Search NASA⌕ Search

SEARCH · Search NASA

Results for “structural”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Structures of single-layer {beta}-sheet proteins evolved from {beta}-hairpin repeats

Free-standing single-layer beta-sheets are extremely rare in naturally occurring proteins, even though beta-sheet motifs are ubiquitous. Here we report the crystal structures of three homologous, single-layer, anti-parallel beta-sheet proteins, comprised of three or four twisted beta-hairpin repeats. The structures reveal that, in addition to the hydrogen bond network characteristic of beta-sheets, additional hydrophobic interactions mediated by small clusters of residues adjacent to the turns likely play a significant role in the structural stability and compensate for the lack of a compact hydrophobic core. These structures enabled identification of a family of secreted proteins that are broadly distributed in bacteria from the human gut microbiome and are putatively involved in the metabolism of complex carbohydrates. A conserved surface patch, rich in solvent-exposed tyrosine residues, was identified on the concave surface of the beta-sheet. These new modular single-layer beta-sheet proteins may serve as a new model system for studying folding and design of beta-rich proteins.

beta-hairpin repeats↗
DOE OSTI2022

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure–function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1–3 GPa, which is consistent with crystalline proteins. Altogether, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Chemical structure↗
DOE OSTI2016

Direct Measurement of the Tunable Electronic Structure of Bilayer MoS 2 by Interlayer Twist

Using angle-resolved photoemission on micrometer-scale sample areas, we directly measure the interlayer twist angle-dependent electronic band structure of bilayer molybdenum-disulfide (MoS 2 ). Our measurements, performed on arbitrarily stacked bilayer MoS 2 flakes prepared by chemical vapor deposition, provide direct evidence for a downshift of the quasiparticle energy of the valence band at the Brillouin zone center ($\bar{Γ}$ point) with the interlayer twist angle, up to a maximum of 120 meV at a twist angle of ~40°. Our direct measurements of the valence band structure enable the extraction of the hole effective mass as a function of the interlayer twist angle. While our results at $\bar{Γ}$agree with recently published photoluminescence data, our measurements of the quasiparticle spectrum over the full 2D Brillouin zone reveal a richer and more complicated change in the electronic structure than previously theoretically predicted. The electronic structure measurements reported here, including the evolution of the effective mass with twist-angle, provide new insight into the physics of twisted transition-metal dichalcogenide bilayers and serve as a guide for the practical design of MoS 2 optoelectronic and spin-/valley-tronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Quantum Yield Heterogeneity among Single Nonblinking Quantum Dots Revealed by Atomic Structure-Quantum Optics Correlation

Physical variations in colloidal nanostructures give rise to heterogeneity in expressed optical behavior. This correlation between nanoscale structure and function demands interrogation of both atomic structure and photophysics at the level of single nanostructures to be fully understood. In this paper, by conducting detailed analyses of fine atomic structure, chemical composition, and time-resolved single-photon photoluminescence data for the same individual nanocrystals, we reveal inhomogeneity in the quantum yields of single nonblinking “giant” CdSe/CdS core/shell quantum dots (g-QDs). We find that each g-QD possesses distinctive single exciton and biexciton quantum yields that result mainly from variations in the degree of charging, rather than from volume or structure inhomogeneity. We further establish that there is a very limited nonemissive “dark” fraction (<2%) among the studied g-QDs and present direct evidence that the g-QD core must lack inorganic passivation for the g-QD to be “dark”. Finally and therefore, in contrast to conventional QDs, ensemble photoluminescence quantum yield is principally defined by charging processes rather than the existence of dark g-QDs.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A fingerprint based metric for measuring similarities of crystalline structures

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Structural stability, electronic structure and mechanical properties of actinide carbides AnC (An = U, Np)

Ab initio calculations are performed to investigate the structural stability, electronic structure and mechanical properties of actinide carbides AnC (An=U, Np) for three different crystal structures, namely NaCl, CsCl and ZnS. Among the considered structures, NaCl structure is found to be the most stable structure for these carbides at normal pressure. A pressure induced structural phase transition from NaCl to ZnS is observed. The electronic structure reveals that these carbides are metals. The calculated elastic constants indicate that these carbides are mechanically stable at normal pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Electronic Structures and Magnetism of Zr-, Th-, and U-based Metal-Organic Frameworks (MOFs) by Density Functional Theory

