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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Identification of human immunodeficiency virus type-1 Gag-TSG101 interaction inhibitors by high-throughput screening

Highlights: • Development of HTS system based on ELISA targeting Gag-TSG101 interaction. • Screening of 9600 compounds identified 11 compounds inhibiting Gag-TSG101 binding. • Hit compounds, HSM-9 and HSM-10 potently reduced HIV-1 replication. The interaction between viral protein Gag and cellular protein tumor susceptibility gene 101 (TSG101) is a crucial step in the HIV-1 replication cycle. This interaction initiates the viral assembly/budding via the cellular endosomal sorting complexes required for transport (ESCRT) pathway, making it a potential target for antiviral therapy. Here we developed a simple, robust, and reliable high-throughput screening (HTS) system based on enzyme-linked immunosorbent assay (ELISA) to identify compounds that inhibit HIV-1 replication by targeting Gag-TSG101 interaction. Through screening of the 9600-compound library using the established HTS system, several hit compounds, which inhibited Gag-TSG101 interaction, were identified. Subsequent assays revealed two hit compounds, HSM-9 and HSM-10, which have antiviral activity against CD4{sup +} T cell-tropic NL4-3 and macrophage-tropic JR-CSF HIV-1 strains. These results suggest that our established HTS system is an indispensable tool for the identification of HIV-1 Gag-TSG101 interaction inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Improving biomass and lipid yields of Desmodesmus armatus and Chlorella vulgaris through mutagenesis and high-throughput screening

Advancement of microalgae for use as biofuel feedstock depends on bioprospecting novel strains and then applying classical strain improvement techniques to enhance desirable traits. Here we successfully demonstrate this approach using two novel strains of green algae, identified as Desmodesmus armatus and Chlorella vulgaris, to improve biomass and lipid yields. UV irradiation was utilized to generate random mutations and the resulting populations were analyzed via flow cytometry to quantify lipid fluorescence while enriching for high lipid variants using fluorescence activated cell sorting. Secondary screening was performed via in situ fluorescence staining to identify the single highest lipid producing mutants from each strain. This approach produced a Desmodesmus armatus mutant that exhibited both increased biomass productivity and higher lipid content, which doubled lipid yield from the culture. The resulting Chlorella vulgaris mutant exhibited an increase in growth only, resulting in a 33% increase in lipid yield.

09 BIOMASS FUELS↗
DOE OSTI2021

The crystal and coordination chemistry of neptunium in all its oxidation states: An expanded structural hierarchy of neptunium compounds

We report structures of 388 neptunium-bearing crystal structures are sorted into an expanded structural hierarchy and examined. The majority of structures contain Np(V) (149), Np(VI) (99), or Np(IV) (91) whereas structures containing Np(II), Np(III), Np(VII), or more than one oxidation state are rare. Coordination geometry of Np(V) and Np(VI) compounds is dominated by the approximately linear neptunyl cation. Actinyl–actinyl interactions (AAIs) are most prevalent in Np(V) compounds and are observed in 48% of these extended structures. Prismatic coordination geometries are commonly observed in Np(IV) compounds. Np(III) organometallic complexes display a variety of coordination modes. Coordination chemistry of Np(VII) compounds is dominated by the tetra-oxido core. Updated average bond length values are provided for Np(IV), Np(V), Np(VI), and Np(VII) compounds and updated bond valence parameters for Np(IV) are reported. A graphical notation system for neptunyl structures coordinated by organic ligands was developed to facilitate discussion and identify similarities between neptunyl compounds coordinated by oxyanions and neptunyl compounds coordinated by organic ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Asymmetric and non-stoichiometric glycoprotein recognition by two distinct antibodies results in broad protection against ebolaviruses

Several ebolaviruses cause outbreaks of severe disease. Vaccines and monoclonal antibody cocktails are available to treat Ebola virus (EBOV) infections, but not Sudan virus (SUDV) or other ebolaviruses. Current cocktails contain antibodies that cross-react with the secreted soluble glycoprotein (sGP) that absorbs virus-neutralizing antibodies. By sorting memory B cells from EBOV infection survivors, we isolated two broadly reactive anti-GP monoclonal antibodies, 1C3 and 1C11, that potently neutralize, protect rodents from disease, and lack sGP cross-reactivity. Both antibodies recognize quaternary epitopes in trimeric ebolavirus GP. 1C11 bridges adjacent protomers via the fusion loop. 1C3 has a tripartite epitope in the center of the trimer apex. One 1C3 antigen-binding fragment anchors simultaneously to the three receptor-binding sites in the GP trimer, and separate 1C3 paratope regions interact differently with identical residues on the three protomers. A cocktail of both antibodies completely protected nonhuman primates from EBOV and SUDV infections, indicating their potential clinical value.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Development of a VRC01-class germline targeting immunogen derived from anti-idiotypic antibodies

