Results for “model compounds” — Page 80

DOE OSTI2021

Strong influence of nonmagnetic ligands on the momentum-dependent spin splitting in antiferromagnets

Here, recent studies show the nonrelativistic antiferromagnetic ordering could generate momentum-dependent spin splitting analogous to the Rashba effect but free from the requirement of relativistic spin-orbit coupling. Whereas the classification of such compounds can be illustrated by different spin-splitting prototypes (SSTs) from symmetry analysis and density-functional-theory calculations, the huge variation in chemical bonding and structures of these diverse compounds possibly clouds the issue of how much of the variation in spin splitting can be traced back to the symmetry-defined characteristics, rather to the underlining chemical and structural diversity. The alternative model Hamiltonian approaches do not confront the issues of chemical and structural complexity but often consider only the magnetic sublattice, dealing with the all-important effects of the nonmagnetic ligands via renormalizing the interactions between the magnetic sites. To this end, we constructed a DFT model Hamiltonian that allows us to study SSTs at constant chemistry while retaining the realistic atomic-scale structure including ligands. This is accomplished by using a single, universal magnetic skeletal lattice (Ni 2+ ions in rocksalt NiO) and designing small displacements of the nonmagnetic (oxygen) sublattice which produce, by design, the different SST magnetic symmetries. We show that (i) even similar crystal structures having very similar band structures can lead to contrasting behavior of spin splitting vs momentum, and (ii) even subtle deformations of the nonmagnetic ligand sublattice could cause a giant spin splitting in AFM-induced SST. This is a paradigm shift relative to the convention of modeling magnets without considering the nonmagnetic ligand that mediates indirect magnetic interaction (e.g., superexchange).

36 MATERIALS SCIENCE↗

DOE OSTI2024

Evidence of ordering in Cu-Ni alloys from experimental electronic entropy measurements

Phase diagrams exhibiting extended solid-solution and lenslike melting are often reproduced using ideal solutions, where ideal mixing considers a fully random configurational entropy of mixing. In the field of irreversible thermodynamics, experimental measurements of the composition variation of high-temperature electronic transport and molten-state properties suggest, however, a strong role for short-range atomic ordering in these systems. Herein, measurements of the thermopower and resistivity are reported for Cu-Ni solid solutions as a function of temperature and composition. The electronic transport properties were interpreted with an irreversible thermodynamic framework, revealing a large electronic contribution to the entropy of mixing. By considering a cluster model for the configurational entropy that uses the electronic contribution to inform the existence of ordered associates, we rationalize such a contribution of the electronic entropy with the ideal entropy of mixing commonly used to model such systems. In conclusion, these results suggest that the short-range order of the atoms plays a significant role in both solid and liquid states, even when there are no dominant intermetallic compounds in these alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Salt supplementation-induced metabolic reprogramming in Streptomyces coelicolor

Members of the genus Streptomyces are major producers of a wide variety of secondary metabolites that serve as bioactive compounds. Many secondary metabolites are produced in response to environmental signals such as biotic and abiotic stresses. In this study, we identified salt supplementation as one of the stimuli activating secondary metabolism in the model Streptomyces species, Streptomyces coelicolor. Comparative metabolomics revealed overproduction of several known secondary metabolites, most notably undecylprodigiosin and coelimycin P1, in addition to their biosynthetic intermediates and derivatives, as well as many unknown metabolites. Transcriptomic analysis revealed activation of diverse biological processes including cation uptake, compatible solute production, and the phosphate limitation stress response through conserved and species-specific mechanisms, presumably to overcome the increased salinity. This response leads to activation of a variety of regulatory and metabolic pathways required for production of secondary metabolites including activation of conserved metabolic pathways for energy and substrate supply and species-specific secondary metabolite biosynthetic gene clusters. Furthermore, several promoter sequences contributing to upregulation of secondary metabolism induced by salt supplementation were identified. Overall, our data show how S. coelicolor copes with the increased salinity and tailors the cellular metabolism toward secondary metabolism in a conserved and species-specific manner.

Otani, Hiroshi [USDOE Joint Genome Institute (JGI)↗

NASA NTRS1977

Radiometric measurement of stratospheric HCl

On March 17, 1976, the atmospheric absorption of solar infrared radiation at sunset was measured by a balloon-borne pressure modulator, whose major feature was a cell containing gaseous HCl, through which the incoming radiation passed. The side-band:wide-band ratio which is related to the amount of atmospheric HCl in the path between the instrument and the sun, was also measured. The vertical mixing ratio profile for gaseous HCl for the altitude range 16-39 km, was derived from the data by using a computer model. The shape of the profile implies that the major source of HCl in the stratosphere is around 32 km. The importance of this profile in determining the effects of man-made chlorine compounds on atmospheric ozone is stressed.

