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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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NASA NTRS2017

Mars Target Encyclopedia: Information Extraction for Planetary Science

We created a new reference database called the Mars Target Encyclopedia (MTE) that contains compositional information about science targets (usually rocks or soils) on Mars. Users can search for all targets that contain an element (e.g., “calcium”) or mineral (e.g., “hematite”) and see a map view of their spatial locations (see Fig. 1). Clicking on a search result or searching for a specific target of interest (e.g., “Dillinger”) brings up a page that compiles previous findings about its composition (see Fig. 2). The information that populates the MTE was mined from the planetary science literature using information extraction technology. All information in the MTE is linked to the source publication from which it was extracted, so users can easily browse the full context in the original document (Fig. 2).

Singh, K.↗

NASA NTRSDate not supplied

Demonstration of a Lunar Water Extraction and Quantification Technique in a Relevant Environment

The presence of water ice in permanently shadowed regions on the lunar surface may enable a sustained human presence on the Moon with minimal need for consumables. The first step toward utilizing lunar water ice to advance human space exploration will be to determine the abundance, accessibility, and distribution of this valuable resource. This paper will describe the most recent test results from the Light Water Analysis and Volatile Extraction (Light WAVE) system that was designed to capture icy regolith samples acquired from a drill. Regolith samples are then weighed, sealed, and heated to release volatiles. Volatiles are captured in a volume with a known temperature and the ideal gas law is used to determine the total quantity of volatiles in the volume. The composition of the volatile mixture is determined using a mass spectrometer so that each volatile can be quantified. By quantifying the amount of water extracted from the regolith sample, and acquiring the mass of the sample, water concentration by mass can be determined. The goal of recent testing was to determine the accuracy of water quantification using a combination of ideal gas law and mass spectrometer analysis. The Light WAVE system maybe applied to future water ice investigation missions without an inherent limit to the number of samples that can be processed, and the system may also enable lunar water sample return.

Aaron J Paz↗

NASA NTRSDate not supplied

Material Property Estimation in Thin Battery Components Using Guided Wave Measurement, Experimental Dispersion Curve Extraction and Finite Element Modeling

At NASA, we are investigating nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques to detect precursors of thermal runaway failure in lithium metal based lithium ion batteries (LIB). The approach is centered on computational simulation models to guide inspection and aid in interpretation of results. Since lithium metal LIBs have combinations of solid and fluid-filled porous materials, obtaining accurate material properties is both challenging and critical for successful simulation of battery inspection. To this end, we investigated a multi-verification approach for material characterization of thin battery components. First, a laser Doppler vibrometer (LDV) was used to measure guided wave fields in thin (microns-thick) battery components subject to broadband excitation. The time-space wavefield data was converted to frequency-wave number data to extract guided wave dispersion curves. A data visualization and post processing graphics user interface (GUI) was developed at NASA to aid the data exploration and analysis. Due to the thinness of the samples, low frequency-thickness-product plate wave approximations allowed for the calculation of closed-form solutions for material elastic property estimation. These approximations were then verified by calculating the Lamb wave solutions using the previously obtained material properties. Finally, the estimated elastic material properties were implemented in a COMSOL simulation model, and dispersion curves were extracted from simulation results. The dispersion curves and derived material properties were then compared to the analysis results from the experimental data. These comparisons informed on the accuracy of the measured material properties and helped demonstrate the accuracy of the finite element analysis (FEA) computational models. This assessment will prove vital when we start simulating more complex multi-layer components and poroelastic media. This paper gives a brief background of the problem space, outlines the workflow for data analysis and verification, shows results from the workflow, and gives an overview of future plans for simulation of lithium metal LIB inspection.

