Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “structural complexity”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Anisotropic modeling and joint-MAP stitching for improved ultrasound model-based iterative reconstruction of large and thick specimens

One-sided non-destructive evaluation (NDE) is widely used to inspect materials, such as concrete structures in nuclear power plants (NPP). A widely used method for one-sided NDE is the synthetic aperture focusing technique (SAFT). The SAFT algorithm produces reasonable results when inspecting simple structures. However, for complex structures, such as heavily reinforced thick concrete structures, SAFT results in artifacts and hence there is a need for a more sophisticated inversion technique. Model-based iterative reconstruction (MBIR) algorithms, which are typically equivalent to regularized inversion techniques, offer a powerful framework to incorporate complex models for the physics, detector miscalibrations and the materials being imaged to obtain high quality reconstructions. Previously, we have proposed an ultrasonic MBIR method that signifcantly improves reconstruction quality compared to SAFT. However, the method made some simplifying assumptions on the propagation model and did not disucss ways to handle data that is obtained by raster scanning a system over a surface to inspect large regions. In this paper, we propose a novel MBIR algorithm that incorporates an anisotropic forward model and allows for the joint processing of data obtained from a system that raster scans a large surface. We demonstrate that the new MBIR method can produce dramatic improvements in reconstruction quality compared to SAFT and suppresses articfacts compared to the perviously presented MBIR approach.

Almansouri, Hani↗
DOE OSTI2015

The contribution of Diamond Light Source to the study of strongly correlated electron systems and complex magnetic structures

We review some of the significant contributions to the field of strongly correlated materials and complex magnets, arising from experiments performed at the Diamond Light Source (Harwell Science and Innovation Campus, Didcot, UK) during the first few years of operation (2007–2014). We provide a comprehensive overview of Diamond research on topological insulators, multiferroics, complex oxides and magnetic nanostructures. Several experiments on ultrafast dynamics, magnetic imaging, photoemission electron microscopy, soft X-ray holography and resonant magnetic hard and soft X-ray scattering are described.

Radaelli, P. G.↗
DOE OSTI2016

3D printing of large, complex metallic glass structures

Metallic glasses (MGs) or amorphous alloys although have superior mechanical properties their products are limited to simple geometries such as foils/plates or rods with thin section-thickness due to the requirement of high cooling rates. In this study, 3D, large dimensions of amorphous structures with complex geometry are manufactured by our newly developed Laser Foil Printing (LFP) technology. Zr-based (LM105, provided by Liquidmetal Technologies, Inc.) amorphous foils of 100 μm thickness are used as feedstock, and they are laser welded, layer-by-layer, to become 3D amorphous structures. Test results by X-Ray diffraction (XRD), differential scanning calorimetry (DSC), and micro-hardness confirm that the printed structures at selected process parameters achieve the same or better degree of amorphization as the raw foils. A mathematical model was developed to calculate the heating and cooling rates during structure manufacturing which helps the selection of process parameters. Here this study expands MG products to 3D arbitrary geometries with large dimensions due to the inherited advantages of the LFP technology which would open many potential applications.

36 MATERIALS SCIENCE↗
NASA NTRS2023

An ALMA Glimpse of Dense Molecular Filaments Associated with High-mass Protostellar Systems in the Large Magellanic Cloud

