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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

CRADA Final Report: CRADA Number NFE-18-07341 with Eonix LLC

Over the past two years, my work has been focused on the development of a commercially viable electrolyte that expands the voltage window of ultracapacitors featuring standard activated carbon electrodes from 2.7V to greater than 3V. During this fellowship in collaboration with staff at Oak Ridge National Laboratory, we refined a high throughput screening process to rapidly analyze the properties and performance of electrolyte candidates in ultracapacitors and establish a quantitative structure activity relationship (QSAR) methodology for candidate selection. Over sixty electrolyte chemistries were screened using these novel techniques.

42 ENGINEERING↗
DOE OSTI2021

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2025

Enhanced Production of Algae Lipids and Carbohydrates for Fuel and Polyurethane Precursors

This Final Technical Report summarizes the goals, approach, and outcomes of the project “Enhanced Production of Algae Lipids and Carbohydrates for Fuel and Polyurethane Precursors”. The project addressed the challenge of improving microalgae biomass productivity and simultaneously producing valuable polyurethane precursors (PUPs), from both lipids and carbohydrates, that can be converted into renewable biofuels and bio-based polyurethane (PU) products. By integrating advanced genetic engineering, traditional breeding, high-throughput screening, pilot cultivation, and chemical conversion technologies, the team achieved significant advancements in algae biotechnology.

09 BIOMASS FUELS↗
DOE OSTI2026

Harnessing Heterologous Bacterial Two-Component Systems as Biosensors to Address Challenges in Fermentation Scale-Up

Scaling up bacterial fermentation from bench to industrial scale often results in unpredictable performance losses, possibly in part due to changes in microenvironmental conditions such as pH. To investigate this, we developed a suite of pH-sensitive biosensors from bacterial two-component systems (TCSs) that provide a dynamic, fluorescent readout in response to extracellular pH changes. TCSs consist of a periplasmic sensor histidine kinase (HK) that, in response to an extracellular stimulus, autophosphorylates intracellularly and subsequently transfers the phosphate to a cognate response regulator (RR) that modulates transcription of target genes. We utilized three pH-responsive TCSs (referred to here as CVJ1, CVJ30, and CVJ79) and linked their output to GFP. This was achieved by placing the RR promoter upstream of GFP or by constructing a chimeric RR composed of the native receiver domain and the DNA-binding domain of another well-characterized RR with a defined promoter. All components - HK, RR (native or chimeric), and GFP under its corresponding promoter - were cloned into a broad-host-range plasmid. Sensors were validated in Escherichia coli and Pseudomonas putida, including the muconic acid-producing strain P. putida TL207. All three biosensors successfully reported pH, with fluorescence (normalized to optical density) correlating strongly with media pH. Among the native sensors, CVJ79 showed the most robust performance while CVJ1 also performed best in its native form; CVJ30 exhibited improved functionality as a chimera, suggesting that modular RR design can enhance compatibility in some heterologous hosts. Further, CVJ79 was activated by alkaline conditions, while CVJ30 responded to acidic environments. Notably, CVJ1 was induced by high pH in wild-type E. coli and P. putida, but low pH in TL207. The observed differences in sensor activation between strains - particularly the divergent response of CVJ1 - suggest that host-specific regulatory pathways may influence how cells perceive and adapt to pH stress. Moving forward, these biosensors can be used to guide the rational design of more robust strains, optimize process conditions in real time, and inform strategies to minimize physiological heterogeneity during scale-up. Integrating these tools into high-throughput screening and bioreactors will be a key step toward improving predictability and performance in industrial bioprocesses.

09 BIOMASS FUELS↗
DOE OSTI2026

Discovery of correlated electron molecular orbital materials using graph representations

Correlated electron molecular orbital (CEMO) materials host emergent electronic states built from molecular orbitals localized over clusters of transition metal ions yet have historically been discovered sporadically and generally been treated as isolated case studies. Here we establish CEMO materials as a systematically discoverable class and introduce a graph-based framework to identify, classify, and organize transition-metal cluster motifs in inorganic solids. Starting from crystal structures in the Materials Project, we construct transition metal connectivity graphs, extract cluster motifs using a bond-cutting algorithm, and determine cluster point groups, effective cluster sublattice dimensionality, and translational symmetry. Applying this approach in a high-throughput screen of 34,548 compounds yields 5,306 cluster-containing materials, including 2,627 stable or metastable compounds with isolated clusters and 984 materials featuring mixed-metal clusters. The resulting dataset reveals symmetry and element dependent trends in cluster formation. By integrating cluster classification with flat band lattice topology and battery-relevant information, we provide further relevant information to multiple scientific communities. The accompanying open dataset, Cluster Finder software, and interactive web platform enable systematic exploration of cluster driven electronic phenomena and establish a general pathway for discovering correlated quantum materials and functional materials with cluster-based or extended metal-metal bonding in inorganic solids.