Peter Juarez↗

NASA NTRS2023

An Improved Hf Vapor Etching Apparatus for Stardust Particle Extraction

Introduction: The NASA Stardust mission captured thousands of particles from the Jupiter-family comet 81P/Wild 2 in a collector composed of aluminum foil and blocks of silica aerogel [1]. To date, most Wild 2 particles available for study are relatively large and coherent particles extracted individually from the ends of hollow, carrot-shaped impact tracks produced during impact into aerogel. However, >65% of the impacting mass can be found in the ‘bulb’ of the track, including nearly all of the <1 μm size fraction [2]. This fraction contains organic-rich material and is likely to include presolar grains, representing a critical opportunity to constrain the organic and presolar inventory of primitive outer solar system materials. However, the small size and susceptibility of this fraction to melting or alteration during capture poses significant analytical challenges. Previous attempts to extract and concentrate fine-grained material from the bulb of Stardust tracks have attempted to develop techniques that efficiently destroy aerogel and leave impactor particles relatively unharmed. The low density and high porosity of silica aerogel makes it more susceptible to attack by etching with hydrofluoric acid (HF) [3] or CF4 plasma ashing [4] than collected cometary silicates. Previous studies of HF vapor etching used HF solutions varying between 5 to 49% and noted that at high concentrations and etch rates, a liquid droplet was produced according to the etching reaction 4HF + SiO2 → SiF4 (g) + 2H2O [3]. SiF4 readily decomposes into HF and silicic acid in water, which has the potential to alter the freed cometary silicates. The plasma ashing technique avoids production of a liquid droplet and minimizes damage to embedded silicates but requires specialized equipment [4]. In this abstract, we present recent improvements to the HF etching procedure with the aim of constructing an etching chamber capable of slowly etching silica aerogel using small quantities of dilute HF. The etching chamber can be assembled using readily available materials.

Stardust↗

NASA NTRSDate not supplied

Extracting Lessons of Resilience Using Machine Mining of the ASRS Database

NASA’s Aviation Safety Reporting System (ASRS) database is the world's largest repository of voluntary, confidential safety information provided by aviation's frontline personnel, including pilots, air traffic controllers, mechanics, flight attendants, dispatchers, and other members of the aviation community and the public. The database contains close to 2 million narratives, many of which describe everyday situations in which people saved the day. In these situations, people’s resilient behavior solved a problem, dealt with a malfunction, and maintained a safe operation despite a serious perturbation. To be able to extract lessons of such resilience from this large database, the use of machine learning algorithms is being explored. In this report, we describe a comparison between two such algorithms: Perilog and Word2Vec. An identical search using both programs was done on a database containing approximately 470,000 ASRS reports submitted between 1988 and 2022. The comparison reveals some of the strength and weaknesses of each algorithm as well as the challenges inherent in using such algorithms to extract lessons of resilience from the ASRS database.

resilience↗

NASA NTRS2023

Automated Fluidics Device for Extraction and Quantification of miRNA Biomarkers From Blood

