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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

TRACER-VOC: Speciated Volatile Organic Compounds at the Tracking Aerosol Convection Interactions Experiment Field Campaign Report

Formation of secondary organic aerosol (SOA) is responsible for the growth of particles into cloud condensation nuclei (CCN)-relevant sizes and accounts for a substantial fraction of aerosol mass in the troposphere. Despite its wide abundance and importance, capturing SOA in regional and climate models is still challenging due to complex chemical transformations involved in production of SOA. Oxidation products of biogenic hydrocarbons, such as isoprene, monoterpenes, and sesquiterpenes account for the largest fraction of SOA mass over the continents. As such, biogenic SOA is an important source of continental CCN, and has been recently shown to nucleate ice in the deposition mode. To better account for the processes involved in SOA formation in global and regional climate models, we need better measurements of sources and transformations of volatile organic compounds (VOCs), the precursors to SOA formation in the atmosphere. Understanding aerosol-deep convection interactions is an important science driver of the Tracking Aerosol Convection Interactions Experiment (TRACER), requiring a characterization of the critical controls on aerosol properties and processes, and how those properties and processes vary spatially and temporally in the Houston area. In that environment, the atmospheric photo-oxidative chemistry that drives the evolution and transformation of aerosol properties and processes is strongly impacted by the emission of VOCs, which have both anthropogenic (e.g., industrial, urban, transportation, biomass burning) and biogenic (e.g., marine) sources in the region. Understanding (and disentangling) the complex role of VOCs in such a heterogenous region requires high-time-resolution, speciated VOC measurements that are spatially resolved. To meet these measurement requirements, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s time-of-flight proton transfer reaction-mass spectrometer (ToF-PTR-MS) was deployed during the TRACER intensive operational period (IOP) from June to September, 2022 at the ancillary site (ANC), which was sited to provide a characterization of the rural atmospheric environment in contrast to the main site.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Interim Measures Pilot Study Completion Report, South Repeater Building, Solid Waste Management Unit 121, Hydraulic Containment and Groundwater Treatment System for Per- and Polyfluoroalkyl Substances

This Per- and Polyfluoroalkyl Substances (PFAS) Interim Measures Pilot Study Completion Report was prepared for the National Aeronautics and Space Administration by AECOM Technical Services, Inc. under Contract 80KSC019D0010, Task Order 80KSC021F0096. The purpose of this report is to document the pilot study activities at the South Repeater Building, Solid Waste Management Unit (SWMU) 121. This report details the additional assessment activities completed from June 2023 through November 2023 and the subsequent pilot study completed at the South Repeater Building conducted in December 2023 through February 2024. Initial activities were conducted in accordance with the Pilot Study Work Plan (NASA 2023), submitted to the KSC Remediation Team and accepted by the team on October 26, 2023. The objectives of the pilot study were to: • Provide information on aquifer characteristics and to aid in future modeling and remedial design activities to mitigate off-KSC migration of PFAS compounds. • Develop information on characteristics of the surficial aquifer system, specifically transmissivity, storage coefficient, hydraulic conductivity, and vertical hydraulic conductivity. • Obtain data to support construction and calibration of a groundwater flow model. • Acquire design parameters necessary for future remedial design activities, specifically radius of influence, drawdown, flow rates, and pump settings. The following activities were completed to meet the pilot study objectives: • Groundwater and surface water sampling • Geophysical investigation via Hydraulic Profiling Tool (HPT) and soil sampling • Installation of observation and extraction wells via rotosonic techniques • Slug, step, and pump testing activities • Initial modeling efforts for a groundwater hydraulic containment system. Results of the pilot study and modeling activities are being used in the development of a groundwater containment and treatment system for the site.