Akhond, Md. Rajbanul [Department of Chemistry, 800↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Indoline-6-Sulfonamide Inhibitors of the Bacterial Enzyme DapE

Inhibitors of the bacterial enzyme dapE-encoded N-succinyl-l,l-diaminopimelic acid desuccinylase (DapE; EC 3.5.1.18) hold promise as antibiotics with a new mechanism of action. Herein we describe the discovery of a new series of indoline sulfonamide DapE inhibitors from a high-throughput screen and the synthesis of a series of analogs. Inhibitory potency was measured by a ninhydrin-based DapE assay recently developed by our group. Molecular docking experiments suggest active site binding with the sulfonamide acting as a zinc-binding group (ZBG).

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Fidelity and enhanced sensitivity of differential transcription profiles following linear amplification of nanogram amounts of endothelial mRNA

Although mRNA amplification is necessary for microarray analyses from limited amounts of cells and tissues, the accuracy of transcription profiles following amplification has not been well characterized. We tested the fidelity of differential gene expression following linear amplification by T7-mediated transcription in a well-established in vitro model of cytokine [tumor necrosis factor alpha (TNFalpha)]-stimulated human endothelial cells using filter arrays of 13,824 human cDNAs. Transcriptional profiles generated from amplified antisense RNA (aRNA) (from 100 ng total RNA, approximately 1 ng mRNA) were compared with profiles generated from unamplified RNA originating from the same homogeneous pool. Amplification accurately identified TNFalpha-induced differential expression in 94% of the genes detected using unamplified samples. Furthermore, an additional 1,150 genes were identified as putatively differentially expressed using amplified RNA which remained undetected using unamplified RNA. Of genes sampled from this set, 67% were validated by quantitative real-time PCR as truly differentially expressed. Thus, in addition to demonstrating fidelity in gene expression relative to unamplified samples, linear amplification results in improved sensitivity of detection and enhances the discovery potential of high-throughput screening by microarrays.

Evaluation Studies↗
NASA NTRS2006

Protein Crystallization Using Room Temperature Ionic Fluids

The ionic liquids (ILs) 1-butyl-3-methylimidizolium chloride (C4mim-C1), 1-butyl-3- methylimidizolium diethyleneglycol monomethylethersulfate ([C4mim]DEMGS), and 1-butyl-1 -methylpyrollidinium dihydrogenphosphate ([p1,4]dhp) were tested for their effects on the crystallization of the proteins canavalin, beta-lactoglobulin B, xylanase, and glucose isomerase, using a standard high throughput screen. The crystallization experiments were set up with the ILs added to the protein solutions at 0.2 and 0.4 M final concentrations. Crystallization droplets were set up at three proteixprecipitant ratios (1:1, 2:1, and 4:l), which served to progressively dilute the effects of the screen components while increasing the equilibrium protein and IL concentrations. Crystals were obtained for all four proteins at a number of conditions where they were not obtained from the IL-free control experiment. Over half of the protein-IL combinations tested had more successful outcomes than negative, where the IL-free crystallization was better than the corresponding IL-containing outcome, relative to the control. One of the most common causes of a negative outcome was solubilization of the protein by the IL, resulting in a clear drop. In one instance, we were able to use the IL-induced solubilizing to obtain beta-lactoglobulin B crystals from conditions that gave precipitated protein in the absence of IL. The results suggest that it may be feasible to develop ILs specifically for the task of macromolecule crystallization.

Pusey, Marc L.↗
NASA NTRS2009

Catalysts for Efficient Production of Carbon Nanotubes

Several metal alloys have shown promise as improved catalysts for catalytic thermal decomposition of hydrocarbon gases to produce carbon nanotubes (CNTs). Heretofore almost every experiment on the production of carbon nanotubes by this method has involved the use of iron, nickel, or cobalt as the catalyst. However, the catalytic-conversion efficiencies of these metals have been observed to be limited. The identification of better catalysts is part of a continuing program to develop means of mass production of high-quality carbon nanotubes at costs lower than those achieved thus far (as much as $100/g for purified multi-wall CNTs or $1,000/g for single-wall CNTs in year 2002). The main effort thus far in this program has been the design and implementation of a process tailored specifically for high-throughput screening of alloys for catalyzing the growth of CNTs. The process includes an integral combination of (1) formulation of libraries of catalysts, (2) synthesis of CNTs from decomposition of ethylene on powders of the alloys in a pyrolytic chemical-vapor-decomposition reactor, and (3) scanning- electron-microscope screening of the CNTs thus synthesized to evaluate the catalytic efficiencies of the alloys. Information gained in this process is put into a database and analyzed to identify promising alloy compositions, which are to be subjected to further evaluation in a subsequent round of testing. Some of these alloys have been found to catalyze the formation of carbon nano tubes from ethylene at temperatures as low as 350 to 400 C. In contrast, the temperatures typically required for prior catalysts range from 550 to 750 C.