An effective HIV-1 vaccine will likely need to elicit broadly neutralizing antibodies (bNAbs). Broad and potent VRC01-class bNAbs have been isolated from multiple infected individuals, suggesting that they could be reproducibly elicited by vaccination. Several HIV-1 envelope-derived germline-targeting immunogens have been designed to engage naive VRC01-class precursor B cells. However, they also present off-target epitopes that could hinder development of VRC01-class bNAbs. We characterize a panel of anti-idiotypic monoclonal antibodies (ai-mAbs) raised against inferred-germline (iGL) VRC01-class antibodies. By leveraging binding, structural, and B cell sorting data, we engineered a bispecific molecule derived from two aimAbs; one specific for VRC01-class heavy chains and one specific for VRC01-class light chains. The bispecific molecule preferentially activates iGL-VRC01 B cells in vitro and induces specific antibody responses in a murine adoptive transfer model with a diverse polyclonal B cell repertoire. This molecule represents an alternative non-envelope-derived germline-targeting immunogen that can selectively activate VRC01-class precursors in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗
DOE OSTI2026

Computational methods in solution-based plastics purification

Plastic waste can be recycled into resins with near-virgin properties by solution-based purification processes that selectively dissolve polymers, remove contaminants, or detach printing residues. Here, in this review, we examine computational methods for predicting the behavior governing solution-based plastic purification, motivated by the vast polymer–solvent–contaminant compositional space. We discuss thermodynamic and machine learning methods for predicting polymer–solvent and polymer–contaminant interaction and review physics-based molecular dynamics simulations that resolve molecular-scale phenomena within polymer matrices inaccessible to screening methods. We highlight how these methods have informed experimental design for dissolution-based recycling and solvent-based contaminant removal. Finally, we discuss the prospective role of agentic AI in integrating these computational tools with real-time sorting data to adapt purification conditions to the compositional variability of real post-consumer feedstocks. This review charts a path toward computationally guided solution-based purification workflows that can respond to the complexity inherent in plastic waste streams.

Altamimi, Ali [Univ. of Wisconsin, Madison, WI (Un↗
DOE OSTI2019

Accounting for uncertainty in RCCE species selection

A framework is presented to quantify, based on Bayesian evidence, the relative plausibility of species se- lection options in rate-controlled constrained equilibrium (RCCE) reduced chemical models, accounting for uncertainty in the kinetic parameters and experimental data used to refine them. This approach bal- ances the joint goals of matching available data and avoiding overfitting, which is well-understood to limit extrapolative capacity for true prediction. The methodology is applied to homogeneous autoigni- tion, where predictions are known to be particularly sensitive to chemical model details, specially at low temperatures. It is first introduced for hydrogen–air autoignition using an established mechanism, then demonstrated in two applications of methane–air autoignition using the larger GRI-1.2 mechanism. This larger mechanism significantly increases the computational cost of model selection (though not of the subsequent application in predictions), which is alleviated with a time-scale-guided pre-sorting strategy. Uses and extensions of this new formulation are discussed.

08 HYDROGEN↗
DOE OSTI2020

Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework

Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ClassNMSW- a real-time classification approach for non-recycled municipal solid waste using hyperspectral imaging

Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

An Eulerian multimaterial framework for simulating high-explosive aquarium tests

Aquarium tests of cylindrical high-explosive charges provide optical data of the detonation front velocity and shape, propagation of the shock wave in the surrounding water, and expansion rates of the detonation products behind the front. Data from aquarium experiments is often used for calibration of reactive burn models based on phenomenological equations of state (EOS) and reaction rate laws. This paper presents a multimaterial numerical modeling framework to solve the 2D axisymmetric reactive Euler equations for high-explosive aquarium tests, in particular for ammonium nitrate - fuel oil (ANFO) explosives. An extension of the Ghost Fluid Method (GFM) is used to handle the dynamic material interfaces for the ANFO explosion products, the charge-confining material (polymethyl methacrylate PMMA), and the surrounding water. This study analyzes the sensitivity of calculations (both computational efficiency and numerical accuracy) to different algorithms for the material interface models including the original GFM versus Riemann solver-based strategies. A novel method for defining the left and right states in the interfacial Riemann problem eliminates the need for sorting or nodal interpolation during the projection along the material interface. Numerical tests indicate that populating the interface node values using the Riemann solution mitigate the overheating error observed in steady-state calculations. Solution convergence and computational efficiency are explored as a function of the spatial and temporal order of the schemes. Results from the computational model with analytical equations of state and fitted reaction rate parameters show very good quantitative agreement with experimentally observed detonation front velocity, reaction products expansion, and shock wave propagation in the surrounding water for a cylindrical ANFO charge encased in PMMA. Finally, the proposed modeling framework, in conjunction with experimental tests, provides a reliable tool to assess equations of state and reaction rate expressions for reactive burn models of confined high explosives.