PFAS↗
DOE OSTI2023

Analysis of degradation products in thermally treated TATB

Delineating the chemical composition of TATB (1,3,5-triamino-2,4,6-trinitrobenzene) residues produced from the exposure to abnormal thermal environments should lead to a better understanding of the decomposition paths. Identifying and quantifying each compound in thermally produced residues, monitors which compounds are degrading or forming along the decomposition route, as well as providing input for the kinetic models of those pathways. Here, in this paper, we report the methodology of isolating, identifying, and where possible, quantifying soluble compounds present in solid residues of thermally treated TATB (330 °C for tens of minutes). Samples were extracted with DMSO, separated using chromatography, and quantified using their absorption at 354 nm. Identification of unknown compounds was accomplished using high resolution mass spectrometry. TATB, F1 (diamino-dinitro-benzofurazan), HO-TATB (2,4,6-triamino-1-hydroxyl-3,5-dinitrobenzene), and T4A (1-chloro-3,5-dinitro-2,4,6-triaminobenzene) were trace compounds detected in the unreacted TATB. Ten more compounds that formed in the residues were structurally identified including F2 (amino-nitro-difurazan). Several more compounds were observed but not completely identified. We propose possible structures for the unknowns. Of the compounds formed, F1 was the most abundant compound reaching 4.5 % by weight of the degraded solid sample. Other degradation compounds were estimated to sum to trace levels, well below 1 %. Most compounds were new, having not been detected and identified in previous studies of production grade and thermally aged TATB. Many compounds only reached detectable concentrations after several min of thermal exposure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phase Diagram of the Easy-Axis Triangular-Lattice 𝐽 1 −𝐽 2 Model

Our work studies the largely unexplored phase diagram of the S = 1/2 easy-axis triangular-lattice J 1 -J 2 model, motivated by recent interest in rare-earth and transition-metal compounds that exhibit strong quantum fluctuations and broad spin excitation continua—hallmarks of spin-liquid behavior. Combining density-matrix renormalization group simulations with analytical insights, we present a comprehensive study of the model that describes these materials. Our results provide compelling evidence for a robust spin-liquid phase stabilized between the exotic supersolid Y phase and the collinear stripe phase, remarkably persistent even in the presence of symmetry-breaking anisotropy. Additionally, we analyze the supersolid Y phase, offering a quantitative characterization of its order parameters and clarifying the surprising absence of a ferromagnetic moment; both features relevant to recent experiments on Ising-like triangular-lattice magnets. Altogether, our work provides the necessary framework and important theoretical guidance to the ongoing searches of the spin liquid and other exotic states in the rare-earth and transition-metal triangular-lattice compounds, as well as for advancing the understanding of anisotropic-exchange magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Precipitation trends determine future occurrences of compound hot–dry events

Compound hot–dry events—co-occurring hot and dry extremes—frequently cause damages to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Strong increases in the occurrence of these events are projected with warming, but associated uncertainties remain large and poorly understood. Here, using climate model large ensembles, we show that mean precipitation trends exclusively modulate the future occurrence of compound hot–dry events over land. This occurs because local warming will be large enough that future droughts will always coincide with at least moderately hot extremes, even in a 2°C warmer world. By contrast, precipitation trends are often weak and equivocal in sign, depending on the model, region and internal climate variability. Therefore, constraining regional precipitation trends will also constrain future compound hot–dry events. These results help to assess future frequencies of other compound extremes characterized by strongly different trends in the drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Asymptotic Relaxation of Moment Equations for a Multi-species, Homogeneous BGK Model

Multi-species BGK models describe the dynamics of rarefied gases with constituent particles of different elements or compounds with potentially nontrivial velocity distributions. Here, in this paper, moment equations for the bulk velocities, energies, and temperatures of a spatially homogeneous multi-species BGK model are examined. A key challenge in analyzing these equations is the fact that the collision frequencies are allowed to depend on the species temperatures, which allows for more realistic simulations of dilute gas flow. Therefore, a positive lower bound is established for the species temperatures. With this lower bound, a global existence and uniqueness of solutions to the coupled velocity-energy ODE system is established. The lower bound also enables a proof of exponential decay to a unique steady-state solution. Numerical results are presented to demonstrate how the bulk velocities and temperatures relax for large times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Perfect short-range ordered alloy with line-compound-like properties in the ZnSnN 2 :ZnO system

