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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Fluorescent Approaches to High Throughput Crystallography

X-ray crystallography remains the primary method for determining the structure of macromolecules. The first requirement is to have crystals, and obtaining them is often the rate-limiting step. The numbers of crystallization trials that are set up for any one protein for structural genomics, and the rate at which they are being set up, now overwhelm the ability for strictly human analysis of the results. Automated analysis methods are now being implemented with varying degrees of success, but these typically cannot reliably extract intermediate results. By covalently modifying a subpopulation, less than or = 1 %, of a macromolecule solution with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of purification. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination the crystals show up as bright objects against a dark background. As crystalline packing is more dense than amorphous precipitate, the fluorescence intensity can be used as a guide in distinguishing different types of precipitated phases, even in the absence of obvious crystalline features, widening the available potential lead conditions in the absence of clear "hits." Non-protein structures, such as salt crystals, will not incorporate the probe and will not show up under fluorescent illumination. Also, brightly fluorescent crystals are readily found against less fluorescent precipitated phases, which under white light illumination may serve to obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries and by having the protein or protein structures all that show up. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons. Preliminary experiments show that the presence of the fluorescent probe does not affect the nucleation process or the quality of the X-ray data obtained.

Pusey, Marc L.↗
NASA NTRS2004

Fluorescent Approaches to High Throughput Crystallography

X-ray crystallography remains the primary method for determining the structure of macromolecules. The first requirement is to have crystals, and obtaining them is often the rate-limiting step. The numbers of crystallization trials that are set up for any one protein for structural genomics, and the rate at which they are being set up, now overwhelm the ability for strictly human analysis of the results. Automated analysis methods are now being implemented with varying degrees of success, but these typically can not reliably extract intermediate results. By covalently modifying a subpopulation, less than or = 1%, of a macromolecule solution with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of purification. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination the crystals show up as bright objects against a dark background. As crystalline packing is more dense than amorphous precipitate, the fluorescence intensity can be used as a guide in distinguishing different types of precipitated phases, even in the absence of obvious crystalline features, widening the available potential lead conditions in the absence of clear "hits." Non-protein structures, such as salt crystals, will not incorporate the probe and will not show up under fluorescent illumination. Also, brightly fluorescent crystals are readily found against less fluorescent precipitated phases, which under white light illumination may serve to obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries and by having the protein or protein structures all that show up. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons. This presentation will focus on the methodology for fluorescent labeling, the crystallization results, and the effects of the trace labeling on the crystal quality.

Pusey, Marc L.↗
NASA NTRS2004

Fluorescent Approaches to High Throughput Crystallography

X-ray crystallography remains the primary method for determining the structure of macromolecules. The first requirement is to have crystals, and obtaining them is often the rate-limiting step. The numbers of crystallization trials that are set up for any one protein for structural genomics, and the rate at which they are being set up, now overwhelm the ability for strictly human analysis of the results. Automated analysis methods are now being implemented with varying degrees of success, but these typically cannot reliably extract intermediate results. By covalently modifying a subpopulation, less than or = 1%, of a macromolecule solution with a fluorescent probe, the labeled material will add to a growing crystal as a microheterogeneous growth unit. Labeling procedures can be readily incorporated into the final stages of a macromolecules purification. The covalently attached probe will concentrate in the crystal relative to the solution, and under fluorescent illumination the crystals will show up as bright objects against a dark background. As crystalline packing is more dense than amorphous precipitate, the fluorescence intensity can be used as a guide in distinguishing different types of precipitated phases, even in the absence of obvious crystalline features, widening the available potential lead conditions in the absence of clear "bits." Non-protein structures, such as salt crystals, will not incorporate the probe and will not show up under fluorescent illumination. Also, brightly fluorescent crystals are readily found against less fluorescent precipitated phases, which under white light illumination may serve to obscure the crystals. Automated image analysis to find crystals should be greatly facilitated, without having to first define crystallization drop boundaries and by having the protein or protein structures all that show up. The trace fluorescently labeled crystals will also emit with sufficient intensity to aid in the automation of crystal alignment using relatively low cost optics, further increasing throughput at synchrotrons. This presentation will focus on the methodology for fluorescent labeling, the crystallization results, and the effects of the trace labeling on the crystal quality.

