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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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Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis.
Molecular nitrogen density and temperature measurements in thermosphere by thermosphere probe
Voyager 1 and 2 sensor data are analyzed in order to derive the composition, energy spectra, and spatial distribution of energetic ions in the Saturn magnetosphere. In order of abundance, the major species are H, H2(+), He, H3(+), C, and O. The fluxes of all species decreased inside the orbit of Dione, and nearly vanished in the 'slot' region within the orbit of Tethys. Both satellite absorption and precipitation due to pitch angle scattering may be important loss processes in that region. In the outer magnetosphere, photodissociation rapidly destroys a large fraction of the H2(+) ions, but dissociation by impact with neutral H atoms is faster for H2(+) ions in the lowest vibrational state. The ground state lifetime of about 23 days places a limit of about 10-100 days on the mean overall residence time for energetic ions in Saturn's magnetosphere.
The overall objective of the project was to combine new molecular-level characterization strategies with soil carbon flux measurements to develop and evaluate model representations of subsurface carbon cycling. We expanded on existing studies in the East River watershed, Colorado, in collaboration with the Berkeley Lab Watershed Function Scientific Focus Area (SFA), SLAC Groundwater Quality SFA, and Rocky Mountain Biological Laboratory (RMBL) to develop an elevation and vegetation gradient that is now the subject of long-term monitoring by the USGS. To achieve the overall objective, we combined field studies of soil respiration with laboratory analyses, ranging from spectroscopy to incubation studies. This combination of techniques enabled us to develop a new understanding of the drivers of high-elevation soil respiration. In the process, we developed three new modeling approaches that improve our ability to conceptualize and ultimately to represent soil respiration in numerical models. The first approach is a plot-scale transient inverse model that can be used to determine in situ CO 2 production rates from measured concentration profiles and surface fluxes. Application of this method revealed the importance of plant phenology and deep CO 2 production in moderating CO 2 fluxes to the atmosphere. The second modeling approach is a molecular-scale tool that enables spectroscopic and elemental data for carbon speciation to be transformed into functional group abundances, or the ‘SOC-fga model’. This method uniquely enables carbon speciation to be tracked within a reactive transport framework to partition carbon among different pathways and storage zones within the soil. The third modeling tool builds strongly on the previous approaches and captures the microbial processes driving heterotrophic respiration. This ‘dormancy model’ allows the native soil microbial population to respond transiently to the presence or absence of water in order to catalyze carbon respiration. This approach was also compared to the simpler and more widely used first-order model using two experimental datasets with different temporal and spatial resolutions. Our results illustrate that the simpler first order model provides a robust and efficient representation of deep (>1 meter) soil respiration, but that shallow soils, where most respiration occurs, require explicit representation of moisture-dependent activation and dormancy rates. By assessing soil organic carbon turnover at multiple scales, we see a complex array of controls emerge. Atte scale of a hillslope, spatial heterogeneity in soil respiration rates dominates and is uncorrelated with instantaneous soil moisture and plant community. At the profile scale, the balance between plant inputs and water availability is the dominant control. At the microbial to molecular scale, physiological processes associated with carbon use and carbon speciation are important controls. These scale-dependent controls emphasize the need for new modeling approaches that examine their interactions and hierarchies.