We present a new solid-state material phase which is a disordered solid solution but offers many ordered line-compound features. The emergent physical phenomena are rooted in the perfect short-range order which conserves the local octet rule. We model the dual-sublattice-mixed semiconductor alloy (ZnSnN 2 ) 1-x (ZnO) 2x using first-principles calculations, Monte-Carlo simulations with a model Hamiltonian, and an extension of the regular solution model by incorporating short-range order. We demonstrate that this unique solid solution, occurring at a “magic” composition, can provide an electronically pristine character without disorder-induced charge localization and, therefore, a superior carrier transport similar to ordered phases. Interestingly, this phase shows singularities that are absent in the conventional solid-solution models, such as the regular solution and band-gap bowing model. Thermodynamically, this alloy phase has a sharply reduced enthalpy at its composition (like a line compound), but it still requires the entropy from long-range disorder to be stabilized at experimentally accessible temperatures.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Is the Martian lithosphere sulfur rich

Several models are proposed to explain the abundance of sulfurand chlorine-containing compounds in the Martian fines. Preliminary analyses attributed the S and Cl enrichment to the upward migration of soluble salt. Alternative possibilities are examined, including remnant primitive lithosphere, a primitive component in the regolith, and trapping of volcanic gases. The alternative models encompass various features of the accretion chemistry thermal history, and core size-mass relationships that have been proposed for Mars.

Clark, B. C.↗
DOE OSTI2024

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Building a predictive model for polycyclic aromatic hydrocarbon dosimetry in organotypically cultured human bronchial epithelial cells using benzo[ a ]pyrene

The airway epithelium is a primary route of exposure for inhaled toxicants, and organotypic culture models represent an important advancement for toxicity testing compared to simple in vitro models that may lack metabolic capability and multicellular structure/communication associated with the bronchial epithelium in vivo. A quantitative understanding of chemical dosimetry is key for interpreting and extrapolating study results; however, dosimetry is understudied in organotypic models limiting ability to predict toxicity. We developed a dosimetry model for primary human bronchial epithelial cells (HBECs) cultured at the air-liquid interface (ALI) using benzo[a]pyrene (BAP), a representative polycyclic aromatic hydrocarbon. Dose and time course evaluation of metabolite formation and enzyme activity and expression were utilized to parameterize a cellular dosimetry model to improve the utility of ALI-HBECs for assessing chemical risk. Dosimetry analysis demonstrated absorption of BAP into cells and an increase in Phase 1 and 2 metabolites over time that correlated with regulation of metabolizing enzymes. BAP was cleared from cells by 48 h after exposure, and the primary metabolites generated in ALI-HBECs were BAP-3-phenol, BAP-4,5-dihydrodiol, BAP-7,8-dihydrodiol, BAP-9,10-dihydrodiol, BAP-7,8,9,10-tetrol, BAP-3-phenol-glucuronide, BAP-4,5-dihydrodiol-glucuronide, and BAP-9,10-dihydrodiol-glucuronide. The resulting dosimetry model described BAP and 7,8-dihydrodiol toxicokinetics in ALI-HBECs and suggested active excretion of 7,8-dihydrodiol. Overall, this study demonstrates metabolic competency of ALI-HBECs for BAP metabolism, demonstrates the usefulness of complex in vitro systems for human-relevant toxicity data, and exhibits how in silico models can be utilized for understanding the dosimetry of test compounds to aid in in vitro to human extrapolation of toxicity data for risk assessments.

Benzo[a]pyrene↗
NASA NTRS1991

Fluid dynamics of multiphase flow in volcanic environments on the Earth, Io, and other planets

Data was compiled on H2O, CO2, S, and SO2 and it was analyzed by various techniques to prepare phase diagrams relevant to conditions on the Earth, Io, Mars, and other planets. The use of Cray models for volcanic eruptions was examined. Attempts are being made to develop equations of state of the compounds listed above to be compatible with the Cray code so that they can be called as subroutines for the volcanic models. This is a major project, as the current code uses only the equation of state for a perfect gas, and requires that the atmosphere be the same composition as the volcanic gas. The analysis of the thermodynamic data is time consuming, and was complicated by changes from a VAX/VMS system to Macintosh's, Crays using CTSS, Crays using Unicos, and Sun workstations.