Metal-organic frameworks (MOFs) have recently gained wide interest as candidate materials for nuclear waste immobilization. While the fundamental thermodynamic properties, such as the substitution energies determine the favorability of radionuclide sequestration by utilization of a MOF matrix, the studies of MOF electronic structure reveal the role of d-, and/or f-electrons on changes in physical properties of actinide-containing materials. We use density functional theory (DFT) calculations to investigate the electronic structures of Zr-, Th-, and U-MOFs, including their electronic band structures and, where appropriate, their magnetic properties. We employ various DFT methods including DFT+U, collinear spin-polarization, spin-orbit coupling, and different flavors of exchange-correlation functionals to assess the robustness to the specific exchange-correlation functional. Unlike the Zr-, and Th-MOFs, the U-MOF is found to be sensitive to electron localization and spin; hence we explore the magnetic structure of the U-MOF in further detail.

Metal-Organic Framework, MOF, Actinides, DFT↗
DOE OSTI2017

Structure Determination of Mycobacterium tuberculosis Serine Protease Hip1 (Rv2224c)

The Mycobacterium tuberculosis (Mtb) serine protease Hip1 (hydrolase important for pathogenesis; Rv2224c) promotes tuberculosis (TB) pathogenesis by impairing host immune responses through proteolysis of a protein substrate, Mtb GroEL2. The cell surface localization of Hip1 and its immunomodulatory functions make Hip1 a good drug target for new adjunctive immune therapies for TB. Here, we report the crystal structure of Hip1 to a resolution of 2.6 Å and the kinetic studies of the enzyme against model substrates and the protein GroEL2. The structure shows a two-domain protein, one of which contains the catalytic residues that are the signature of a serine protease. Surprisingly, a threonine is located within the active site close enough to hydrogen bond with the catalytic residues Asp463 and His490. Mutation of this residue, Thr466, to alanine established its importance for function. Our studies provide insights into the structure of a member of a novel family of proteases. Knowledge of the Hip1 structure will aid in designing inhibitors that could block Hip1 activity

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Atomic Structure of Pt 3 Ni Nanoframe Electrocatalysts by in Situ X-ray Absorption Spectroscopy

Understanding the atomic structure of a catalyst is crucial to exposing the source of its performance characteristics. It is highly unlikely that a catalyst remains the same under reaction conditions when compared to as-synthesized. Hence, the ideal experiment to study the catalyst structure should be performed in situ. Here, we use X-ray absorption spectroscopy (XAS) as an in situ technique to study Pt3Ni nanoframe particles which have been proven to be an excellent electro catalyst for the oxygen reduction reaction (ORR). The surface characteristics of the nanoframes were probed through electrochemical hydrogen underpotential deposition and carbon monoxide electrooxidation, which showed that nanoframe surfaces with different structure exhibit varying levels of binding strength to adsorbate molecules. It is well-known that Pt-skin formation on Pt-Ni catalysts will enhance ORR activity by weakening the binding energy between the surface and adsorbates. Ex situ and in situ XAS results reveal that nanoframes which bind adsorbates more strongly have a rougher Pt surface caused by insufficient segregation of Pt to the surface and consequent Ni dissolution. In contrast, nanoframes which exhibit extremely high ORR activity simultaneously demonstrate more significant segregation of Pt over Ni-rich subsurface layers, allowing better formation of the critical Pt-skin. This work demonstrates that the high ORR activity of the Pt3Ni hollow nanoframes depends on successful formation of the Pt-skin surface structure.

Becknell, Nigel↗
DOE OSTI2016

Ab initio electronic structure of quasi-two-dimensional materials: A “native” Gaussian–plane wave approach

Ab initio electronic structure calculations of two-dimensional layered structures are typically performed using codes that were developed for three-dimensional structures, which are periodic in all three directions. The introduction of a periodicity in the third direction (perpendicular to the layer) is completely artificial and may lead in some cases to spurious results and to difficulties in treating the action of external fields. In this paper we develop a new approach, which is “native” to quasi-2D materials, making use of basis function that are periodic in the plane, but atomic-like in the perpendicular direction. We show how some of the basic tools of ab initio electronic structure theory — density functional theory, GW approximation and Bethe-Salpeter equation — are implemented in the new basis. We argue that the new approach will be preferable to the conventional one in treating the peculiarities of layered materials, including the long range of the unscreened Coulomb interaction in insulators, and the effects of strain, corrugations, and external fields.

