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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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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Threat Reduction Research Networks: Fostering Sustainable Collaborations Through Trainings for Genomics for Biosurveillance

Scientific research communities can be represented as heterogeneous or multidimensional networks encompassing multiple types of entities and relationships. These networks might include researchers, institutions, meetings, and publications, connected by relationships like authorship, employment, and attendance. We describe a method for efficiently and flexibly capturing, storing, and extracting information from multidimensional scientific networks using a graph database. The database structure is based on an ontology that captures allowable types of entities and relationships. This allows us to construct a variety of projections of the underlying multidimensional graph through database queries to answer specific research questions. We demonstrate this process through a study of the U.S. Biological Threat Reduction Program (BTRP), which seeks to develop Threat Reduction Networks to build and strengthen a sustainable international community of biosecurity, biosafety, and biosurveillance experts to address shared biological threat reduction challenges. Networks like these create connectional intelligence among researchers and institutions around the world, and are central to the concept of cooperative threat reduction. Our analysis focuses on a series of seven BTRP genome sequencing training workshops, showing how they created a growing network of participants and countries over time, which is also reflected in coauthorship relationships among attendees. By capturing concept and relationship hierarchies, our ontology-based approach allows us to pose general or specific questions about networks within the same framework. This approach can be applied to other research communities or multidimensional social networks to capture, analyze, and visualize different types of interactions and how they change over time.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Turning Natural Herbaceous Fibers into Advanced Materials for Sustainability

Considering the growing concerns about natural resource depletion, energy inequality, and climate crises, biomass-derived materials—the most abundant organic matter on the planet—have received a lot of attention as a potential alternative to petroleum-based plastics. Herbaceous biomasses and extracted cellulose have recently been extensively used in the development of high-performance and multifunctional materials. Herbaceous biomass has sparked interest due to its species diversity, abundance, low cost, lightweight, and sustainability. This review discusses the structure versus property relationships of various sources of herbaceous biomasses (e.g., sugarcane, straw, and bamboo) and their extracted biomaterials, as well as the latest emerging applications from macro- and microscales to nanoscales. We report, high-strength structural materials, porous carbon materials, multichannel materials, and flexible materials are examples of these applications, which include sustainable electronics, environmentally friendly energy harvesting, smart materials, and biodegradable structural buildings.

36 MATERIALS SCIENCE↗

DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2024

δ 2 H isotopic offsets in xylem water measurements under cryogenic vacuum distillation: Quantifying and correcting wood‐water hydrogen exchange influences

Stable isotopes δ 18 O and δ 2 H are used to infer vegetation water sources. In some studies, significant xylem water δ 2 H offsets from potential source waters have been observed. The offsets appear to be more prevalent with cryogenic vacuum distillation (CVD) of plant water. Hypothesized mechanisms for these offsets include changes during plant water uptake and transport, and methodological problems. We propose that a large portion of the offsets are due to hydrogen isotope exchange between xylem water and non-crystalline hydroxyl groups of wood cellulose and hemicellulose during CVD. We present a method for estimating the hypothesized isotopic exchange between wood tissues and water, which is the result of Rayleigh and equilibrium fractionation. To estimate the exchange, we use published wood properties for North American tree species and isotope chemical relationships as a function of moisture content, CVD temperature and water extraction efficiency. A simple model of exchange between xylem water and hydroxyl groups captures the range of observations in studies in which CVD and non-CVD methods were compared. To evaluate the model, we compared observed δ 2 H offsets (sw-excess) values from two field datasets (90°C, n = 364, and 170°C, n = 43) to δ 2 H offsets estimated with our chemical model. We found good agreement between observed and estimated δ 2 H offsets for samples extracted at 90°C (r 2 = 0.69) but not for samples extracted at 170°C (r 2 = 0.20). The offset may be eliminated by increasing the extraction temperature to 229°C or by adding a standard sufficient to raise the moisture content to >150%. A correction can also be approximated by applying a theoretical calculation based on the extraction temperature, moisture content and water extraction efficiency.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1982

The lunar regolith - Chemistry, mineralogy, and petrology

The data base on the lunar regolith is surveyed to form a synthesis of the lunar regolith chemistry, mineralogy, and petrology. The data were derived from samples collected by the Apollo missions 11-17 and the Luna 16, 20, and 24 probes. The missions were sent to sample formations and areas which typified the common observed features of the lunar surface. Drive tubes were used to extract samples from beneath the surface in order to study the relationship between the regolith and the bedrock, as well as to identify the processes that formed the regolith, which is regarded as the prime source of raw materials for early lunar industrial activities. Regolith origins are now understood to be destructive processes of comminution and constructional processes of agglutinate formation. Mixing occurs on the local scale, although lateral transport is inefficient on the moon. The usual contents of the fraction of regolith less than 10 microns in diameter are Al2O3, CaO, Na2O, K2O, light REE, and Th.