Sun, Ted X.↗
NASA NTRS2005

Improved Charge-Transfer Fluorescent Dyes

Improved charge-transfer fluorescent dyes have been developed for use as molecular probes. These dyes are based on benzofuran nuclei with attached phenyl groups substituted with, variously, electron donors, electron acceptors, or combinations of donors and acceptors. Optionally, these dyes could be incorporated as parts of polymer backbones or as pendant groups or attached to certain surfaces via self-assembly-based methods. These dyes exhibit high fluorescence quantum yields -- ranging from 0.2 to 0.98, depending upon solvents and chemical structures. The wavelengths, quantum yields, intensities, and lifetimes of the fluorescence emitted by these dyes vary with (and, hence, can be used as indicators of) the polarities of solvents in which they are dissolved: In solvents of increasing polarity, fluorescence spectra shift to longer wavelengths, fluorescence quantum yields decrease, and fluorescence lifetimes increase. The wavelengths, quantum yields, intensities, and lifetimes are also expected to be sensitive to viscosities and/or glass-transition temperatures. Some chemical species -- especially amines, amino acids, and metal ions -- quench the fluorescence of these dyes, with consequent reductions in intensities, quantum yields, and lifetimes. As a result, the dyes can be used to detect these species. Another useful characteristic of these dyes is a capability for both two-photon and one-photon absorption. Typically, these dyes absorb single photons in the ultraviolet region of the spectrum (wavelengths < 400 nm) and emit photons in the long-wavelength ultraviolet, visible, and, when dissolved in some solvents, near-infrared regions. In addition, these dyes can be excited by two-photon absorption at near-infrared wavelengths (600 to 800 nm) to produce fluorescence spectra identical to those obtained in response to excitation by single photons at half the corresponding wavelengths (300 to 400 nm). While many prior fluorescent dyes exhibit high quantum yields, solvent-polarity- dependent fluorescence behavior, susceptibility to quenching by certain chemical species, and/or two-photon fluorescence, none of them has the combination of all of these attributes. Because the present dyes do have all of these attributes, they have potential utility as molecular probes in a variety of applications. Examples include (1) monitoring curing and deterioration of polymers; (2) monitoring protein expression; (3) high-throughput screening of drugs; (4) monitoring such chemical species as glucose, amines, amino acids, and metal ions; and (5) photodynamic therapy of cancers and other diseases.

Meador, Michael↗
NASA NTRS2004

Rapid Chemometric Filtering of Spectral Data

A method of rapid, programmable filtering of spectral transmittance, reflectance, or fluorescence data to measure the concentrations of chemical species has been proposed. By programmable is meant that a variety of spectral analyses can readily be performed and modified in software, firmware, and/or electronic hardware, without need to change optical filters or other optical hardware of the associated spectrometers. The method is intended to enable real-time identification of single or multiple target chemical species in applications that involve high-throughput screening of multiple samples. Examples of such applications include (but are not limited to) combinatorial chemistry, flow cytometry, bead assays, testing drugs, remote sensing, and identification of targets. The basic concept of the proposed method is to perform real-time crosscorrelations of a measured spectrum with one or more analytical function(s) of wavelength that could be, for example, the known spectra of target species. Assuming that measured spectral intensities are proportional to concentrations of target species plus background spectral intensities, then after subtraction of background levels, it should be possible to determine target species concentrations from cross-correlation values. Of course, the problem of determining the concentrations is more complex when spectra of different species overlap, but the problem can be solved by use of multiple analytical functions in combination with computational techniques that have been developed previously for analyses of this type. The method is applicable to the design and operation of a spectrometer in which spectrally dispersed light is measured by means of an active-pixel sensor (APS) array. The row or column dimension of such an array is generally chosen to be aligned along the spectral-dispersion dimension, so that each pixel intercepts light in a narrow spectral band centered on a wavelength that is a known function of the pixel position. The proposed method admits of two hardware implementations for computing cross-correlations in real time.