We use extensive 350 micron polarimetry and continuum maps obtained with Hertz and SHARC II along with HCN and HCO(sup +) spectroscopic data to trace the orientation of the magnetic field in the Orion A star-forming region. Using the polarimetry data, we find that the direction of the projection of the magnetic field in the plane of the sky relative to the orientation of the integral-shaped filament varies considerably as one moves from north to south. While in IRAS 05327-0457 and OMC-3 MMS 1-6 the projection of the field is primarily perpendicular to the filament it becomes better aligned with it at OMC-3 MMS 8-9 and well aligned with it at OMC-2 FIR 6. The OMC-2 FIR 4 cloud, located between the last two, is a peculiar object where we find almost no polarization. There is a relatively sharp boundary within its core where two adjacent regions exhibiting differing polarization angles merge. The projected angle of the field is more complicated in OMC-1 where it exhibits smooth variations in its orientation across the face of this massive complex. We also note that while the relative orientation of the projected angle of the magnetic field to the filament varies significantly in the OMC-3 and OMC-2 regions, its orientation relative to a fixed position on the sky shows much more stability. This suggests that, perhaps, the orientation of the field is relatively unaffected by the mass condensations present in these parts of the molecular cloud. By combining the polarimetry and spectroscopic data we were able to measure a set of average d u e s for the inclination angle of the magnetic field relative to the line of sight. We find that the field is oriented quite close to the plane of the sky in most places. More precisely, the inclination of the magnetic field is approx. = 73 deg around OMC-3 MMS 6, approx. = 74 deg at OMC-3 MMS 8-9, approx. = 80 deg at OMC-2 FIR 4, approx. = 65 deg in the northeastern part of OMC-1, and approx. = 49 deg in the Bas. The small difference in the inclination of the field between OMC-3 and OMC-2 seems to strengthen the idea that the orientation of the magnetic field is relatively unaffected by the agglomeration of matter located in these regions. We also present polarimetry data for the OMC-4 region located some 13 min. south of OMC-1.
Herein, we examine the effect of removing self-interaction error (SIE) on the calculation of molecular polarizabilities in the local spin density (LSDA) and generalized gradient approximations (GGA). To this end, we utilize a database of 132 molecules taken from a recent benchmark study [Hait and Head-Gordon, Phys. Chem. Chem. Phys. , 2018, 20 , 19800] to assess the influence of SIE on polarizabilities by comparing results with accurate reference data. Our results confirm that the general overestimation of molecular polarizabilities by these density functional approximations can be attributed to SIE. However, removing SIE using the Perdew–Zunger self-interaction-correction (PZ-SIC) method, implemented using the Fermi–Löwdin Orbital SIC approach, leads to an underestimation of molecular polarizabilities, showing that PZ-SIC overcorrects when combined with LSDA or GGA. Application of a recently proposed locally scaled SIC [Zope, et al., J. Chem. Phys. , 2019, 151 , 214108] is found to provide more accurate polarizabilities. We attribute this to the ability of the local scaling scheme to selectively correct for SIE in the regions of space where the correction is needed most.
A solar panel array with more than ten years space exposure was removed from the Mir core module in November 1997, and an eight panel section was returned to Earth in January 1998. Several solar cells were removed from panel eight of the returned array and placed in a high vacuum system with a residual gas analyzer (200 amu mass spectrometer) and a cold finger. Similar unflown solar cells of the same vintage were later obtained from Energia. Several of the unflown cells were also placed in the vacuum system and outgassed residues were collected on the LN2 cold finger. Almost 3 mg of outgassed residue was collected -from a string of three unflown solar cells over a period of 94 hours under vacuum. The collected residue was weighed with a microbalance, and then the residue was analyzed by FTIR spectroscopy, and by gas chromatograph-mass spectroscopy. About 25 outgassed constituents were separated by the gas chromatograph, and a high-resolution mass spectrum was obtained of each constituent. Molecular identifications have been made for the constituents. The constituents are primarily cyclic siloxanes, and several of the constituents are isomers of the same molecule. Most of the outgassed constituents have a molecular mass of about 500 amu. Almost one mg of residue was extracted from one sq cm of coverglass/adhesive from a flown solar cell by soaking in isopropyl alcohol for 30 minutes. The gas chromatograph separated about 20 constituents. The constituents are mostly cyclic siloxanes with linear branches, hydrocarbons, and phthalates. The typical molecular mass is about 600 amu. These identifications of specific outgassing molecules have resulted in a more complete understanding of the SiO(x) contamination on the Mir solar cell coverglasses, and on the MEEP experiment trays and optical specimens during the Shuttle-Mir Phase One flight experiment program. Adjusted outgassing rates based on the data reported here, and/or measured outgassing rates and specific molecular identifications of ISS hardware samples are needed to input into model predictions of induced environment effects of the ISS.
Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.