Recent millimeter/submillimeter facilities have revealed the physical properties of filamentary molecular clouds in relation to high-mass star formation. A uniform survey of the nearest, face-on star-forming galaxy, the Large Magellanic Cloud (LMC), complements the Galactic knowledge. We present ALMA survey data with a spatial resolution of ∼0.1 pc in the 0.87 mm continuum and HCO+ (4–3) emission toward 30 protostellar objects with luminosities of 104–105.5L⊙ in the LMC. The spatial distributions of the HCO+ (4–3) line and thermal dust emission are well correlated, indicating that the line effectively traces dense, filamentary gas with an H2 volume density of ≳105 cm−3 and a line mass of ∼103–104M⊙ pc−1. Furthermore, we obtain an increase in the velocity line widths of filamentary clouds, which follows a power-law dependence on their H2 column densities with an exponent of ∼0.5. This trend is consistent with observations toward filamentary clouds in nearby star-forming regions within ≲1 kpc from us and suggests enhanced internal turbulence within the filaments due to surrounding gas accretion. Among the 30 sources, we find that 14 are associated with hub-filamentary structures, and these complex structures predominantly appear in protostellar luminosities exceeding ∼5 × 104L⊙. The hub-filament systems tend to appear in the latest stages of their natal cloud evolution, often linked to prominent H II regions and numerous stellar clusters. Our preliminary statistics suggest that the massive filaments accompanied by hub-type complex features may be a necessary intermediate product in forming extremely luminous high-mass stellar systems capable of ultimately dispersing the parent cloud.

Marta M Sewilo↗
DOE OSTI2014

Genomic Functionalization: The Next Revolution In Biology

We have implemented a ligand-alignment algorithm into our developed computational pipeline for identifying specificity-determining features (SDFs) in protein-ligand complexes. Given a set of protein-ligand complex structures, the algorithm aligns the complexes by ligand rather than by the C -RMSD or standard approach, providing a single reference frame for extracting SDFs. We anticipate that this ligand-alignment capability will be highly useful for protein function prediction. We already have a database containing > 20 K ligand-protein complex crystal structures taken from the Protein Data Bank. By aligning these proteins to single reference frames using ligand alignment, we can submit the complexes to our pipeline for SDF extraction. The SDFs derived from this training procedure can be used as thumbprints that are hallmarks of individual enzyme classes. These SDF thumbprints may then serve as guides to the prediction of function of new unknown proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Photodissociation of (SO{sub 2}⋯XH) Van der Waals complexes and clusters (XH = C{sub 2}H{sub 2}, C{sub 2}H{sub 4}, C{sub 2}H{sub 6}) excited at 32 040–32 090 cm{sup −1} with formation of HSO{sub 2} and X

We studied photodecomposition dynamics of (SO{sub 2}⋯XH) Van der Waals’ (VdW) complexes and clusters in gas phase, with X = C{sub 2}H, C{sub 2}H{sub 3}, and C{sub 2}H{sub 5}. SO{sub 2} was excited by frequency-doubled radiation of a tunable dye laser and resonance-enhanced multiphoton ionization was used to detect the C{sub 2}H (m/z 25), C{sub 2}H{sub 3} (m/z 27), and C{sub 2}H{sub 5} (m/z 29) ions by time-of-flight mass spectroscopy. Spectra obtained at higher nozzle pressures (P{sub 0} > 2.5 atm) indicate formation of clusters. Detailed studies of the VdW complex structure were carried out by analyzing the rotational structure of the respective action spectra. We also performed ab initio theoretical analysis of structures of the VdW complexes and transitional states leading to photodecomposition. We find that the structure of the transition state is significantly different as compared to the equilibrium ground-state structure of the respective complex. The photodecomposition mechanism depends on the hydrocarbon molecule bound to SO{sub 2}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Four-coordinate six-membered ketiminate and diketiminate boron complexes. Synthesis, structure, reactivity, and application in the design of nitrogen heterocycles

The data on ketiminate and diketiminate boron complexes are first reviewed. The present review is focused on the synthesis and chemical transformations of these chelates. The special attention is paid to the application of these boron chelates in the synthesis of nitrogen heterocycles including CF{sub 3}-substituted derivatives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Uncovering the lipidic basis for the preparation of functional nicotinic acetylcholine receptor detergent complexes for structural studies