Beaman, Gregory↗
NASA NTRS2020

Computational Design of Eutectic Molten Salt Mixtures: What Can Thermodynamic Models Do?

In the search for efficient energy storage battery technologies, designing stable electrolytes has been a long-standing challenge. Electrolytes based on molten salt eutectics are known for their stability with minimum parasitic reactions when compared to their widely used organic counterparts. However, the operating temperatures of these molten salt electrolyte-based batteries are dictated by the melting point of the eutectic mixtures. Design and high throughput screening of low melting temperature eutectic molten salt mixtures have been hindered by the lack of computational models. In this work, we develop thermodynamic models to predict the eutectic points of several molten salt mixtures. The framework of the COSMO-SAC model is used for the predictions and is compared with experimental data and other thermodynamic approaches. Rapid thermodynamics-based approaches, as shown in this study, can accelerate the discovery of new materials, complementing experimental techniques.

Ashwin Ravichandran↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
DOE OSTI2021

Kinetic Measurements in Heterogeneous Catalysis

This contribution is about the experimental determination of the rate of a heterogeneous catalytic reaction and the analysis of kinetic data. In this case, the reaction rate can be defined as the frequency at which the closed sequence of elementary steps transforming reactants into products is occurring. However, the rate of chemical reaction is not directly observed; rather, one records the rate of substance change. The rate of chemical reaction is calculated based on the rate of substance change and assumed stoichiometry of the reaction. In order to make this an intensive quantity (i.e., independent of the volume in which the reaction is performed, this frequency is divided by the latter or by a quantity proportional to the latter). As the focus of what follows is on heterogeneously catalyzed reactions this can be the catalyst mass, the catalyst surface area or the total number of active sites present in the reaction volume confined by the walls of a chemical reactor. For example, the rate may be reported as one of the following: Net rate of consumption/production of component i mol m-3 s-1 Net specific rate of consumption/production of component i mol kg-1 s-1 Rate of substance change (per unit volume of catalyst) mol m-3 s-1 Specific reaction rate (per unit mass of catalyst) mol kg-1 s-1 Net rate of consumption/production of component i mol m-2 s-1 The turnover frequency (TOF) is a characteristic originally introduced by Boudart [1] is expressed as TOF=R/G_tot , where R is the steady-state rate of reactant consumption or product generation and G_totis the total areal density of active sites [mol m-2]. The latter is typically taken from experimental chemisorption data obtained at low temperature. The advancement of a chemical reaction leads to changes in the amounts of reactants and products which, for a stoichiometric single reaction, are connected by the stoichiometric coefficients. In many cases, for the sake of simplicity, we will assume that the investigated reaction proceeds according to a single reaction pathway, so that the net production rate of a reaction component is directly proportional to the reaction rate and that the latter equals the net production rate of any of the involved components when divided by the appropriate stoichiometric coefficient. For complex stoichiometric reactions the net production rates of the involved components are linear combinations of the reaction rates. Different goals for kinetic measurements can be formulated: Catalyst testing, i.e., obtaining the kinetic dependences for the development and discrimination of efficient catalytic materials. Precise kinetic characterization of active materials via high-throughput screening is a part of such activity. Detailed kinetics, i.e., revealing the detailed mechanism of complex catalytic reaction via systematic kinetic studies, both steady-state and non-steady-state Industrial kinetics, i.e., obtaining data and relationships for describing and predicting the behaviour of catalytic reactors and processes at industrial scale Finally, mathematical modeling and analysis where kinetic measurements provide data for understanding complex kinetic phenomena, e.g., oscillations, non-linear self-organization, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Small molecule inhibitors for treatment of alpha viruses

An in vitro assay was designed to measure the activity of the alphavirus non-structural protein 2 (nsP2), which is the viral protease and is required for viral replication. By taking advantage of fluorescence-resonance energy transfer between two proteins, a protease cleavage assay was generated. This was utilized for high-throughput screening of 40,000 small molecules. Inhibitors were validated using cell-based assays to measure alphavirus infection and cytotoxicity. Certain compounds were then characterized for anti-viral efficacy in various cell lines in numerous assays. Compounds were tested against Chikungunya virus, Venezuelan Equine Encephalitis virus, Rift Valley Fever virus, and Zika virus. Three compounds (compounds I, II, and III) showed pan-alphavirus anti-viral efficacy at concentrations that did not result in cell toxicity. An additional compound, structure IV, showed broad spectrum inhibition of all viruses tested. Pharmaceutical preparations and methods of treatment including these compounds are provided herein.

60 APPLIED LIFE SCIENCES↗