42 ENGINEERING↗
DOE OSTI2019

Algorithms and algebraic solutions of decay chain differential equations for stable and unstable nuclide fractionation

Radioactive decay processes, such as alpha decay, produce decay chains where the mass numbers of nuclides decrease as larger nuclides expel energetic particles to form smaller nuclides. Under these conditions, the coefficient matrix that describes the differential rate expressions for radioactive decay can be made lower triangular. With this special structure, formulating an algebraic solution to the decay chains can be done by first formulating the eigenvectors that make up the coefficient matrix, which can then be solved using forward substitution for a lower triangular matrix. This work details the derivation of algebraic solutions for decay chains of any number of stable and unstable nuclides with any number of branching based on this eigenvector analysis. A prototype computational code was developed to validate and compare this methodology against a number of other methods for solving similar systems. A two-phase sorting algorithm yielding the lower triangular matrix structure was established to apply the developed algebraic solutions for decay chains involving beta-emitting radionuclides transformed into daughter nuclides without change in their mass number. In conclusion, the methodologies produced in this work provide an efficient way to estimate nuclide fractions from natural decay processes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Evolutionary multi-objective optimization and Pareto-frontal uncertainty quantification of interatomic forcefields for thermal conductivity simulations

Predictive Molecular Dynamics simulations of thermal transport require forcefields that can simultaneously reproduce several structural, thermodynamic and vibrational properties of materials like lattice constants, phonon density of states, and specific heat. This requires a multi-objective optimization approach for forcefield parameterization. Existing methodologies for forcefield parameterization use ad-hoc and empirical weighting schemes to convert this into a single-objective optimization problem. Here, we provide and describe software to perform multi-objective optimization of Stillinger–Weber forcefields (SWFF) for two-dimensional layered materials using the recently developed 3rd generation non-dominated sorting genetic algorithm (NSGA-III). NSGA-III converges to the set of optimal forcefields lying on the Pareto front in the multi-dimensional objective space. This set of forcefields is used for uncertainty quantification of computed thermal conductivity due to variability in the forcefield parameters. We demonstrate this new optimization scheme by constructing a SWFF for a representative two-dimensional material, 2H-MoSe 2 and quantifying the uncertainty in their computed thermal conductivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Non-photosynthetic lineages sibling to Cyanobacteria associate with eukaryotes in the open ocean

Margulisbacteria are elusive uncultivated bacteria that have illuminated evolutionary transitions in the progenitor of Cyanobacteria, the latter being a critically important phylum that underpins oxygenic photosynthesis. The non-photosynthetic Margulisbacteria were discovered in a sulfidic spring and later in other habitats. Currently, this candidate phylum partitions into the Riflemargulisbacteria, primarily from sediments and groundwater, the Termititenax from insect gut microbiomes, and the Marinamargulisbacteria, from marine samples. We found that Marinamargulisbacteria amplicons were unusually distributed in size-fractionated samples from the sunlit photic and dark twilight zones of the ocean. Further, sequencing of wild marine protists rendered genomic information for distinct marinamargulisbacterial clades co-associated with uncultivated, non-photosynthetic Stramenopila and Opisthokonta protists. Phylogenomic analyses combining these data and available metagenome-assembled genomes (MAGs) and single-amplified genomes (SAGs) from sorted bacteria revealed new Marinamargulisbacteria lineages. The lineages delineate by their environment, forming clades comprising freshwater, marine pelagic, or sediment/hypoxic taxa. In conclusion, the remarkable diversity of Margulisbacteria indicates success in colonizing various habitats, potentially in a conserved strategy involving eukaryotic cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Home energy management (HEM) database: A list with coded attributes of 308 devices commercially available in the US

Policymakers worldwide are currently discussing whether to include home energy management (HEM) products in their portfolio of technologies to reduce carbon emissions and improve grid reliability. However, very little data is available about these products. Here we present the results of an extensive review including 308 HEM products available on the US market in 2015–2016. We gathered these data from publicly available sources such as vendor websites, online marketplaces and other vendor documents. A coding guide was developed iteratively during the data collection and utilized to classify the devices. Each product was coded based on 96 distinct attributes, grouped into 11 categories: Identifying information, Product components, Hardware, Communication, Software, Information - feedback, Information - feedforward, Control, Utility interaction, Additional benefits and Usability. The codes describe product features and functionalities, user interaction and interoperability with other devices. A mix of binary attributes and more descriptive codes allow to sort and group data without losing important qualitative information. The information is stored in a large spreadsheet included with this article, along with an explanatory coding guide. This dataset is analyzed and described in a research article entitled “Categories and functionality of smart home technology for energy management” (Ford et al., 2017) [1].