Radiation Assessment DuRing Exposure And long-Duration Spaceflight (RADREADS) demonstrates space-compatible point-of-care technology for quantitative biological monitoring of blood miRNA biomarkers in response to long-term low dose radiation exposure. This individualized monitoring approach will inform targeted treatment strategies to maximize medical resource utilization by accounting for individual susceptibility to radiation-related illnesses. As human spaceflight progresses beyond Earth’s magnetic shielding, radiation exposure poses a significant risk to astronaut health and safety. Extended operation in this environment comes with an increased risk of radiation exposure, leading to higher risks of radiation sickness, cancer, central nervous system effects, and degenerative diseases. While conventional physical dosimetry techniques capture radiation dose, individualistic susceptibility to radiation damage is varied. Multiple characteristics, including age, body weight, sex, genetics, and immune status, have been found to influence radiosensitivity (Liu et al. 2011, and Bouffler 2016). This differential response necessitates individualized monitoring and targeted treatment strategies to maximize medical resource utilization; however, a practical diagnostic platform for quantifying long-term, low dose radiation-induced tissue damage does not currently exist. MicroRNAs (miRNAs) are a class of small, non-coding RNAs that regulate gene expression by mediating the degradation of messenger RNA. The levels of particular miRNAs are influenced by biological processes such as inflammation and serve as biomarkers for a variety of conditions including cancer (Singh et al. 2017). MicroRNAs are found in various bodily fluids and are amenable to collection via liquid biopsies, providing a minimally invasive and easily quantifiable readout for a variety of radiosensitive reporters. A preliminary signature of 15 spaceflight sensitive miRNA has been identified in rodent and human studies, including miR-21-5p, miR-24-3p, miR-92a-3p, miR-17-5p, miR-16a-3p, miR-34a-3p, and miR-223-3p. These targets generally increased expression with radiation dose and linear energy transfer, though variation between individuals is not yet described. Current gaps in the field include a lack of understanding of longitudinal biological responses to long-term, low dose radiation exposure and the absence of space-compatible point-of-care technology for quantitative biological monitoring. In this body of work, we aim to develop an automated bleed-to-read system to process whole blood for the detection of miRNA biomarkers in order to monitor individualistic responses to radiation exposure. This will be achieved via separating serum (or plasma) from whole blood, followed by extraction, amplification, and quantification of the miRNA using a RT-qPCR reaction. Previously, the WetLab-2 hardware enabled execution of a RT-qPCR reaction aboard ISS; however, it is a manual system that requires crew manipulation and bulky components (Parra et al. 2017). To address these issues, automated fluid handling hardware was developed for each stage of sample preparation. Extraction of total RNA is achieved by sequentially pumping reagents through an off-the-shelf nucleic acid binding column (miRNeasy Serum/Plasma Advanced Kit, Qiagen). This approach eliminates several manual pipetting and centrifuging steps and limits the use of toxic chemicals commonly found in other sample processing techniques. The resulting elution will then be automatically dispensed for RT-qPCR analysis using a compact rotary qPCR (Mic qPCR Cycler, Bio Molecular Systems) that will improve spaceflight compatibility by removing bubbles from the detection region, another challenge highlighted by WetLab-2 (Parra et al. 2017). Efforts are also being made to simplify the RT-qPCR reaction to a 1-step air-dryable mix to improve long-term reagent stability at room temperature and reduce system complexity. By automating the RT-qPCR processes via microfluidic manipulation, RADREADS will reduce crewmember hands-on time and enable the personalized detection of radiation-induced tissue damage during long duration missions. Minimally invasive, longitudinal monitoring of individual’s response to radiation exposure will inform how the physiological system responds to long-term low dose space radiation and enables development of targeted countermeasures by the medical team. Ultimately, this portable technology will require minimal technical expertise and can also be used to monitor miRNA biomarkers associated with other diseases.

Tristen Head↗

NASA NTRSDate not supplied

Manufacturing and Metal Extraction Lunar Technology (MMELT)

Extraction processes of oxygen from regolith under development include carbothermal, molten regolith electrolysis (MRE), ionic liquids, and others, all of which result in a metal slag byproduct. The separation of some of these metals from the slag byproduct have been accomplished on small scales. However, no system currently exists that can extract aluminum, from these processes at scale, to create suitable feedstocks for manufacturing. Funding for MMELT will be used to develop a demonstration of the first end-to-end process to additively manufacture aluminum wire from lunar regolith in a simulated environment.

Manufacturing↗

NASA NTRS2024

Raman spectroscopy of Ryugu particles and their extracted residues: Fluorescence background characteristics and similarities to CI chondrites

We present here an investigation of Ryugu particles recovered by the Hayabusa2 space mission and their extracted carbonaceous acid residues using Raman spectroscopy. Raman parameters of Ryugu intact grains and their acid residues are characterized by broad D (defect induced) and G (graphite) band widths, indicating the presence of polyaromatic carbonaceous matter with low thermal maturity. Raman spectra of Ryugu particles and CI (type 1) chondrites exhibit stronger laser-induced fluorescence backgrounds compared to Type 2 and Type 3 carbonaceous chondrites. The high fluorescence signatures and wide bandwidths of the D and G bands of Ryugu intact grains are similar to the Raman spectra observed in CI chondrites, reflecting the low structural order of their aromatic carbonaceous matter, and strengthening the link between Ryugu particles and CI chondrites. The high fluorescence background intensity of the Ryugu particles is due to multiple causes, but it is likely that the relative abundance of geometry-bearing macromolecular organic matter in total organic carbon contents makes a large contribution to the fluorescence intensities. Locally observed high fluorescence in the acid-extracted residues of Ryugu is due to nitrogen-bearing outlier phase. The high fluorescence signature is one consequence of the low degree of thermal maturity of the organic matter and supports evidence that the Ryugu particles have escaped significant parent body thermal metamorphism.

