin labels with hindered internal motion are desired. Here, we introduce a highly ordered nitroxide side chain, designated R9, attached at a single cysteine residue via a non-reducible thioether linkage. The reaction to introduce R9 is highly selective for solvent-exposed cysteine residues. Structures of R9 at two helical sites in T4 Lysozyme were determined by X-ray crystallography and the mobility in helical sequences was characterized by EPR spectral lineshape analysis, Saturation Transfer EPR, and Saturation Recovery EPR. In addition, interspin distance measurements between pairs of R9 residues are reported. Collectively, all data indicate that R9 will be useful for monitoring slow internal structural fluctuations, and applications to distance mapping via dipolar spectroscopy and relaxation enhancement methods are anticipated.

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

Scattering Matrix Determination in Crystalline Materials from 4D Scanning Transmission Electron Microscopy at a Single Defocus Value

Recent work has revived interest in the scattering matrix formulation of electron scattering in transmission electron microscopy as a stepping stone toward atomic-resolution structure determination in the presence of multiple scattering. Here, we discuss ways of visualizing the scattering matrix that make its properties clear. Through a simulation-based case study incorporating shot noise, we shown how regularizing on this continuity enables the scattering matrix to be reconstructed from 4D scanning transmission electron microscopy (STEM) measurements from a single defocus value. Intriguingly, for crystalline samples, this process also yields the sample thickness to nanometer accuracy with no a priori knowledge about the sample structure. The reconstruction quality is gauged by using the reconstructed scattering matrix to simulate STEM images at defocus values different from that of the data from which it was reconstructed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The synthesis of novel lanthanum hydroxyborate at extreme conditions

The novel structure of lanthanum hydroxyborate La 2 B 2 O 5 (OH) 2 was synthesized by the reaction of partially hydrolyzed lanthanum and boron oxide in a diamond anvil cell under high-pressure/high-temperature (HPHT) conditions of 30 GPa and ~2,400 K. The single-crystal X-ray structure determination of the lanthanum hydroxyborate revealed: P$\overline{3}$c1 , a = 6.555(2) Å, c = 17.485(8) Å, Z = 6, R 1 = 0.056. The three-dimensional structure consists of discrete planar BO 3 groups and three crystallographically different La ions: one is surrounded by 9, one by 10, and one by 12 oxygen anions. The band gap was estimated using ab initio calculations to be 4.64 eV at ambient pressure and 5.26 eV at 30 GPa. The current work describes the novel HPHT lanthanum hydroxyborate with potential application as a deep-ultraviolet birefringent material.

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

Geospatial analysis of preterm and small-for-gestational age births in Washington D.C.

Background: This study is based on the recognition that adverse pregnancy outcomes significantly affect maternal and infant health, leading to increased morbidity and mortality. These outcomes are shaped by a complex interplay of individual-level factors—like maternal age and education—and community-level influences, including socio-economic status and access to healthcare. Understanding these determinants is crucial for developing effective public health strategies, especially for marginalized populations, by identifying high-risk areas and informing targeted interventions that address both individual and structural barriers. Methods: We utilized geospatial analysis to explore the association between individual- and community-level factors and adverse pregnancy outcomes, specifically preterm birth (PTB) and small-for-gestational-age (SGA) birthweight in Washington, D.C. We used Empirical Bayes smoothing methods to calculate rates of adverse birth outcomes from 2010 to 2018 at the U.S. Census tract–level. Spatial scan statistics were used to investigate if adverse birth outcomes clustered in specific areas. ANOVA tests were conducted for individual- and community-level factors within identified clusters. Results: Spatial analysis identified significant high-risk clusters for PTB and SGA infants primarily in southeastern Washington, D.C., particularly in Wards 7 and 8. Individuals residing within these clusters experienced a 47% increased risk of PTB (RR = 1.467) and a 56% increased risk of SGA (RR = 1.560) compared to those outside clusters. Space–time analysis revealed temporal variation, with PTB clusters persisting from 2011 to 2014 and SGA clusters extending through 2017. Compared to low-risk clusters, high-risk clusters had younger birthing individuals (mean age ~26.5 vs. ~33 years), lower maternal college degree attainment (~20% vs. ~80%), higher rates of late or no prenatal care (~16% vs. 11%), and increased prevalence of smoking and hypertension (all P < 0.001). Community-level indicators showed lower median household incomes ($\$40,000$ vs. ~$\$105,000$), greater poverty (~16% vs. ~7% below $\$10,000$/year), higher public assistance use (~32% vs. ~5%), and reduced healthcare access (greater distances to emergency and specialty care) in high-risk areas (all P < 0.001). Neighborhood deprivation indices were significantly elevated, commutes were longer, and population density was lower in these clusters. These findings highlight that adverse birth outcomes cluster in neighborhoods with pronounced socioeconomic and health disparities. Conclusion: High-risk birth clusters highlight intertwined factors: individual, socio-economic, and geographic. Addressing these requires comprehensive interventions focusing on social and structural determinants of health.

Birth outcomes↗
DOE OSTI2021

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

Here, we outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self-organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure-function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The crystal structure of a new calcium aluminate phase containing formate

A new calcium aluminate phase containing formate ions was synthesized and its crystal structure determined. This new phase is indicated as M-phase and was firstly observed in Portland cement pastes hydrated in presence of Ca-formate and in excess of water. The crystal structure of the M-phase was successfully solved in the R-3 space group of the trigonal system on the basis of synchrotron X-ray single crystal diffraction data. The structural model was confirmed by Rietveld refinement of the powder diffraction data acquired on the synthesized pure sample. The crystal structure of the M-phase is similar to that of ettringite, being characterized by columns of AlO{sub 6} octahedra alternating with groups of three edge-sharing CaO{sub 7} polyhedra. The formate ions (HCOO){sup −} share two oxygens with Ca polyhedra and are located in the interspace between the columns. The crystal structure of the M-phase testifies the strong interaction occurring between small organic molecules as formate and the calcium aluminate components of Portland cement.

36 MATERIALS SCIENCE↗
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↗