Home automation↗
DOE OSTI2026

Intelligent industrial demand response to increase grid flexibility and reliability: A review

The rapid transition toward renewable energy has introduced challenges in grid stability due to the intermittency of non-dispatchable sources like solar and wind. Industrial Demand Response (IDR) offers a promising, cost-effective solution that adjusts energy consumption patterns to align with supply, increases renewable utilization, and reduces costs. This review provides an updated analysis of IDR, sorting technologies into five categories: energy storage, scheduled energy usage, operational flexibility, on-site generation, and intelligent operations. Energy storage solutions, while requiring little flexibility, often have the longest payback periods. While slightly better, on-site generation also has longer payback periods, ranging from 5 to 20 years or more. Scheduled energy usage, operational flexibility, and intelligent operations allow significant peak reduction at lower capital costs but require greater flexibility. While 15–20 % peak reduction is within the range of all five categories, scheduled energy use and on-site energy generation are shown to have reductions of up to 70–80 % in select scenarios. Combining multiple IDR strategies from these five categories maximizes both financial and operational benefits. Synergistic approaches are shown to enhance grid stability while reducing costs. As the grid evolves, IDR will enable a more flexible, renewable-powered future that will benefit industrial facilities and the broader energy system.

Demand flexibility↗
DOE OSTI2026

Spatial and temporal characterization of municipal solid waste based on resource recovery pathways

This study presents a two-year, quarterly assessment of MSW across four source sectors (residential, schools, restaurants, and grocery stores) from sixteen sites across five U.S. states. MSW was manually sorted into 27 categories and aggregated into pathway fractions: high-moisture (HM) organics, low-moisture (LM) organics, recyclable (RC) materials, and residuals for disposal. Organics represented 89 % of the MSW stream. The largest fraction was HM organics consisting of food waste (31 %) and yard waste (3 %), with large coefficient of variations (CV), 79 and 278 %, respectively, reflecting high seasonal and site variability that varied significantly (p < 0.01) across sampling periods. The HM fraction showed properties favorable for anaerobic digestion, with moisture content ranging from 56 to 95 % and volatile solids ranges of 86-95 %. In contrast, the LM and RC fractions remained more stable (plastics CV = 41 %; paper CV = 53 %) with heating values up to 26.9 MJ/kg across sources, reflecting suitability for gasification. Microstructural analysis revealed less porosity in residential waste sampled at the landfill, which can influence preprocessing efficiency and microbial accessibility. Pathway informed allocations showed that 35 % of MSW is suitable for anaerobic digestion, 36 % for gasification, and 18 % for recycling, leaving 11 % requiring landfill disposal. These results provide quantitative evidence to determine feedstock allocation, waste-to-energy system design, and the development of data-driven sustainability and resource recovery strategies within a circular bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2017

Low doses and non-targeted effects in environmental radiation protection; where are we now and where should we go?

The field of low dose radiobiology has advanced considerably in the last 30 years from small indications in the 1980's that all was not simple, to a paradigm shift which occurred during the 1990's, which severely dented the dose-driven models and DNA centric theories which had dominated until then. However while the science has evolved, the application of that science in environmental health protection has not. A reason for this appears to be the uncertainties regarding the shape of the low dose response curve, which lead regulators to adopt a precautionary approach to radiation protection. Radiation protection models assume a linear relationship between dose (i.e. energy deposition) and effect (in this case probability of an adverse DNA interaction leading to a mutation). This model does not consider non-targeted effects (NTE) such as bystander effects or delayed effects, which occur in progeny cells or offspring not directly receiving energy deposition from the dose. There is huge controversy concerning the role of NTE with some saying they reflect “biology” and that repair and homeostatic mechanisms sort out the apparent damage while others consider them to be a class of damage which increases the size of the target. One thing which has recently become apparent is that NTE may be very critical for modelling long-term effects at the level of the population rather than the individual. The issue is that NTE resulting from an acute high dose such as occurred after the A-bomb or Chernobyl occur in parallel with chronic effects induced by the continuing residual effects due to radiation dose decay. This means that if ambient radiation doses are measured for example 25 years after the Chernobyl accident, they only represent a portion of the dose effect because the contribution of NTE is not included. - Highlights: • This paper presents a new concept in Environmental Radiobiology, which could explain why effects are detected in Chernobyl and Fukushima wildlife at ambient doses which are very low. • We suggest that non-targeted effects such as genomic instability, lethal mutations and bystander signalling, contribute a persistent legacy dimension to the total measured effect. • This “historic dose” component needs to be incorporated into dose effect models in order to predict true levels of risk.

54 ENVIRONMENTAL SCIENCES↗