Minamitani, Elizabeth Forsythe↗
DOE OSTI2022

Bloom filter de-identifier

A bloom filter is a binary data structure that is designed to be memory efficient and able to determine whether an element is present in a set. The bloom filter is a probabilistic data structure. It can tell you whether an element, or set of elements, is likely to exist within a set. It can also tell you whether an element, or set of elements, does not exist within a set. So false positives are probable, but false negatives are not. Our novel approach utilizes the bloom filter to determine how many bigrams (two letter sequence) each value has in common. The resulting metric approximates the relative similarity of two text values.

Bleeker, Amelia↗
DOE OSTI2022

Structural Analysis Report for Sandia High Altitude Aerosol Research (SHAAR) Payloads

Sandia National Laboratories has interest in mounting enclosures to gather scientific data aboard NASA scientific balloons. This report documents the structural integrity of three separate payloads considered ‘piggybacks.’ To date, there are no design criteria for piggybacks, therefore each piggyback shall follow the same design requirements set by NASA per the Gondola Structural Design Requirements. This analysis report shall describe how each payload meets the design requirements laid out in the Gondola Design Requirements.

47 OTHER INSTRUMENTATION↗
NASA NTRS1993

Selection of experimental modal data sets for damage detection via model update

When using a finite element model update algorithm for detecting damage in structures, it is important that the experimental modal data sets used in the update be selected in a coherent manner. In the case of a structure with extremely localized modal behavior, it is necessary to use both low and high frequency modes, but many of the modes in between may be excluded. In this paper, we examine two different mode selection strategies based on modal strain energy, and compare their success to the choice of an equal number of modes based merely on lowest frequency. Additionally, some parameters are introduced to enable a quantitative assessment of the success of our damage detection algorithm when using the various set selection criteria.

Doebling, S. W.↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS1995

Space and time aliasing structure is monthly mean polar-orbiting satellite data

Monthly mean wind fields from the European Remote Sensing Satellite (ERS1) scatterometer are presented. A banded structure which resembles the satellite subtrack is clearly and consistently apparent in the isotachs as well as the u and v components of the routinely produced fields. The structure also appears in the means of data from other polar-orbiting satellites and instruments. An experiment is designed to trace the cause of the banded structure. The European Centre for Medium-Range Weather Forecast (ECMWF) gridded surface wind analyses are used as a control set. These analyses are also sampled with the ERS1 temporal-spatial samplig pattern to form a simulated scatterometer wind set. Both sets are used to create monthly averages. The banded structures appear in the monthly mean simulated data but do not appear in the control set. It is concluded that the source of the banded structure lies in the spatial and temporal sampling of the polar-orbiting satellite which results in undersampling. The problem involves multiple timescales and space scales, oversampling and under-sampling in space, aliasing in the time and space domains, and preferentially sampled variability. It is shown that commonly used spatial smoothers (or filters), while producing visually pleasing results, also significantly bias the true mean. A three-dimensional spatial-temporal interpolator is designed and used to determine the mean field. It is found to produce satisfactory monthly means from both simulated and real ERS1 data. The implications to climate studies involving polar-orbiting satellite data are discussed.

Zeng, Lixin↗
NASA NTRS1984

Evaluation of long-duration exposure to the natural space environment on graphite-polyimide and graphite-epoxy mechanical properties (A0175)

The primary objective of this experiment is to accumulate the needed operational data associated with the exposure of graphite-polyimide and graphite-epoxy material to the environments of space. The experiment will be mounted in two 3-in.-deep peripheral trays. Graphite-polyimide specimens will occupy 1 1/3 trays and the graphite-epoxy specimens will occupy two-thirds of a tray. The experiment approach requires two matched sets of specimens with traceable records that are maintained for materials processing and specimen quality. After fabrication, one set of each test specimen will be sectioned and structurally tested to serve as a data baseline. After the flight, the other set of specimens will undergo extensive measurements of mechanical properties for comparison with the original data baseline. Structural testing of the graphite-polyimide specimens will provide strength and elastic data in tension, compression, and shear. Transverse tension microcracking and crack propagation will be evaluated by photomicroscopy. Structural testing of the graphite-epoxy specimens will include verification of laminate, core, adhesive, and fatigue properties as applied to the design and analysis of the payload bay door. Microcracking and crack propagation will also be analyzed by photomicroscopy.

