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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Supporting flight data analysis for Space Shuttle Orbiter experiments at NASA Ames Research Center

The space shuttle orbiter experiments program is responsible for collecting flight data to extend the research and technology base for future aerospace vehicle design. The infrared imagery of shuttle (IRIS), catalytic surface effects, and tile gap heating experiments sponsored by Ames Research Center are part of this program. The software required to process the flight data which support these experiments is described. In addition, data analysis techniques, developed in support of the IRIS experiment, are discussed. Using the flight data base, the techniques provide information useful in analyzing and correcting problems with the experiment, and in interpreting the IRIS image obtained during the entry of the third shuttle mission.

Green, M. J.↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2026

Spatiotemporal Dynamics of the Relative Abundance of Soil Nutrient‐Degrading Enzyme‐Encoding Genes Across Continental US Ecoregions

Understanding the spatiotemporal patterns in the relative abundance of soil extracellular enzyme‐encoding genes is critical for predicting microbial responses to environmental change and their potential role in nutrient cycling. Yet, integrating novel metagenomic observations with spatiotemporal environmental gradients to infer regional patterns and future trajectories has remained unclear. To address this gap, we applied a machine learning (ML) approach, integrating soil metagenomic data with environmental variables—soil properties, topography, vegetation, and climate—to predict the relative abundance of enzyme‐encoding genes for soil carbon (C), nitrogen (N), and phosphorus (P) across surface soils of the continental United States. We assessed potential responses under future emission scenarios (SSP2‐4.5 and SSP5‐8.5) by comparing a baseline (1985–2014) to a future period (2071–2100). The ML model explained 57%–63% of baseline variation. Precipitation was identified as the most influential factor for the relative abundance of C‐ and N‐degrading enzyme‐encoding genes, while slope length, representing horizontal distance that water can travel downslope, was the primary driver for P‐degrading enzyme‐encoding genes abundance. Projections revealed spatially heterogeneous shifts across continental US ecoregions: the relative abundance of C‐ and N‐degrading enzyme‐encoding genes decreased in wetter ecoregions and increased in drier ecoregions under future climate, while P‐degrading enzyme‐encoding genes abundance decreased significantly in semiarid and Mediterranean ecoregions. This study demonstrates the utility of metagenomic data for mapping soil genetic potential and predicting its regional response to environmental change, to inform ecosystem management strategies.

extracellular enzyme-encoding genes↗
DOE OSTI2020

A Comprehensive Scientific Survey of Excipients Used in Currently Marketed, Therapeutic Biological Drug Products

The steady development of biotechnology-derived therapeutic biologics over the last few decades has generated drugs that are now standard medical treatments for a range of indications. While the development of protein products has surged in recent years, the formulation and delivery of these complex molecules have relied on drug-specific studies and, in some instances, data from non-proteinaceous drug products. The commonalities, trends, and gaps in excipient technologies used to support the development of therapeutic proteins largely remain unexplored due to the drug-specific nature of many formulations. Using a comprehensive and relational database approach, we aimed to provide a scientific survey of all approved or licensed biotechnology-derived drug products with the goal of providing evidence-based information on common attributes and trending features in protein product excipients. We examined 665 formulations, and 395 unique formulations based on having unique excipients within them, that supported 211 therapeutic proteins as of June 2020. We report the prevalence of each excipient class and excipient chemical used in eight different drug types including monoclonal antibodies, antibody conjugates, cytokines and growth factors, enzymes, polypeptide hormones, pulmonary surfactants, recombinant fusion proteins, and toxins. We also report the prevalence by excipient type among all therapeutic proteins, in the context of each drug’s recommended pH range, concentration ranges for excipients, and route of administration. The results of our analyses indicate certain excipients common to monoclonal antibodies, cytokines, and polypeptide hormones. We also report on excipients unique to protein drug products, such as amino acids, solubilizers, and lyoprotectants. Overall, our report summarizes the current landscape of excipients used in marketed biotechnology-derived therapeutic biologic products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

High-throughput measurements of CO 2 permeance and solubility in ionic liquid reveal a synergistic role of ionic interactions and void fractions