Using sub-3-cycle pulses from mode-locked Cr:ZnS lasers at λ ≈ 2.4 µm as a driving source, we performed high-resolution dual-frequency-comb spectroscopy in the longwave infrared (LWIR) range. A duo of highly coherent broadband (6.6–11.4 µm) frequency combs were produced via intrapulse difference frequency generation in zinc germanium phosphide (ZGP) crystals. Fast (up to 0.1 s per spectrum) acquisition of 240,000 comb-mode-resolved data points, spaced by 80 MHz and referenced to a Rb clock, was demonstrated, resulting in metrology grade molecular spectra of N 2 O (nitrous oxide) and CH 3 OH (methane). The key to high-speed massive spectral data acquisition was low intensity and phase noise of the LWIR combs and high (7.5%) downconversion efficiency, resulting in a LWIR power of 300 mW for each comb.
The high-resolution measurement capability of Fourier-transform mass spectrometry (FT-MS) has made it a necessity for exploring the molecular composition of complex organic mixtures, like soil, plant, aquatic, and petroleum samples. This demand has driven a need for informatics tools to explore and analyze FT-MS data in a robust and reproducible manner. FREDA is an interactive web application developed to enable spectrometrists to format, process, and explore their FT-MS data without the need for statistical programming expertise. FREDA was built to explore outputs from a molecular identification tool, like CoreMS, and provide a suite of methods to filter data, compute chemical properties of peaks, statistically compare samples and groups of samples, conduct exploratory data analysis, and download the results with a report detailing all steps conducted. To demonstrate the utility of FREDA, an example analysis was conducted using FT-MS data from a soil microbiology study of samples collected in two different soil depths at the Sphagnum bog forest north of Grand Rapids, Minnesota. Differences between the two depths are observed using Kendrick, Gibbs free energy, and van Krevelen plots. G-tests are used to quantify a significant difference between the groups. All analyses and plotting are conducted using only the FREDA application. FREDA is an open-source and readily available web application that allows users to explore and make statistically valid conclusions about their FT-MS data. The application is available online (https://map.emsl.pnnl.gov/app/freda) with a tutorial web series (https://youtu.be/k5HLE2kNSBY?si=yB6sGoyvzxrFf5MP) and freely accessible code on Github (https://github.com/EMSL-Computing/FREDA).
The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.
Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment the data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes.
Results from extensive molecular dynamics simulations of molten LiCl, NaCl, KCl, and RbCl over a wide range of temperatures are reported. Comparison is made between the “Polarizable Ion Model” (PIM) and the non-polarizable “Rigid Ion Model” (RIM). Densities, self-diffusivities, shear viscosities, ionic conductivities, and thermal conductivities are computed and compared with experimental data. In addition, radial distribution functions are computed from ab initio molecular dynamics simulations and compared with the two sets of classical simulations as well as experimental data. Here, the two classical models perform reasonably well at capturing structural and dynamic properties of the four molten alkali chlorides, both qualitatively and often quantitatively. With the singular exception of liquid density, for which the PIM is more accurate than the RIM, there are few clear trends to suggest that one model is more accurate than the other for the four alkali halide systems studied here.
The process of applying spectroscopy to the Space Shuttle Main Engine (SSME) for plume diagnostics, as it exists today, originated at Marshall Space Flight Center in Huntsville, Alabama, and its implementation was assured largely through the efforts of Sverdrup AEDC, in Tullahoma, Tennessee. This team continues to lead and guide efforts in the plume diagnostics arena. The process, Optical Plume Anomaly Detection (OPAD), formed the basis for various activities in the development of ground-based systems as well as the development of in-flight plume spectroscopy. OPAD currently provides and will continue to provide valuable information relative to future systems definitions, instrumentation development, code validation, and data diagnostic processing. OPAD is based on the detection of anomalous atomic and molecular species in the SSME plume using two complete, stand-alone optical spectrometers. To-date OPAD has acquired data on 44 test firings of the SSME at the Technology Test Bed (TTB) at MSFC. The purpose of this paper will be to provide an introduction to the OPAD system by discussing the process of obtaining data as well as the methods of examining and interpreting the data. It will encompass such issues as selection of instrumentation correlation of data to nominal engine operation, investigation of SSME component erosion via OPAD spectral data, necessity and benefits of plume seeding, application of artificial intelligence (AI) techniques to data analysis, and the present status of efforts to quantify specie erosion utilizing standard plume and chemistry codes as well as radiative models currently under development.