Papike, J. J.↗

NASA NTRS2005

Multiscale Modeling for the Analysis for Grain-Scale Fracture Within Aluminum Microstructures

Multiscale modeling methods for the analysis of metallic microstructures are discussed. Both molecular dynamics and the finite element method are used to analyze crack propagation and stress distribution in a nanoscale aluminum bicrystal model subjected to hydrostatic loading. Quantitative similarity is observed between the results from the two very different analysis methods. A bilinear traction-displacement relationship that may be embedded into cohesive zone finite elements is extracted from the nanoscale molecular dynamics results.

Glaessgen, Edward H.↗

NASA NTRS1973

A study of morphology, provenance, and movement of desert sand seas in Africa, Asia, and Australia

The author has identified the following significant results. The most significant result to date is recongition that complexes are indeed visible on ERTS-1 images, and that the extent and nature of dune fields in many remote areas of the world can, for the first time, be observed almost simultaneously and directly compared. The primary scientific problem is to determine accurately the relationships among certain patterns, trends, and boundaries clearly revealed on ERTS-1 imagery to true surface configurations of desert landforms, and further, to relate the defined features to controlling factors such as wind direction and intensity, moisture in the air and on the ground and barriers to sand movement. It is hoped to produce, by photographic and cartographic extraction from ERTS-1 images, a set of thematic maps that will illustrate the relationships of optical appearance of various materials and effects of the color of various sand bodies upon ERTS-1 images.

Mckee, E. D.↗

DOE OSTI2020

Competing effects of soil fertility and toxicity on tropical greening

Tropical forests are expected to green up with increasing atmospheric CO 2 concentrations, but primary productivity may be limited by soil nutrient availability. However, rarely have canopy-scale measurements been assessed against soil measurements in the tropics. Here, we sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil phosphorus (P) and base cations (K, Mg) corresponded to declines in remotely sensed mean annual canopy greenness (r 2 = 0.77– 0.85; p < 0.1), controlling for precipitation. The reason for this inverse relationship appears to be that litterfall also increased with increasing soil P and cation availability (r 2 = 0.88–0.98; p < 0.1), resulting in a decline in greenness with increasing annual litterfall (r 2 = 0.94; p < 0.1). As such, greater soil nutrient availability corresponded to greater leaf turnover, resulting in decreased greenness. However, these decreases in greenness with increasing soil P and cations were countered by increases in greenness with increasing soil nitrogen (N) (r 2 = 0.14; p < 0.1), which had no significant relationship with litterfall, likely reflecting a direct effect of soil N on leaf chlorophyll content, but not on litterfall rates. In addition, greenness increased with extractable soil aluminum (Al) (r 2 = 0.97; p < 0.1), but Al had no significant relationship with litterfall, suggesting a physiological adaptation of plants to high levels of toxic metals. Thus, spatial gradients in canopy greenness are not necessarily positive indicators of soil nutrient scarcity. Using a novel remote sensing index of canopy greenness limitation, we assessed how observed greenness compares with potential greenness. We found a strong relationship with soil N only (r 2 = 0.65; p < 0.1), suggesting that tropical canopy greenness in Panama is predominantly limited by soil N, even if plant productivity (e.g., litterfall) responds to rock-derived nutrients. Moreover, greenness limitation was also significantly correlated with fine root biomass and soil carbon stocks (r 2 = 0.62–0.71; p < 0.1), suggesting a feedback from soil N to canopy greenness to soil carbon storage. Altogether, these data point to the potential utility of a remote sensing product for assessing belowground properties in tropical ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Probing air-water interfaces of dibutyl phosphoric acid (HDBP) aqueous solutions using vibrational sum frequency generation (vSFG) spectroscopy

