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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Chemical composition of the SiO2/InSb interface as determined by X-ray photoelectron spectroscopy

In connection with the relatively poor insulating characteristics of the native oxides of most III-V compound semiconductors, there has been interest in the development of deposited dielectric layers for surface passivation and MOS device fabrication on III-V compound semiconductor substrates. The chemical system of SiO2 deposited on single-crystal InSb substrates has been proposed for the fabrication of IR CCD. The considered investigation is concerned with a detailed examination of the chemical nature of the interactions between deposited and native oxides using X-ray photoelectron spectroscopy (XPS) in conjunction with a chemical-etching technique for depth profiling. The compositional structure of the interfaces obtained in the SiO2/native oxide/InSb system is derived on the basis of an intensity analysis of XPS spectra. Thermal oxidation of the InSb substrate is found to result in an In-rich native oxide and excess elemental Sb at the native oxide/substrate interface.

Vasquez, R. P.↗
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 NTRS2019

Atmospheric Chemistry Modeling 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 NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

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 O (sup 1) D (Deuterium) 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 NTRS1978

Work function measurement of lanthanum-boron compounds

The relationship between emission properties and sample composition is studied for lanthanum-boron compounds. Specifically, the La-B system is considered between 1400 and 2100 K and between LaB(4.24) and LaB(29.2) to determine the phase relationship, chemical activity of the compounds, vapor composition, and vaporization rate. The results indicate that: (1) a blue-colored phase near LaB(9) exists between a purple-colored LaB(6) and elemental boron, (2) vaporization is sufficiently more rapid than diffusion so that great compositional differences exist between the surface and the interior, (3) an activation energy lowers the boron vaporization rate from LaB(6), and (4) a steady-state surface composition between LaB(6.04) and LaB(6.07) exists for freely vaporizing materials as a function of interior composition, purity, and temperature. It is noted that the ultimate life of a thermionic diode is governed by electrode vaporization rate whereas efficiency is governed by the electrode work function.

Jacobson, D. L.↗
NASA NTRS2001

Membrane rejection of nitrogen compounds

Rejection characteristics of nitrogen compounds were examined for reverse osmosis, nanofiltration, and low-pressure reverse osmosis membranes. The rejection of nitrogen compounds is explained by integrating experimental results with calculations using the extended Nernst-Planck model coupled with a steric hindrance model. The molecular weight and chemical structure of nitrogen compounds appear to be less important in determining rejection than electrostatic properties. The rejection is greatest when the Donnan potential exceeds 0.05 V or when the ratio of the solute radius to the pore radius is greater than 0.8. The transport of solute in the pore is dominated by diffusion, although convective transport is significant for organic nitrogen compounds. Electromigration contributes negligibly to the overall solute transport in the membrane. Urea, a small organic compound, has lower rejection than ionic compounds such as ammonium, nitrate, and nitrite, indicating the critical role of electrostatic interaction in rejection. This suggests that better treatment efficiency for organic nitrogen compounds can be obtained after ammonification of urea.

NASA Discipline Life Support Systems↗
NASA NTRS1990

Chemical nature of silicon nitride-indium phosphide interface and rapid thermal annealing for InP MISFETs

A rapid thermal annealing (RTA) process in pure N2 or pure H2 was developed for ion-implanted and encapsulated indium phosphide compound semiconductors, and the chemical nature at the silicon nitride-InP interface before and after RTA was examined using XPS. Results obtained from SIMS on the atomic concentration profiles of the implanted silicon in InP before and after RTA are presented, together with electrical characteristics of the annealed implants. Using the RTA process developed, InP metal-insulator semiconductor FETs (MISFETS) were fabricated. The MISFETS prepared had threshold voltages of +1 V, transconductance of 27 mS/mm, peak channel mobility of 1200 sq cm/V per sec, and drain current drift of only 7 percent.

Biedenbender, M. D.↗
DOE OSTI2024

In Vitro Antimicrobial Activity of Volatile Compounds from the Lichen Pseudevernia furfuracea (L.) Zopf. Against Multidrug-Resistant Bacteria and Fish Pathogens