This study compares the lipid composition, including individual phospholipid molecular species of solubilized nAChR detergent complexes (nAChR-DCs) with those of the bulk lipids from their source, Torpedo californica (Tc) electric tissue. This lipidomic analysis revealed seventy-seven (77) phospholipid species in the Tc tissue. Analysis of affinity-purified nAChR-DCs prepared with C-12 to C-16 phospholipid analog detergents alkylphosphocholine (FC) and lysofoscholine (LFC) demonstrated that nAChR-DCs prepared with FC12, LFC14, and LFC16 contained >60 phospholipids/nAChR, which was more than twice of those prepared with FC14, FC16, and LFC12. Significantly, all the nAChR-DCs lacked ethanolamine and anionic phospholipids, contained only four cholesterol molecules, and a limited number of phospholipid molecular species per nAChR. Upon incorporation into oocytes, FC12 produce significant functionality, whereas LFC14 and LFC16 nAChR-DCs displayed an increased functionality as compared to the crude Tc membrane. All three nAChR-DCs displayed different degrees of alterations in macroscopic activation and desensitization kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Ellipsometric study of metal-organic chemically vapor deposited III-V semiconductor structures

An ellipsometric study of MOCVD-grown layers of AlGaAs and InGaAs in thick films and strained layer complex structures is presented. It is concluded that the ternary composition of thick nonstrained layers can be accurately determined to within experimental errors using numerical algorithms. In the case of complex structures, thickness of all layers and the alloy composition of nonstrained layers can be determined simultaneously, provided that the correlations between parameters is no higher than 0.9.

Alterovitz, Samuel A.↗
DOE OSTI2018

Structures of glycolate oxidase from Nicotiana benthamiana reveal a conserved pH sensor affecting the binding of FMN

Highlights: • We solved the structures of apo- and FMN-bound GOX from Nicotiana benthamiana. • We reported the first crystal structure of GOX without FMN among all species. • We found that the binding of FMN induces a pronounced conformational change of GOX. • We found a conserved pH sensor that might directly regulate the binding of FMN. • We provided a feasible method to regulate the activity of GOX in patients and crops. Glycolate oxidase (GOX), a flavin mononucleotide (FMN)-dependent enzyme, modulates reactive oxygen species-mediated signal transduction in green plants. It has been a target protein for crop improvement because of performing a key step in photorespiration that causes the energy losses. In human, GOX is involved in the production of oxalate, which is a key metabolite in the formation of kidney stone. Here, we report the first apo-GOX structure and its complex structure with cofactor FMN from Nicotiana benthamiana by X-ray crystallography. The binding of FMN induces a pronounced conformational change of GOX tetramer. Interestingly, a conserved pH sensor found among different species might directly regulate the binding of FMN and the enzyme activity. Combined with enzymatic experiments and biophysical analyses, we provide new insights in the molecular mechanism of regulating GOX biological activity reversibly and new methods of agricultural production and clinical application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Simple Scattering: Lipid nanoparticle structural data repository

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

Identification and interpretation of tectonic features from ERTS-A imagery

The author has identified the following significant results. A set of criteria characterizing known mineralized areas are: (1) occurrence at structural bends, discontinuities, complex deformations along fault zones and intersections; (2) complex terrain textures produced by fracture intersections; (3) color and tone anomalies produced by igneous intrusives, alteration effects, and oxidation. Significant fracture systems which appear to be most commonly related to mineralization in central and northeastern Nevada trend northeast-southwest, north-south, and north-northwest. In the area from Goldfield and Beatty, Nevada, to south of Las Vegas, ERTS-1 and Skylab imagery have been examined together to study an apparent correlation of mineralized areas with suspected hydrothermal alteration effects. It was observed that areas of mineralization are of complex structure, usually cut by many fractures, and sometimes have a similar mottled or variegated appearance. Although the Beatty-Rhyolite mining district is now largely inactive, several analogous areas not known to be mineralized have been identified. Identification of specific mineral prospects within general targets requires extensive field work and detailed geophysical exploration.