Ryugu particles↗

DOE OSTI2020

Metabolite, Protein, and Lipid Extraction (MPLEx): a method that simultaneously inactivates Middle East respiratory syndrome coronavirus and allows analysis of multiple host cell components following infection

Mass spectrometry (MS)-based, integrated proteomics, metabolomics, and lipidomics (collectively, multi-omics) studies provide a very detailed snap shot of virus-induced changes to the host following infection and can lead to the identification of novel prophylactic and therapeutic targets for preventing or lessening disease severity. Multi-omics studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of the three separate analytes and their subsequent informatics pipelines.

Nicora, Carrie D.↗

DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

3D printed materials, stress-strain behavior, mult↗

DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗

DOE OSTI2020

Amidoxime-functionalized materials and their use in extracting metal ions from liquid solutions

A method of producing a functionalized material that extracts metal ions from solution, the method comprising: (i) providing a precursor material having nitrile groups appended to its surface; and (ii) reacting said nitrile groups with hydroxylamine or a derivative thereof in the presence of a polar aprotic solvent at a temperature of 60-80° C. for at least 1 hour, to convert at least a portion of said nitrile groups to amidoxime and imide dioxime groups, followed by reaction with a base capable of hydrolyzing any remaining nitrile groups to carboxylic acid groups; wherein said functionalized material has a higher uranium absorption capacity than a functionalized material produced under same conditions except that the nitrile groups are reacted with hydroxylamine in only a protic solvent. The invention is also directed to functionalized materials produced by the above-described method, and methods for using the functionalized material for extracting metal ions from metal-containing solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗

DOE OSTI2020

Extracting dependencies between network assets using deep learning

A network analysis tool receives network flow information and uses deep learning—machine learning that models high-level abstractions in the network flow information—to identify dependencies between network assets. Based on the identified dependencies, the network analysis tool can discover functional relationships between network assets. For example, a network analysis tool receives network flow information, identifies dependencies between multiple network assets based on evaluation of the network flow information, and outputs results of the identification of the dependencies. When evaluating the network flow information, the network analysis tool can pre-process the network flow information to produce input vectors, use deep learning to extract patterns in the input vectors, and then determine dependencies based on the extracted patterns. The network analysis tool can repeat this process so as to update an assessment of the dependencies between network assets on a near real-time basis.

Carroll, Thomas E.↗

DOE OSTI2022

Panel-Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

deep learning↗

DOE OSTI2022

Amidoxime-functionalized materials and their use in extracting metal ions from liquid solutions

Janke, Christopher J.↗

DOE OSTI2022

Extraction of Proton and Deuteron F2 Structure Function from Inclusive Electron-Nucleon Scattering at Large Bjorken-X

The E12-10-002 experiment measured the inclusive electron-nucleon cross section at Jeferson Lab at large Q2 (up to 16 GeV2) and a large range of x (up to 0.99). The data is analyzed and the cross sections are extracted for hydrogen and deuterium. Then the F2 structure function is extracted for hydrogen and deuterium. Finally the quark-hadron duality is tested for the proton F2 structure function for the previous world data and this experiment.

Biswas, Debaditya↗

DOE OSTI2022

System and method embodiments for element extraction and separation

The present disclosure provides system and method embodiments for extracting and separating rare earth elements (REEs) from various starting materials and sources. The system and method embodiments disclosed herein facilitate efficient REE extraction, separation, and/or isolation, even when the REEs are present in a starting material at a relatively low level compared to undesirable metals co-present in the starting material. In at least some examples, the disclosed system and method embodiments may be used to recover one or more REEs from coal derived acid mine drainage.

Boukhalfa, Hakim↗