Lichens are symbiotic organisms with unique secondary metabolism. Various metabolites from lichens have shown antimicrobial activity. Nevertheless, very few studies have investigated the antimicrobial potential of the volatile compounds they produce. This study investigates the chemical composition and antimicrobial properties of volatile compounds from Pseudevernia furfuracea collected in two regions of Morocco. Hydrodistillation was used to obtain volatile compounds from samples collected in the High Atlas and Middle Atlas. Gas chromatography–mass spectrometry (GC-MS) analysis identified phenolic cyclic compounds as the primary constituents, with atraric acid and chloroatranol being the most abundant. Additionally, eight compounds were detected in lichens for the first time. The antimicrobial activity of these compounds was assessed using disc diffusion and broth microdilution methods. Both samples demonstrated significant antimicrobial effects against multidrug-resistant human bacteria, reference microorganisms, fish pathogens, and Candida albicans, with minimum inhibitory concentrations (MICs) ranging from 1000 µg/mL to 31.25 µg/mL. This study provides the first report on the volatile compounds from Pseudevernia furfuracea and their antimicrobial effects, particularly against fish pathogens, suggesting their potential as novel antimicrobial agents for human and veterinary use. Further research is warranted to explore these findings in more detail.

Essadki, Yasser (ORCID:0009000648460075)↗
DOE OSTI2023

Constraining the Reaction Rate of Criegee Intermediates with Carboxylic Acids during the Multiphase Ozonolysis of Aerosolized Alkenes

Criegee intermediates (CIs) are central to the ozonolysis of unsaturated organic compounds, controlling the formation of small fragmentation reaction products and higher molecular weight oligomeric compounds through competing unimolecular and bimolecular pathways. In particular, the reaction of thermalized CI with carboxylic acids (RCOOH) to produce oligomeric α-acyloxyalkyl hydroperoxides (AAHPs) is of interest to the chemical transformation of organic compounds in the atmosphere. In the gas phase, the CI + RCOOH reaction proceeds at supercollisional rates, consistent with a barrierless reaction mechanism. However, the rate of this reaction in condensed phases remains uncertain. Here, we report the results of aerosol flow tube studies of the ozonolysis of submicron organic aerosol containing mixtures of an internal n-alkene (Z-9-tricosene, tri) and a saturated acid (2-hexyldecanoic acid, HDA). The decay of the reactants and formation of reaction products are monitored using atmospheric pressure chemical ionization mass spectrometry (APCI–MS). A 6-fold decrease in the reactive uptake of ozone is observed with increasing acid concentration, consistent with a previous study using alcohol additives. The decay of HDA with ozone exposure shows that HDA scavenges CIs at rates comparable to competing unimolecular pathways involving CIs. The decay kinetics of HDA are analyzed using a simple kinetic model in order to constrain the ratio of the CI + RCOOH and unimolecular CI rate constants. Empirical fitting to this model, combined with theoretical estimates of the unimolecular loss rate, yields a CI + RCOOH rate constant of 1.85 ± 0.27 × 10 –19 cm 3 molec –1 s –1 , which is six orders of magnitude slower than the expected diffusion limit in a liquid organic matrix. Stochastic kinetic simulations are conducted with different values of the CI + RCOOH rate constant to validate this result and show that the diffusion-limited rate is indeed much too rapid to reproduce the experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Large Single Crystals of V 1– x Mo x O 2 from a Two-Step Chemical Vapor Transport Synthesis

This article details the synthesis of single crystals with the chemical formulas V 1-x Mo x O 2 ( 0 ≤ x ≤ 0.6) using the chemical vapor transport method at high temperatures (850 to 950 °C). These compounds are of interest due to the special relationship between their structure and electronic properties. Understanding this relationship will help guide future efforts in designing energy-saving devices. While some of these compounds have been synthesized before, this is the first time their synthesis has been reported for such a wide range of compounds in the form of bulk single crystals. The single crystal form is required for detailed characterization of the compounds. The materials were characterized chemically and structurally in order to validate the precise chemical compositions that were reported. Characterization methods included x-ray diffraction, inductively-coupled plasma atomic emission spectroscopy, and electron dispersive spectroscopy. The synthesis method is tunable, and the introduction of multiple steps both overcome the previous chemical challenges precluding synthesis of high molybdenum content crystals and allow for the creation of larger single crystals that were otherwise possible. The crystals synthesized this way are suitable for specialized measurements for determining the precise physical properties and atomic structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1976

Application of bicyclic and cage compounds

The results of a literature survey of the field of bicyclic and cage compounds were presented, with the objective of identifying those types of compounds with unusual physical and chemical stability, and determining what practical applications have been found for these compounds. Major applications have been as polymers, polymer additives, medicinals, and pesticides. Lesser applications have included fuels, fuel additives, lubricants, lubricant additives, and perfumes. Several areas where further work might be useful were also outlined; these are primarily in the areas of polymers, polymer additives, medicinals, and synthetic lubricants.