Liquid-liquid extraction is a separation technique implemented in a wide variety of areas, achieving particular success in both the nuclear and biomedical fields. In this work, vibrational sum frequency generation spectroscopy (VSFG) and surface tension measurements were used to investigate the adsorption of dibutyl phosphate (DBP) at air-aqueous interfaces to simulate liquid-liquid systems relevant to the Plutonium Uranium Redox Extraction (PUREX) Process. The objective of this work is to establish qualitative relationships between changes in the bulk aqueous phase concentrations of DBP and its concentration and structure at air-liquid interface as probed with VSFG. Nitric acid concentration and solution ionic strength were varied to examine their effect on the interfacial DBP.. Introduction of DBP into neat water resulted in reduction of the VSFG spectral intensity in the dangling O-H region (3680 – 3800 cm -1 ) but large increase in the H-bonded O-H stretch frequency region (3000 – 3500 cm-1) and the appearance of the CH 3 symmetric stretch and CH 3 Fermi resonance peaks at ~ 2880 and 2945 cm -1 , respectively, indicating DBP at the air-water interface. The intensity of the C-H strecth peaks increased as DBP concentration increased from 0.24 to 32 mM, accompanied by a decreasing surface tension values. At fixed DBP concentration, the addition of either or both of HNO 3 and NaNO 3 to an ionic strength of 1 M or 3 M led to significant reduction of the O-H VSFG peaks and enhancement of the C-H peaks. The origins of these experimental observations are attributed to both the increased HDBP molecules partitioning and adsorption to the interface and the protonation of the interfacial DBP- molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data

This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using row-resolution (i.e., 15 minutes) smart meter data. We first demonstrate that the electricity consumption profiles on mild-temperature days can be used to approximate the base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed from the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and verify the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.

Kim, Hyeonjin↗

DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗

NASA NTRS1982

A method for the extraction and quantitation of phycoerythrin from algae

A summary of a new technique for the extraction and quantitation of phycoerythrin (PHE) from algal samples is described. Results of analysis of four extracts representing three PHE types from algae including cryptomonad and cyanophyte types are presented. The method of extraction and an equation for quantitation are given. A graph showing the relationship of concentration and fluorescence units that may be used with samples fluorescing around 575-580 nm (probably dominated by cryptophytes in estuarine waters) and 560 nm (dominated by cyanophytes characteristics of the open ocean) is provided.

Stewart, D. E.↗

DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Extracted Water Induces Concentration Fluctuations in Model Ternary Liquid–Liquid Extraction System

Organic phase aggregation and phase splitting are two important (and connected) phenomena in liquid–liquid extraction of metal ions. Previously, we demonstrated how structure in binary mixtures of extractant and diluent originates from concentration fluctuations associated with the so-called “third-phase formation” liquid–liquid phase instability. For more complex systems, we sought to understand how extracted aqueous components affect these concentration fluctuations. Here, in this study, we use small-angle X-ray scattering and molecular dynamics simulations to investigate organic phase structural changes upon contact with water in a model ternary system of water/alkane/extractant. Critical exponents were determined from scattering with temperature variation and were consistent with mean field values. Then, by controlling the water content in the organic phase through direct dissolution of fixed quantities at constant temperature, we relate the observed increase in the fluctuation length scale to changes to the spinodal temperature of the third-phase formation phase boundary. We find a roughly linear relationship between water concentration and the spinodal temperature and a similar slope in that relationship for different linear alkane diluents. This suggests that the mechanism by which water extraction impacts the organic phase structure is to enhance concentration fluctuations through the introduction of additional polar extractant-water complexes, which increases the spinodal temperature and thereby decreases the reduced temperature, i.e., the distance to the instability. This explanation for how solutes induce aggregation in liquid–liquid extraction organic phases may extend to more complex systems, explaining the large increases in correlation lengths reported under high acid and metal loading.

Bonnett, Brittany L. [Argonne National Laboratory ↗

DOE OSTI2023

Utility of Shockley–Read–Hall analysis to extract defect properties from semiconductor minority carrier lifetime data

The semiconductor minority carrier lifetime contains information about several important material properties, including Shockley–Read–Hall defect levels/concentrations and radiative/Auger recombination rates, and the complex relationships between these parameters produce a non-trivial temperature-dependence of the measured lifetime. It is tempting to fit temperature-dependent lifetime data to extract the properties of the Shockley–Read–Hall recombination centers; however, without a priori knowledge of the distribution of the Shockley–Read–Hall states across the bandgap, this fit problem is under-constrained in most circumstances. Shockley–Read–Hall lifetime data are not well-suited for the extraction of Shockley–Read–Hall defect levels but can be used effectively to extract minority carrier recombination lifetimes. The minority carrier recombination lifetime is observed at temperatures below 100 K in a Si-doped n-type InGaAs/InAsSb superlattice, and deviation from its expected temperature-dependence indicates that the capture cross section of the defect associated with Si-doping has an activation energy of 1.5 meV or a characteristic temperature of 17 K. This lower temperature regime is also preferrable for the analysis of the physics of defect introduction with displacement-damage-generating particle irradiation.