Eyre, J. R.↗

NASA NTRS1981

Comet nucleus - Some characteristics and a hypothesis on origin and structure

The spectroscopic properties of comets are discussed, and consideration is given to the problems of the cosmic ray irradiation and the origin and structure of cometary nuclei. The classification of cometary spectra as continuum dominant, molecular emissions dominant and CO(+) dominant is examined, and changes in spectral characteristics with heliocentric distance although not with cometary age are pointed out. Spectral evidence for a variety of organic compounds in the cometary nucleus is noted, and a scenario is presented whereby these complex molecules may form as a result of cosmic ray irradiation of cometary ices in the Oort cloud. Finally, a model is proposed for the accumulation of small icy grains to form a cometary nucleus composed of cometesimals of size distribution following a -5th power dependence on mass.

Donn, B.↗

NASA NTRS1987

Airfoil flutter model suspension system

A wind tunnel suspension system for testing flutter models under various loads and at various angles of attack is described. The invention comprises a mounting bracket assembly affixing the suspension system to the wind tunnel, a drag-link assembly and a compound spring arrangement comprises a plunge spring working in opposition to a compressive spring so as to provide a high stiffness to trim out steady state loads and simultaneously a low stiffness to dynamic loads. By this arrangement an airfoil may be tested for oscillatory response in both plunge and pitch modes while being held under high lifting loads in a wind tunnel.

Reed, Wilmer H.↗

NASA NTRS1993

Simplified jet fuel reaction mechanism for lean burn combustion application

Successful modeling of combustion and emissions in gas turbine engine combustors requires an adequate description of the reaction mechanism. Detailed mechanisms contain a large number of chemical species participating simultaneously in many elementary kinetic steps. Current computational fluid dynamic models must include fuel vaporization, fuel-air mixing, chemical reactions, and complicated boundary geometries. A five-step Jet-A fuel mechanism which involves pyrolysis and subsequent oxidation of paraffin and aromatic compounds is presented. This mechanism is verified by comparing with Jet-A fuel ignition delay time experimental data, and species concentrations obtained from flametube experiments. This five-step mechanism appears to be better than the current one- and two-step mechanisms.

Lee, Chi-Ming↗

NASA NTRS1983

Shock Tube Studies of Soot Formation

The objective of this research is to record the time histories of the major and minor species which appear in the pyrolysis of toluene, benzene, butadiene, allene, and acetylene; to develop a set of reactions that will model the observed profiles over a wide temperature and concentration range; to identify the critical reactions that influence the pre-particle soot formation process. Toluene and benzene were chosen as two key aromatic compounds which are representative of the pyrolytic process. Butadiene, allene, and acetylene were selected to investigate the formation of aromatic compounds from non-cyclic species. The experimental apparatus used for the study consists of a shock tube coupled to a time-of-flight mass spectrometer Spectra are recorded at 30 microsecond intervals for a total observation time of 0.50 - 1.20 milliseconds. Peak heights of the species of interest in the m/e range 12-300 are measured as a function of reaction time. Calibration curves are constructed which aid the conversion of peak heights to concentrations. The mixtures range from 1 percent-6 percent fuel; the balance is neon diluent.

R D Kern↗

NASA NTRS2004

Biology and The Future of Mars

It is possible that at some time in the future we might recreate a habitable climate on Mars returning it to the life-bearing state it may have enjoyed early in its history. Our studies of Mars are still in a preliminary state but everything we have learned suggests that it may he possible to restore Mars to a habitable climate. Long part of the intersection of science and fiction (eg. Clarke, 1995), serious studies of planetary ecosynthesis on Mars began after the results of the Viking mission indicated that all the compounds needed for life were present on the surface of Mars is some accessible form (Averner and MacElroy, 1976; McKay et al., 1991; Fogg, 1995). Recent work has focused on the use of climate models to compute the timescales to warm Mars (McKay et al., 1991 ; McKay and Marinova, 2001). Planetary ecosynthesis on Mars has implications for the objectives and conduct of robotic and human exploration. In particular the question of forward contamination must be considered in a new way if we wish to control the introduction of life to Mars in advance of planetary ecosynthesis.

McKay, Christopher P.↗

NASA NTRS2011

Performance Tests of Snow-Related Variables Over the Tibetan Plateau and Himalayas Using a New Version of NASA GEOS-5 Land Surface Model that Includes the Snow Darkening Effect