Clark, R. D.↗
NASA NTRS1987

C-13 N.M.R. study of charge transfer in alkali metal-ammonia graphite compounds

The usefulness of C-13 NMR spectroscopy and chemical shift for the study of charge transfer in graphite intercalation compounds is demonstrated. A linear relationship is established between the chemical shifts and the square root of the absolute value of the electronic charge per carbon atom. The method is applied to the ternary alkali metal-ammonia-graphite compounds. Weak solvations of electronic charges by ammonia are observed.

Tsang, T.↗
DOE OSTI2022

Supported molybdenum oxides for the aldol condensation reaction of acetaldehyde

The (retro-)aldol condensation reaction is an important chemical transformation in the upgrading of biomass-derived compounds into fuels and valuable specialty chemicals. In this study, we found that supported molybdenum oxide (MoO x ) catalysts were active and selective for the aldol condensation of acetaldehyde to crotonaldehyde under steady-state reactor conditions. Through a combination of transmission electron microscopy (TEM), ultraviolet–visible (UV–VIS) diffuse reflectance spectroscopy, Fourier transform infrared (FTIR) spectroscopy of adsorbed pyridine, and steady-state reactor testing, we determined that highly dispersed MoO x has a strong interaction with a γ-Al 2 O 3 support resulting in optimal catalyst performance at low weight loadings. In contrast, MoO x particles supported on SiO 2 have a weaker interaction with the support, resulting in a monotonic relationship between Mo loading and aldol condensation activity. The Lewis acid site density and strength are important parameters for predicting aldol condensation activity across all samples. The concentration of weak acid sites had a poor correlation with aldol condensation activity, most likely because these sites are too weak to activate acetaldehyde for the reaction. Medium and strong acid sites both had good correlations to aldol condensation activity. Results from X-ray absorption near edge structure (XANES) and acetaldehyde temperature programmed desorption (TPD) indicated that partially reduced MoO x was more active for aldol condensation, but pretreatment in reducing or oxidizing environments had no significant effect on steady-state catalytic activity. Finally, characterization of spent catalyst samples through temperature programmed oxidation (TPO) and thermogravimetric analysis (TGA) revealed that catalysts with high densities of strong acid sites tended to form more carbonaceous deposits on the surface over the course of the reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

Identifying the Wide Diversity of Extraterrestrial Purine and Pyrimidine Nucleobases in Carbonaceous Meteorites

The lack of pyrimidine diversity in meteorites remains a mystery since prebiotic chemical models and laboratory experiments have predicted that these compounds can also be produced from chemical precursors found in meteorites. Here we report the detection of nucleobases in three carbonaceous meteorites using state-of-the-art analytical techniques optimized for small-scale quantification of nucleobases down to the range of parts per trillion (ppt). In addition to previously detected purine nucleobases in meteorites such as guanine and adenine, we identify various pyrimidine nucleobases such as cytosine, uracil, and thymine, and their structural isomers such as isocytosine, imidazole-4-carboxylic acid, and 6-methyluracil, respectively. Given the similarity in the molecular distribution of pyrimidines in meteorites and those in photon-processed interstellar ice analogues, some of these derivatives could have been generated by photochemical reactions prevailing in the interstellar medium and later incorporated into asteroids during solar system formation. This study demonstrates that a diversity of meteoritic nucleobases could serve as building blocks of DNA and RNA on the early Earth.

Yasuhiro Oba↗
DOE OSTI2022

Phase I DOE DE-SC0021823 Entitled “Changing the Design Rules of Rubber to Create Lighter Weight, More Fuel Efficient Tires” Final Technical Report 04/08/2022

Molecular Rebar Design, LLC has successfully developed new rubber composites with individualized carbon nanotubes called MOLECULAR REBAR® (MR), improving tire tread compound performance for use with electric vehicles (EVs). Through the iterative nature of the Phase I experimentation, MRD developed a novel carbon nanotube material that disperses well with other typical tire compound fillers and can be chemically functionalized for unique material property benefits. The new MR material is a chemically functionalized, discrete multi-wall carbon nanotube that binds into the rubber compound using silane technology. This Molecular Rebar based tire tread material drastically improves DIN abrasion resistance 25%+ (correlating to improved tire life), reduces tread weight by 6-7%, and reduces rolling resistance 20%+ (correlating to improved energy efficiency), as compared to an in-use, state-of-the-art, silica-silane tread compound. The formulations can be adjusted to maximize energy efficiency, to maximize wear or to have a blend of improvement depending on the tire performance needs of end use customers.

36 MATERIALS SCIENCE↗