Powell, J. H.↗
NASA NTRS1988

The computational structural mechanics testbed architecture. Volume 1: The language

This is the first set of five volumes which describe the software architecture for the Computational Structural Mechanics Testbed. Derived from NICE, an integrated software system developed at Lockheed Palo Alto Research Laboratory, the architecture is composed of the command language CLAMP, the command language interpreter CLIP, and the data manager GAL. Volumes 1, 2, and 3 (NASA CR's 178384, 178385, and 178386, respectively) describe CLAMP and CLIP, and the CLIP-processor interface. Volumes 4 and 5 (NASA CR's 178387 and 178388, respectively) describe GAL and its low-level I/O. CLAMP, an acronym for Command Language for Applied Mechanics Processors, is designed to control the flow of execution of processors written for NICE. Volume 1 presents the basic elements of the CLAMP language and is intended for all users.

Felippa, Carlos A.↗
NASA NTRS1988

The computational structural mechanics testbed architecture. Volume 2: The interface

This is the third set of five volumes which describe the software architecture for the Computational Structural Mechanics Testbed. Derived from NICE, an integrated software system developed at Lockheed Palo Alto Research Laboratory, the architecture is composed of the command language CLAMP, the command language interpreter CLIP, and the data manager GAL. Volumes 1, 2, and 3 (NASA CR's 178384, 178385, and 178386, respectively) describe CLAMP and CLIP and the CLIP-processor interface. Volumes 4 and 5 (NASA CR's 178387 and 178388, respectively) describe GAL and its low-level I/O. CLAMP, an acronym for Command Language for Applied Mechanics Processors, is designed to control the flow of execution of processors written for NICE. Volume 3 describes the CLIP-Processor interface and related topics. It is intended only for processor developers.

Felippa, Carlos A.↗
DOE OSTI2023

Molecular-dynamics simulation methods for macromolecular crystallography

It is investigated whether molecular-dynamics (MD) simulations can be used to enhance macromolecular crystallography (MX) studies. Historically, protein crystal structures have been described using a single set of atomic coordinates. Because conformational variation is important for protein function, researchers now often build models that contain multiple structures. Methods for building such models can fail, however, in regions where the crystallographic density is difficult to interpret, for example at the protein–solvent interface. To address this limitation, a set of MD–MX methods that combine MD simulations of protein crystals with conventional modeling and refinement tools have been developed. In an application to a cyclic adenosine monophosphate-dependent protein kinase at room temperature, the procedure improved the interpretation of ambiguous density, yielding an alternative water model and a revised protein model including multiple conformations. The revised model provides mechanistic insights into the catalytic and regulatory interactions of the enzyme. The same methods may be used in other MX studies to seek mechanistic insights.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1983

Structure of the Saint Francois Mountains and surrounding lead belt, south east Missouri: Inferences from thermal IR and other data sets

Several digital data sets were registered to a common base map for southern Missouri. These registered data sets were examined in an attempt to improve our understanding of the crustal structure and the relationships between structure and Pb-Zn-Cu, Fe, Mn, and Ba ores in the area. Data included land station reading of gravity, geologic data, and Heat Capacity Mapping Mission (HCMM) images. The HCMM day infrared (IR) images of Missouri displayed linear features, which parallel a newly discovered NW-SE trending Bouguer gravity low. The structural pattern over the gravity low suggests vertical uplift of the low density crust via isostatic readjustment and consequent fracturing of the Paleozoic sedimentary cover. The presence of a gravity low is interesting because rifts are often sites of extensive mineralization. The HCMM thermal data were also examined in conjunction with digital topography, land use, and LANDSAT MSS data to understand the phenomena that produce linears on the HCMM day IR images. Finally, the kinds of information that can be extracted from day-night IR pairs are discussed.

Arvidson, R. E.↗
DOE OSTI2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗
DOE OSTI2021

A D-term Modeling Code (DMC) for Simultaneous Calibration and Full-Stokes Imaging of Very Long Baseline Interferometric Data

In this paper we present DMC, a model and associated tool for polarimetric imaging of very long baseline interferometry data sets that simultaneously reconstructs the full-Stokes emission structure along with the station-based gain and leakage calibration terms. DMC formulates the imaging problem in terms of posterior exploration, which is achieved using Hamiltonian Monte Carlo sampling. The resulting posterior distribution provides a natural quantification of uncertainty in both the image structure and the data calibration. We run DMC on both synthetic and real data sets, the results of which demonstrate its ability to accurately recover both the image structure and calibration quantities, as well as to assess their corresponding uncertainties. The framework underpinning DMC is flexible, and its specific implementation is under continued development.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Shape restricted additive hazards models: Monotone, unimodal, and U‐shape hazard functions