Kieffer, Susan Werner↗
DOE OSTI2020

Quantification of CO 2 Mineralization at the Wallula Basalt Pilot Project

In 2013, the Pacific Northwest National Laboratory led a geologic carbon sequestration field demonstration where ~1000 tonnes of CO 2 was injected into several deep Columbia River Basalt zones near Wallula, Washington. Rock core samples extracted from the injection zone two years after CO 2 injection revealed nascent carbonate mineralization that was qualitatively consistent with expectations from laboratory experiments and reactive transport modeling. In this study, we report on a new detailed analysis of the 2012 pre-injection and 2015 post-injection hydrologic tests that capitalizes on the difference in fluid properties between scCO 2 and water to assess changes in near-field, wellbore, and reservoir conditions that are apparent approximately two years following the end of injection. This comparative hydrologic test analysis method provides a new way to quantify the amount of injected CO 2 that was mineralized in the field test. Modeling results indicate that approximately 60% of the injected CO 2 was sequestered via mineralization within two years, with the resulting carbonates occupying ~4% of the available reservoir pore space. The method presented here provides a new monitoring tool to assess the fate of CO 2 injected into chemically reactive basalt formations but could also be adapted for long-term monitoring and verification within more traditional subsurface carbon storage reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hierarchical excitations from correlated spin tetrahedra on the breathing pyrochlore lattice

The hierarchy of the coupling strengths in a physical system often engenders an effective model at low energies where the decoupled high-energy modes are integrated out. Here, using neutron scattering, we show that the spin excitations in the breathing pyrochlore lattice compound CuInCr 4 S 8 are hierarchical and can be approximated by an effective model of correlated tetrahedra at low energies. At higher energies, intratetrahedron excitations together with strong magnon-phonon couplings are observed, which suggests the possible role of the lattice degree of freedom in stabilizing the spin tetrahedra. Finally, our work illustrates the spin dynamics in CuInCr 4 S 8 and demonstrates a general effective-cluster approach to understand the dynamics on the breathing-type lattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
DOE OSTI2020

A comparison of computational models for predicting yield sooting index

Sooting propensity, a measurement of how much particulate matter is produced when a fuel is burned, is a property of significant interest among researchers who are striving to discover the next generation of cleaner, more efficient fuels and fuel additives. Many compounds are not viable as fuels and/or fuel additives, and as a result, designing cleaner-burning biofuels using only experimental techniques is inefficient. Predictive models have been instrumental in reducing this inherent difficulty, providing researchers with a tool to preemptively screen compounds before production and testing. The present work compares the accuracies and interpretabilities of existing models used to predict a particular measure of sooting propensity, Yield Sooting Index (YSI). These models include artificial neural networks, graph neural networks, and multivariate equations. A novel equation for predicting YSI based on atom path count and bond order is proposed, which can highlight key structural components that contribute to YSI. It was found that artificial neural networks slightly outperform graph neural networks and greatly outperform multivariate equations in blind (test set) prediction accuracy; however, graph neural networks and multivariate equations provide significantly more interpretability as to how compound structure relates to YSI. Predictions of YSI are compared to experimental measurements for previously un-tested compounds with cetane numbers comparable to diesel fuel (50-60) (butyl decanoate, ethyl decanoate, 1,4-bis(ethenoxymethyl)cyclohexane, and 5-heptyloxolan-2-one), and it was found that these compounds produce significantly less soot compared to diesel fuel.

09 BIOMASS FUELS↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS1999

The prebiotic synthesis of modified purines and their potential role in the RNA world

Modified purines are found in all organisms in the tRNA, rRNA, and even DNA, raising the possibility of an early role for these compounds in the evolution of life. These include N6-methyladenine, 1-methyladenine, N6,N6-dimethyladenine, 1-methylhypoxanthine, 1-methylguanine, and N2-methylguanine. We find that these bases as well as a number of nonbiological modified purines can be synthesized from adenine and guanine by the simple reaction of an amine or an amino group with adenine and guanine under the concentrated conditions of the drying-lagoon or drying-beach model of prebiotic synthesis with yields as high as 50%. These compounds are therefore as prebiotic as adenine and guanine and could have played an important role in the RNA world by providing additional functional groups in ribozymes, especially for the construction of hydrophobic binding pockets.

NASA Discipline Exobiology↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