The factors that govern CO 2 solubility in ionic liquids (ILs) are of great interest for the development of new materials for CO 2 capture and utilization. The cationic functional group (i.e., imidazolium, pyrrolidinium, pyridinium, etc.), alkyl chain length of cation, degree of fluorination of anion, anion size, and the void fraction in IL are known to influence CO 2 solubility. However, a comprehensive explanation of how these factors collectively affect CO 2 solubility has not been developed yet. This knowledge gap is largely attributed to the lack of CO 2 solubility data for IL structures other than imidazolium based ILs. We report here an automated high-throughput (HT) setup for the measurement of CO 2 solubility in room-temperature ILs (RTILs) combining six different anions and nine different cations for a total of 19 different specific ranges of RTILs. The HT setup first dispenses up to 200 µL of RTILs in a 96-well microtiter plate and then utilizes a robotic arm to measure cyclic voltammogram (CV) in each well using maneuverable Ag electrodes. The Cottrell analysis of the CO 2 reduction CV peak provides a direct measurement of CO 2 permeance in RTILs, which yields Henry’s constant from the estimated diffusion coefficient of CO 2 . Henry’s constants thus obtained are in very good agreement with those reported earlier. The measured CO 2 permeance and Henry’s constant of all RTILs seem to follow a first-order dependence on void fraction and a second-order dependence on electrostatic interaction between anion and cation of IL, with some synergistic dependence on the product of a void fraction and electrostatic interaction, making them two important descriptors for the design of novel ILs.

CO2 Solubility↗
DOE OSTI2024

Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only $\mathcal{O}$(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}$(n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}$(n log n) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Identification of high-dielectric constant compounds from statistical design

Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Detecting topological phase transitions in cadmium arsenide films via the transverse magnetoresistance

Topological protection against localization causes electrical transport phenomena in disordered topological materials to differ from those in topologically trivial systems. For example, a transition between a regime of weak localization to one of weak antilocalization can occur in systems such as topological insulators and topological semimetals when an external potential is applied across the system. Here, we report on the transverse magnetoresistance of thin films of cadmium arsenide, a topological non-trivial material, as we tune the electronic states and the Fermi level. We show that the appearance of weak localization and weak antilocalization sensitively reflects the relative contributions of multiple transport channels involving both gapless (massless) and gapped (massive) Dirac fermion states present in these films. Furthermore, the data is consistent with expectations of the different topological states of these films. Weak (anti-) localization phenomena can therefore serve as a probe of the types of Dirac fermions present in topological semimetals.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Identification of hidden N4-like viruses and their interactions with hosts

The N4-like viruses, which were recently assigned to the novel viral family Schitoviridae in 2021, belong to a podoviral-like viral lineage and possess conserved genomic characteristics and a unique replication mechanism. Despite their significance, our understanding of N4-like viruses is primarily based on viral isolates. To address this knowledge gap, this study has established a comprehensive N4-like viral data sets comprising 342 high-quality N4-like viruses/proviruses (144 viral isolates, 158 uncultured viruses, and 40 integrated N4-like proviruses). These viruses were classified into 97 subfamilies (89 of which are newly identified), 148 genera (100 of which are newly identified), and 253 species (177 of which are newly identified). The study reveals that N4-like viruses inhibit the polar region, oligotrophic open oceans, and the human gut, where they infect various bacterial lineages, such as Alpha/Beta/Gamma/Epsilon-proteobacteria in the Proteobacteria phylum. Although N4-like viral endogenization appears to be prevalent in Proteobacteria, it has also been observed in Firmicutes. Additionally, the phylogenetic analysis has identified evolutionary divergence within the hallmark genes of N4-like viruses, indicating a complex origin of the different conserved parts of viral genomes. Moreover, 1,101 putative auxiliary metabolic genes (AMGs) were identified in the N4-like viral pan-proteome, which mainly participate in nucleotide and cofactor/vitamin metabolisms. Of these AMGs, 27 were found to be associated with virulence, suggesting their potential involvement in the spread of bacterial pathogenicity. The findings of this study are significant, as N4-like viruses represent a unique viral lineage with a distinct replication mechanism and a conserved core genome. This work has resulted in a comprehensive global map of the entire N4-like viral lineage, including information on their distribution in different biomes, evolutionary divergence, genomic diversity, and the potential for viral-mediated host metabolic reprogramming. As such, this work significantly contributes to our understanding of the ecological function and viral-host interactions of bacteriophages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Respiration, Moisture, and Temperature in Four Distinct Lowland Panamanian Forests from 2015-2022