We consider estimation of the semiparametric additive hazards model with an unspecified baseline hazard function where the effect of a continuous covariate has a specific shape but otherwise unspecified. Such estimation is particularly useful for a unimodal hazard function, where the hazard is monotone increasing and monotone decreasing with an unknown mode. A popular approach of the proportional hazards model is limited in such setting due to the complicated structure of the partial likelihood. Our model defines a quadratic loss function, and its simple structure allows a global Hessian matrix that does not involve parameters. Thus, once the global Hessian matrix is computed, a standard quadratic programming method can be applicable by profiling all possible locations of the mode. However, the quadratic programming method may be inefficient to handle a large global Hessian matrix in the profiling algorithm due to a large dimensionality, where the dimension of the global Hessian matrix and number of hypothetical modes are the same order as the sample size. We propose the quadratic pool adjacent violators algorithm to reduce computational costs. The proposed algorithm is extended to the model with a time‐dependent covariate with monotone or U‐shape hazard function. In simulation studies, our proposed method improves computational speed compared to the quadratic programming method, with bias and mean square error reductions. We analyze data from a recent cardiovascular study.

Mathematical & Computational Biology↗
DOE OSTI2023

Smectite phase separation is driven by hydration-mediated interfacial charge

Smectite clay minerals have an outsize impact on the response of clay-rich media to common stimuli, such as hydration and ion exchange, motivating extensive effort to understand behaviors resulting from these processes such as swelling and exfoliation. Smectites are common and historic systems for investigating colloidal and interfacial phenomena, with two swelling regimes commonly identified across myriad clays: osmotic swelling at high water activity and crystalline swelling at low water activity. However, no current swelling model seamlessly spans the full ranges of water, salt and clay content encountered in natural or engineered settings. Here, we show that structures previously rationalized as either osmotic or crystalline coexist as a rich array of distinct colloidal phases that differ by water content, layer stacking thickness, and curvature. We present an analytical model for intermolecular potentials among water, salt and clay in both mono- and divalent electrolytes that predicts swelling pressures across high and low water activities. Our results indicate that all clay swelling is osmotic swelling, but that the osmotic pressure of charged mineral interfaces becomes attractive and dominates that of the electrolyte at high clay activities. Global energy minima are often not reached on experimental timescales due to many local energy minima that promote long-lived intermediate states with vast differences in clay, ion, and water mobilities, leading to hyperdiffusive layer dynamics driven by variable hydration-mediated interfacial charge.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

New precious metal containing normal spinels: LiRhRu 1-x Ir x O 4 , LiFeIr 1-x Ru x O 4 , and LiCoIr 1-x Ru x O 4

Large spin-orbit-coupled cations in geometrically frustrated crystal structures have the most suitable setting for exploring novel exotic states of matter. Spinel oxides (AM 2 O 4 ) are well-known examples of geometrically frustrated systems. In this study, we report for the first time the synthesis of compositions LiRhRu 1-x Ir x O 4 (x = 0–0.5), LiFeIr 1-x Ru x O 4 (x = 0–0.5), and LiCoIr 1-x Ru x O 4 (x = 0–0.3) containing precious metal cations on edge-sharing octahedral M-sites, and systematically investigate their magnetic and electrical properties. 57 Fe Mössbauer spectroscopy revealed that iron is trivalent in all LiFeIr 1-x Ru x O 4 solid solutions. Magnetic measurements indicate deviations from theoretical spin-only magnetic moment values, indicating the influence of spin-orbit coupling owing to the presence of 4d and 5d block elements. The LiFeIr 1-x Ru x O 4 series shows spin-glass-like freezing behavior with T g ≈ 20 K, and a small frustration index (f ≈ 1-2), indicating that the frustration originates from site disorder. LiRhRu 1-x Ir x O 4 and LiCoIr 1-x Ru x O 4 exhibit strongly geometrically frustrated magnetism. Electrical resistivity measurements as a function of temperature indicate that all phases are semiconducting. Seebeck coefficient measurements show that LiRhRu 1-x Ir x O 4 and LiFeIr 1-x Ru x O 4 are p-type semiconductors with holes as the major charge carriers. A sign reversal of the Seebeck coefficient indicates both holes and electrons as carriers for LiCoIr 1-x RuxO 4 (x = 0–0.2), but only holes as major carriers for x = 0.3. Here, the Seebeck coefficient and power factor increase drastically in the LiRhRu 1-x Ir x O 4 solid solution with Ir substitution, reaching a maximum of ≈ +125 μV/K and ≈2.3×10 -6 W/mK 2 at ∼650 K for x = 0.5.

Charge carriers↗