Physics↗

NASA NTRS1992

An interactive multi-block grid generation system

A grid generation procedure combining interactive and batch grid generation programs was put together to generate multi-block grids for complex aircraft configurations. The interactive section provides the tools for 3D geometry manipulation, surface grid extraction, boundary domain construction for 3D volume grid generation, and block-block relationships and boundary conditions for flow solvers. The procedure improves the flexibility and quality of grid generation to meet the design/analysis requirements.

Kao, T. J.↗

NASA NTRS2000

Thermal Field Imaging Using Ultrasound

It is often desirable to be able to determine the temperature field in the interiors of opaque fluids forced into convection by externally imposed temperature gradients. To measure the temperature at a point in an opaque fluid in the usual fashion requires insertion of a probe, and to determine the full field therefore requires either the ability to move this probe or the introduction of multiple probes. Neither of these solutions is particularly satisfactory, although they can lead to quite accurate measurements. As an alternative we have investigated the use of ultrasound as a relatively non-intrusive probe of the temperature field in convecting opaque fluids. The temperature dependence of the sound velocity can be sufficiently great to permit a determination of the temperature from timing the traversal of an ultrasound pulse across a chamber. In this paper we will present our results on convecting flows of transparent and opaque fluids. Our experimental cells consist of relatively narrow rectangular cavities made of thermally insulating materials on the sides, and metal top and bottom plates. The ultrasound transducer is powered by a pulser/receiver, the signal output of which goes to a very high speed signal averager. The average of several hundred to several thousand signals is then sent to a computer for storage and analysis. The experimental procedure is to establish a convective flow by imposing a vertical temperature gradient on the chamber, and then to measure, at several regularly spaced locations, the transit time for an ultrasound pulse to traverse the chamber horizontally (parallel to the convecting rolls) and return to the transducer. The transit time is related to the temperature of the fluid through which the sound pulse travels. Knowing the relationship between transit time and temperature (determined in a separate experiment), we can extract the average temperature across the chamber at that location. By changing the location of the transducer it is then possible to find the average temperature at different locations along the chamber, thereby determining the temperature profile along the system. (In the future we will construct an array of transducers. This will give us the capability to determine the temperature profile much more rapidly than at present, an important consideration if time-dependent phenomena are to be studied.) To validate our procedure we introduced encapsulated liquid crystal particles into glycerol. The liquid crystal particles' color varies depending on the temperature of the fluid. A photograph of the fluid through transparent sidewalls therefore gives a picture of the temperature field of the convecting fluid, independent of our ultrasound imaging. A representative result is shown in the Figure 1, which reveals a very satisfying correspondence between the two techniques. Therefore we have a great deal of confidence that the ultrasound imaging approach is indeed measuring the actual temperature profile of the fluid. The technique has also been applied to convecting liquid metal flows, and representative data will be presented from those experiments as well.

Andereck, D.↗

DOE OSTI2023

gRNA-SeqRET: a universal tool for targeted and genome-scale gRNA design and sequence extraction for prokaryotes and eukaryotes

High-throughput genetic screening is frequently employed to rapidly associate gene with phenotype and establish sequence-function relationships. With the advent of CRISPR technology, and the ability to functionally interrogate previously genetically recalcitrant organisms, non-model organisms can be investigated using pooled guide RNA (gRNA) libraries and sequencing-based assays to quantitatively assess fitness of every targeted locus in parallel. To aid the construction of pooled gRNA assemblies, we have developed an in silico design workflow for gRNA selection using the gRNA Sequence Region Extraction Tool (gRNA-SeqRET). Built upon the previously developed CCTop, gRNA-SeqRET enables automated, scalable design of gRNA libraries that target user-specified regions or whole genomes of any prokaryote or eukaryote. Additionally, gRNA-SeqRET automates the bulk extraction of any regions of sequence relative to genes or other features, aiding in the design of homology arms for insertion or deletion constructs. We also assess in silico the application of a designed gRNA library to other closely related genomes and demonstrate that for very closely related organisms Average Nucleotide Identity (ANI) > 95% a large fraction of the library may be of relevance. The gRNA-SeqRET web application pipeline can be accessed at https://grna.jgi.doe.gov. The source code is comprised of freely available software tools and customized Python scripts, and is available at https://bitbucket.org/berkeleylab/grnadesigner/src/master/ under a modified BSD open-source license (https://bitbucket.org/berkeleylab/grnadesigner).

59 BASIC BIOLOGICAL SCIENCES↗