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that throughfall reduction (i.e. chronic drying) would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We conducted a field study in four distinct lowland Panamanian forests that vary in background rainfall and soil fertility. In each forest we constructed throughfall reduction structures that reduce moisture inputs to plots by ~50% (see methods). In drying and paired control plots we measured soil CO2 fluxes, soil moisture (volume/volume), soil temperature (°C), air temperature (°C), and forest floor biomass during wet-dry cycles (2015 – 2022). We also collected litterfall biomass and surveyed all tree species for diameter at breast height (dbh), and identified tree species in 2018 and 2019.Results:We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks. Datasets included: Datasets included here include .csv and .xls files for soil respiration (carbon dioxide flux) collected using the LiCor (see methods), air temperature °C , soil moisture (vl/vl), and soil temperature °C. Soil moisture and soil temperature were collected at 0-10cm depths using hand-held probes (see methods). Data are on ~3 week timescales from 2015-2022 with some gaps. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Throughfall-Reduction Drying Effects on Gravimetric Soil Moisture at Two Depths in Four Lowland Panamanian Forests from 2015-2022

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that drying would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We measured soil CO2 fluxes, soil moisture, soil temperature, and forest floor biomass during wet-dry cycles (2015 – 2022) in four Panamanian forests that vary in rainfall and soil fertility. We also surveyed all tree species and identified to species in 2018 and 2019.Results: We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests to 20cm depths, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks (results presented in Cusack et al. 2023). Datasets included: Datasets included here include .csv and .xls files for gravimetric soil moisture (weight/weight). Soil moisture was collected on a quarterly basis from 0-10 cm and 10-20 cm depths using hand-held constant-volume soil corers. Data are on ~3 month timescales from 2015-2022 with some gaps. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Triboelectric Nanogenerator Repeatability and Reproducibility Study

The development of triboelectric nanogenerators (TENGs) has largely focused on optimizing output performance, often at the expense of other critical research considerations such as the development of reliable technical procedures. In particular, the reliability of reported results—specifically repeatability and reproducibility—remains underexplored and is frequently limited to brief discussion within available literature. Without rigorous validation through repeatability and reproducibility studies, the credibility and broader applicability of reported findings remain uncertain. This study addresses this gap by systematically evaluating the repeatability and reproducibility of TENG performance data. Five polymer materials—Kapton, polyethylene (PE), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF)—were investigated across all pairwise combinations of 25 total combinations for the reproducibility study and three selected pairs of the 25 samples were selected for the repeatability study. For each TENG pairing, we analyzed the methodology, experimental procedures, and resulting performance data to quantify consistency and reliability. The objective of this work is to assess the validity of the collected dataset and determine whether the observed performance trends are consistent for use in future TENG design and optimization studies. Establishing reliable and reproducible data is essential for advancing the development of high-output TENG systems and ensuring their dependable implementation in practical applications.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Experimental aerodynamic heating to simulated shuttle tiles

The heat transfer to simulated shuttle thermal protection system tiles was investigated experimentally using a highly instrumented metallic thin wall tile arranged with other metal tiles in a staggered tile array. Cold-wall heating rate data for laminar and turbulent flow were obtained in the Langley 8-foot high temperature tunnel at a nominal Mach number of 7, a nominal total temperature of 3300 R, free-stream unit Reynolds number from 3.4 x 10 to the 5th power to 2.2 x 10 to the 6th power per foot, and free-stream dynamic pressure of 1.8 psia to 9.1 psia. Experimental data are presented to illustrate the effects of flow angularity and gap width on both local peak heating and overall heating loads.

Avery, D. E.↗
NASA NTRS1983

Experimental aerodynamic heating to simulated Shuttle tiles

The heat transfer to simulated Shuttle thermal protection system tiles was investigated experimentally using a highly instrumented metallic thin wall tile arranged with other metal tiles in a staggered tile array. Cold-wall heating rate data for laminar and turbulent flow were obtained in the Langley 8-Foot High-Temperature Tunnel at a nominal Mach number of 7, a nominal total temperature of 3300 deg R, free-stream unit Reynolds number from 340,000 to 2,200,000 per foot, and free-stream dynamic pressure of 1.8 to 9.1 psia. Experimental data are presented to illustrate the effects of flow angularity and gap width on both local peak heating and overall heating loads.

Avery, D. E.↗