Trevisanutto, Paolo E.↗
DOE OSTI2017

Experimental evidence for pressure-induced first order transition in cerium nitride from B1 to B10 structure type

Here, the crystal structure of CeN was investigated up to pressures of 82 GPa, using diamond anvil cell powder X-ray diffraction in two experiments with He and Si-oil as the pressure transmitting media. In contrast to previous reports, we do not observe the B2 (CsCl type) structure at high pressure. Instead, the structural phase transition, starting at 65 GPa, from the ambient rock salt B1 structure results in a distorted CsCl-like B10 structure, irrespective of the pressure medium. Our result unambiguously confirms two recent density functional theory (DFT) studies predicting the B10 phase to be stable at these pressures, rather than the B2 (CsCl type) phase previously reported. The B10 structure appears to approach the B2 structure as pressure is increased further, but DFT calculations indicate that an L1 0 structure (AuCu type) is energetically favored

36 MATERIALS SCIENCE↗
DOE OSTI2017

Modeling adsorption properties of structurally deformed metal–organic frameworks using structure–property map

Structural deformation and collapse in metal-organic frameworks (MOFs) can lead to loss of long-range order, making it a challenge to model these amorphous materials using conventional computational methods. In this work, we show that a structure–property map consisting of simulated data for crystalline MOFs can be used to indirectly obtain adsorption properties of structurally deformed MOFs. The structure–property map (with dimensions such as Henry coefficient, heat of adsorption, and pore volume) was constructed using a large data set of over 12000 crystalline MOFs from molecular simulations. By mapping the experimental data points of deformed SNU-200, MOF-5, and Ni-MOF-74 onto this structure–property map, we show that the experimentally deformed MOFs share similar adsorption properties with their nearest neighbor crystalline structures. Furthermore, once the nearest neighbor crystalline MOFs for a deformed MOF are selected from a structure–property map at a specific condition, then the adsorption properties of these MOFs can be successfully transformed onto the degraded MOFs, leading to a new way to obtain properties of materials whose structural information is lost.

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

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2023

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

3D visualization↗
DOE OSTI2018

A Two-Fold Structural Classification Method for Determining the Accurate Ensemble of Protein Structures

Atomic-level structural characterization of flexible proteins, such as intrinsically disordered proteins and multi-domain proteins connected by flexible linkers, is challenging as they possess distinct conformations in physiological conditions. Significant efforts have been made to develop integrated approaches by combining small angle neutron/X-ray scattering experiments with molecular simulations to reveal the distinct atomic structures and the corresponding populations for these flexible proteins. One widely used method, the basis-set supported ensemble method, classifies the simulation-generated protein conformations into a set of structural basis and then derives the corresponding populations by fitting to the experimental data. This method makes an implicit assumption that protein conformations of similar structures have similar small angle scattering profiles.The present work demonstrates that, for various protein systems ranging from compact globular proteins and flexible multidomain proteins through to intrinsically disordered proteins, this method provides inaccurate assessment of the structural ensemble of the protein molecules due to the breakdown of the assumption made. To alleviate this problem, a two-fold-clustering method is developed to cluster the simulation-generated protein structures using information on both 3D structure and scattering profiles. As benchmarked by both simulation and experimental results, this new method yields much more accurate populations of structural basis of protein molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A quantitative study of the structure-activity relationship in hierarchical zeolites using liquid-phase reactions

Micro/meso/macroporous (hierarchical) zeolites show remarkable catalytic performance for reactions involving bulky reactants. However, quantitative assessment of the microstructural characteristics contributing to the observed performance remains elusive. Here, structure–activity relationships are established for a set of micro/mesoporous self-pillared pentasil (SPP) zeolites using two parallel liquid-phase reactions (benzyl alcohol alkylation and self-etherification) based on analysis of mass transport and reaction kinetics. A reaction–diffusion mathematical model is developed that quantitatively assigns the catalytic contributions of the external surface and micropores of SPP zeolites for these reactions. In addition, the effect of the zeolite external surface structure on the corresponding catalytic activity is quantitatively assessed by comparing SPP zeolites (with MFI structure) with MCM-22 (with MWW structure). This work demonstrates that reaction–diffusion modeling allows quantitative description of the catalytic performance of hierarchical zeolites and provides a model reaction to assess nm-sized characteristic diffusion lengths in MFI.

bio-inspired↗
DOE OSTI2021

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state-of-the-art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field.

Global optimization, Chemical clusters, Artificial↗