Abdel-Gawad, M.↗
NASA NTRS1980

A study of the cleft region using synoptic ionospheric plasma data obtained by the polar orbiting satellites Aeros-B and Isis-2

The concentrations of O(+) and NO(+) in the dayside high-latitude cleft region of the ionosphere are investigated based on synoptic particle and plasma measurements obtained by the polar orbiting Aeros-B and Isis-2 satellites. At a time when the orbital planes of the satellites are almost at right angles to each other, three maxima in ion temperature are observed, with two of them accompanied by an increased electron temperature and electron density irregularities, and the density of the molecular ions NO(+) and O2(+) is found to increase at the expense of O(+) density. Results are discussed in terms of a theory relating perpendicular electric fields to oxygen atom reaction rates. Systematic analysis of the Aeros data base reveals 14 additional instances of O(+) to NO(+) conversion, with a large variety of forms and structures reflecting the complex structure and dynamics of the high-latitude dayside ionosphere.

Kist, R.↗
DOE OSTI2015

Synthesis of 2-deoxy-2,2-difluoro-α-maltosyl fluoride and its X-ray structure in complex with Streptomyces coelicolor GlgEI-V279S

Streptomyces coelicolor (Sco) GlgEI is a glycoside hydrolase involved in α-glucan biosynthesis and can be used as a model enzyme for structure-based inhibitor design targeting Mycobacterium tuberculosis (Mtb) GlgE. The latter is a genetically validated drug target for the development of anti-Tuberculosis (TB) treatments. Inhibition of Mtb GlgE results in a lethal buildup of the GlgE substrate maltose-1-phosphate (M1P). However, Mtb GlgE is difficult to crystallize and affords lower resolution X-ray structures. Sco GlgEI-V279S on the other hand crystallizes readily, produces high resolution X-ray data, and has active site topology identical to Mtb GlgE. In this article, we report the X-ray structure of Sco GlgEI-V279S in complex with 2-deoxy-2,2-difluoro-α-maltosyl fluoride (α-MTF, 5) at 2.3 Å resolution. α-MTF was designed as a non-hydrolysable mimic of M1P to probe the active site of GlgE1 prior to covalent bond formation without disruption of catalytic residues. The α-MTF complex revealed hydrogen bonding between Glu423 and the C1F which provides evidence that Glu423 functions as proton donor during catalysis. Further, hydrogen bonding between Arg392 and the axial C2 difluoromethylene moiety of α-MTF was observed suggesting that the C2 position tolerates substitution with hydrogen bond acceptors. The key step in the synthesis of α-MDF was transformation of peracetylated 2-fluoro-maltal 1 into peracetylated 2,2-difluoro-α-maltosyl fluoride 2 in a single step via the use of Selectfluor ® .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Synthesis, Structure, and Complex Magnetism of MIr 2 In 8 (M = Eu, Sr)

In this paper, we report the synthesis, crystal structure, and physical properties of two new polar intermetallic compounds, EuIr 2 In 8 and SrIr 2 In 8 . Both were synthesized in good yield using In metal as a reactive flux medium, enabling the growth of large crystals for physical property measurements. They crystallize in the orthorhombic space group Pbam with the CeFe 2 Al 8 structure type, which is sometimes also referred to as the CaCo 2 Al 8 structure type. The two analogues have unit cell parameters of a = 13.847(3) Å, b = 16.118(3) Å, and c = 4.3885(9) Å for M = Eu and a = 13.847(3) Å, b = 16.113(3) Å, and c = 4.3962(9) Å for M = Sr at room temperature. SrIr 2 In 8 is a diamagnetic metal with no local magnetic moments on either the Sr or Ir sites, and the diamagnetic contribution from core electrons overwhelms the expected Pauli paramagnetism normally seen in intermetallic compounds. Magnetism in EuIr 2 In 8 is dominated by the local Eu moments, which order antiferromagnetically at 5 K in low applied fields. Increasing the field strength depresses the magnetic ordering temperature and also induces a spin reorientation at lower temperature, indicating complex competing magnetic interactions. Finally, low-temperature heat capacity measurements show a significant enhancement of the Sommerfeld coefficient in EuIr 2 In 8 relative to that in SrIr 2 In 8 , with estimated values of γ = 118(3) and 18.0(2) mJ mol –1 K –2 , respectively.

36 MATERIALS SCIENCE↗