The snow darkening effect, i.e. the reduction of snow albedo, is caused by absorption of solar radiation by absorbing aerosols (dust, black carbon, and organic carbon) deposited on the snow surface. This process is probably important over Himalayan and Tibetan glaciers due to the transport of highly polluted Atmospheric Brown Cloud (ABC) from the Indo-Gangetic Plain (IGP). This effect has been incorporated into the NASA Goddard Earth Observing System model, version 5 (GEOS-5) atmospheric transport model. The Catchment land surface model (LSM) used in GEOS-5 considers 3 snow layers. Code was developed to track the mass concentration of aerosols in the three layers, taking into account such processes as the flushing of the compounds as liquid water percolates through the snowpack. In GEOS-5, aerosol emissions, transports, and depositions are well simulated in the Goddard Chemistry Aerosol Radiation and Transport (GO CART) module; we recently made the connection between GOCART and the GEOS-5 system fitted with the revised LSM. Preliminary simulations were performed with this new system in "replay" mode (i.e., with atmospheric dynamics guided by reanalysis) at 2x2.5 degree horizontal resolution, covering the period 1 November 2005 - 31 December 2009; we consider the final three years of simulation here. The three simulations used the following variants of the LSM: (1) the original Catchment LSM with a fixed fresh snowfall density of 150 kg m-3 ; (2) the LSM fitted with the new snow albedo code, used here without aerosol deposition but with changes in density formulation and melting water effect on snow specific surface area, (3) the LSM fitted with the new snow albedo code as same as (2) but with fixed aerosol deposition rates (computed from GOCART values averaged over the Tibetan Plateau domain [Ion.: 60-120E; lat.: 20-50N] during March-May 2008) applied to all grid points at every time step. For (2) and (3), the same setting on the fresh snowfall density as in (1) was used.

Yasunari, Tppei J.↗

NASA NTRS2013

Prospecting for Polar Volatiles: Results from the Resolve Field

Both the Moon and Mercury evidently host ice and other volatile compounds in cold traps at the planets poles. Determining the form, spatial distribution, and abundance of these volatiles at the lunar poles can help us understand how and when they were delivered and emplaced. This bears directly on the delivery of water and prebiotic compounds to the inner planets over the solar system s history, and also informs plans for utilizing the volatiles as resources for sustained human exploration as well as the commercial development of space. Temperature models and orbital data suggest near-surface volatile concentrations may exist at polar locations not strictly in permanent shadow. Remote operation of a robotic lunar rover mission for the 7-10 days of available sunlight would permit key questions to be answered. But such a short, quick-tempo mission has unique challenges and requires a new concept of operations. Both science and rover operations decisionmaking must be done in real time, requiring immediate situational awareness, data analysis, and decision support tools.

Elphic, Richard C.↗

NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗

NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Shreyas J Honrao↗

DOE OSTI2024

Experimental Testing of the Theoretically Predicted Magnetic Properties for Kagomé Compounds in the Li–Fe–Ge System

Explore the source record for details and available documents.

Mantravadi, Aishwarya [Department of Chemistry; Io↗

NASA NTRS2001

Integration of Atmospheric, Laboratory, and Satellite Data to Estimate Biospheric Sources of Oxygenated Organic Compounds

For this study, we intend to use a long time series of coarse resolution (8 km and quarter degree) vegetation leaf area index (LAI) and fraction of absorbed PAR (FAPAR) derived from AVHRR data for the time period July 1981 through Dec 2001. The Global Inventory Monitoring and Modeling Studies (GIMMS) data set of AVHRR channel reflectances currently was developed by Dr. Tucker at NASA Goddard. The spatial resolution of the data is 8 km, and a 15-day maximum Normalized Difference Vegetation Index (NDVI) composite time series for the period July 1981 through December 1999 has been produced. The data processing included improved navigation, calibration for intra- and inter-sensor variations, partial atmospheric correction for gaseous absorption and scattering. Stratospheric aerosol effects associated with volcanic eruptions were corrected using a combination of the methods for the data from the period April 1982 - December 1984 (El Chichon) and June 1991 - December 1993 (Mt. Pinatubo).

Myneni, Ranga B.↗

DOE OSTI2021

Oxygen magnetic polarization, nodes in spin density, and zigzag spin order in oxides

Recent studies on Ba 2 CoO 4 (BCO) and SrRuO 3 (SRO) have unveiled a variety of intriguing phenomena, such as magnetic polarization on oxygens, unexpected nodes in the spin density profile along bonds, and unusual zigzag spin patterns in triangular lattices. Here, using simple model calculations supplemented by density functional theory we explain the presence of nodes based on the antibonding character of the dominant singly occupied molecular orbitals along the transition metal (TM) to oxygen bonds. Our simple model also allows us to explain the net polarization on oxygen as originated from the hybridization between atoms and mobility of the electrons with spins opposite to those of the closest TM atoms. Overall, our results are not limited to BCO and SRO, but they are generic and qualitatively predict the net polarization expected on any ligands, according to the spin order of the closest TM atoms and the number of intermediate ligand atoms. Finally, we propose that a robust easy-axis anisotropy would suppress the competing 120° antiferromagnetic order to stabilize the zigzag pattern order as ground state in a triangular lattice. Our generic predictions should be applicable to any other compound with characteristics similar to those of BCO and SRO.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗

NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