The possibility of using molecular nitrogen gas additive as an energy storage which in turn produces cesium ionization in advanced thermionic converter is first described theoretically. Experimental data confirmed for the first time such cesium ionization by vibrationally excited molecular nitrogen. Based on these experimental observations, the use of this scheme for the verification of validity of the recent Lam's theory and its application for the development of an advanced flowing thermionic converter are discussed.
A map of the Galactic center region in the forbidden Si II 34.8-micron line is presented. The line emission arises from within the photodissociation region (PDR) associated with the neutral gas ring surrounding an ionized gas core confined within 2 pc of the Galactic center. Si II is a useful probe of the inner regions of the ring since it is always optically thin. The Si II data, when analyzed in conjunction with O I, C II, and molecular measurements, outlines the transition region between the PDR and the surrounding molecular cloud. The Si II emission is found to extend beyond that of the O II into the neutral gas ring. Although the interpretation is not unique, the data are consistent with a constant gas-phase abundance of silicon within the inner part of the PDR while the gaseous silicon is depleted by molecule formation in the transition region.
This repository enables fine-tuning of the HydraGNN Predictive GFM 2026 — an open-source ensemble of pre-trained graph foundation models for atomistic materials modeling, developed at Oak Ridge National Laboratory. The GFM 2026 is freely available and downloadable via Globus from the OLCF Data Constellation (DOI: 10.13139/OLCF/2562660). Starting from these pre-trained weights, this repository provides a complete transfer learning pipeline for adapting the GFM ensemble to domain-specific molecular and materials property prediction tasks. It includes: 1) Utilities for ensemble fine-tuning with task-specific output heads 2) Example pipelines for eight widely-used materials and molecular datasets 3) Tools for model adaptation and head configuration 4) Data preprocessing utilities for each supported dataset 5) Benchmarking and evaluation scripts
Molecular motors that hydrolyze ATP and use the derived energy to generate force are involved in a variety of diverse cellular functions. Genetic, biochemical, and cellular localization data have implicated motors in a variety of functions such as vesicle and organelle transport, cytoskeleton dynamics, morphogenesis, polarized growth, cell movements, spindle formation, chromosome movement, nuclear fusion, and signal transduction. In non-plant systems three families of molecular motors (kinesins, dyneins, and myosins) have been well characterized. These motors use microtubules (in the case of kinesines and dyneins) or actin filaments (in the case of myosins) as tracks to transport cargo materials intracellularly. During the last decade tremendous progress has been made in understanding the structure and function of various motors in animals. These studies are yielding interesting insights into the functions of molecular motors and the origin of different families of motors. Furthermore, the paradigm that motors bind cargo and move along cytoskeletal tracks does not explain the functions of some of the motors. Relatively little is known about the molecular motors and their roles in plants. In recent years, by using biochemical, cell biological, molecular, and genetic approaches a few molecular motors have been isolated and characterized from plants. These studies indicate that some of the motors in plants have novel features and regulatory mechanisms. The role of molecular motors in plant cell division, cell expansion, cytoplasmic streaming, cell-to-cell communication, membrane trafficking, and morphogenesis is beginning to be understood. Analyses of the Arabidopsis genome sequence database (51% of genome) with conserved motor domains of kinesin and myosin families indicates the presence of a large number (about 40) of molecular motors and the functions of many of these motors remain to be discovered. It is likely that many more motors with novel regulatory mechanisms that perform plant-specific functions are yet to be discovered. Although the identification of motors in plants, especially in Arabidopsis, is progressing at a rapid pace because of the ongoing plant genome sequencing projects, only a few plant motors have been characterized in any detail. Elucidation of function and regulation of this multitude of motors in a given species is going to be a challenging and exciting area of research in plant cell biology. Structural features of some plant motors suggest calcium, through calmodulin, is likely to play a key role in regulating the function of both microtubule- and actin-based motors in plants.