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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3–4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Metadata for a systematic description of signal data

This chapter aims to provide a comprehensive overview of metadata types that may be useful during system design, optimization, and automation. Metadata are grouped into three main categories: (a) metadata describing signal generation, (b) metadata describing signal quality, and (c) contextual information in the form of annotations. Each of these categories is introduced and explained in three separate sections. Importantly, this chapter mainly answers what is considered metadata. To a lesser degree, recommendations are made regarding the selection of metadata for long-term storage. Chapter 4 will explain where and how to store metadata. Chapters 5 and 6 explain how to collect certain metadata through dedicated sensor validation tests (Chapter 5) or algorithmic analysis (Chapter 6).

Alferes, Janelcy↗
DOE OSTI2020

Nuclear Criticality Safety Fundamentals [Slides]

Objectives of this presentation are: 1. Explain the difference between radiation safety vs. criticality safety, 2. Define criticality, 3. Explain the three types of chain reactions, 4. Define two terms scientists use to characterize criticality, 5. Explain what occurs during a criticality accident, 6. Identify prevention measures for avoiding a criticality accident, and 7. Identify the parameters that effect criticality of a system.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effect of Microstructural Bands on the Localized Corrosion of Laser Surface-melted 316L Stainless Steel

The localized corrosion of laser surface melted (LSM) 316L stainless steel is investigated by a combination of potentiodynamic anodic polarization in 0.1M HCl and microscopic investigation of the initiation and propagation of localized corrosion. The pitting potential of LSM 316L is significantly lower than the pitting potential of wrought 316L. The LSM microstructure is highly banded as a consequence of the high laser power density and high linear energy density. The bands are composed of zones of changing modes of solidification, cycling between very narrow regions of primary austenite solidification and very wide regions of primary ferrite solidification. Pits initiate in the outer edge of each band where the mode of solidification is primary austenite plane front solidification and primary austenite cellular solidification. The primary austenite regions have low chromium concentration (and possibly low molybdenum concentration), which explains their susceptibility to pitting corrosion. The ferrite is enriched in chromium, which explains the absence of pitting in the primary ferrite regions. The presence of the low chromium regions of primary austenite solidification explains the lower pitting resistance of LSM 316L relative to wrought 316L. The influence of banding on localized corrosion is applicable to other rapidly solidified processes such as additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Flexoelectricity and New Phenomena

It is easy to miss the scientific implications of our recent work on Triboelectricity. Everyone knows that rubbing and contact can produce static electricity; less appreciated is that the thermodynamic driver has been an open question since static electricity was first observed by Thales of Miletus around 585 BC. People think they understand it, for instance one common explanation that can still be found in the current literature is that differences in the work function drives charge transfer, often called the Volta-Helmholtz hypothesis. As summarized in 1967 by Harper, this fails to explain many experimental observations, for instance that charging can occur when two pieces of the same material are rubbed against each other. We are the first to place triboelectricity on a solid foundation rooted in quantum mechanics – the flexoelectric effect. We were able to explain a significant number of previously unexplained phenomena: 1) Bipolar tribocurrents associated with stick-slip, due to the change in sign of the strain gradients. 2) A one-third power scaling of tribocurrents with indentation force. 3) Tribocharging when two identical materials are used, these being due to local variations in the asperities so there are usually local potential differences. 4) Inhomogeneous charging of insulators, related to the statistical nature of asperities. 5) An experimentally observed reversal in the sign of charge transfer for negative and positive curvature, which is related to a change in the sign of the strain gradient. Exploiting our DOE prior funded work on flexoelectricity, we obtained semi-quantitative matching to existing experimental measurements of the surface charge in triboelectric experiments. The work has been well received in the literature. The work has been the focus of a number of popular science press articles, and also formed the basis for a Podcast for children 6-10 “The Rise and Fall of Static Man” posted in December 2019 by NPR as part of their “Wow in the World” series. I was also briefly interviewed by the Chicago PBS station in January 2020. This work are significant for a wide range of energy applications; to quote from an independent source: "Triboelectric power has plenty of potential, says Wenzhuo Wu, an assistant professor of engineering at Purdue. If the basics of static electricity are better understood, we could maximize the efficiency of wind or wave power generators, Wu says. The body's own movement could be used to power internal medical devices. Imagine being able to create a roof shaped to harness the power of a raindrop — the friction of the rain passing over the surface — to generate triboelectricity, powering the building below it." This is the start of new science, some of which we already partially understand such as the role of band bending in charge transfer. We need to understand charge transfer combining elasticity, quantum mechanics, band bending and defect states. These directly involve several of the DOE Grand Challenges: "How do we control material processes at the level of electrons? How do remarkable properties of matter emerge from complex correlations of the atomic or electronic constituents and how can we control these properties? How do we characterize and control matter away—especially very far away— from equilibrium?" I will argue that this work truly falls into the class of disruptive science; it is not just a simple extension, linear science. Not everyone will accept the approach. Since we explain far more about triboelectricity than anyone before, the preponderance of evidence supports the model. The feedback I have received is that many agree with the work, to quote: "The model makes sense, says Michael McAlpine, a professor of engineering at the University of Minnesota. "It's such a simple explanation, I was surprised I didn't put my finger on that," McAlpine says." The proposal received strong reviews. It was also publicized on the Department of Energy Web Site.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Understanding Twinning and Deformation in High Entropy Alloys

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE cor- relations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. This model opens a possibility to use charge density as a descriptor for predicting SFE in alloys. Furthermore, a descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Utility of Macroecological Rules for Microbial Biogeography

Macroecological rules have been developed for plants and animals that describe large-scale distributional patterns and attempt to explain the underlying physiological and ecological processes behind them. Similarly, microorganisms exhibit patterns in relative abundance, distribution, diversity, and traits across space and time, yet it remains unclear the extent to which microorganisms follow macroecological rules initially developed for macroorganisms. Additionally, the usefulness of these rules as a null hypothesis when surveying microorganisms has yet to be fully evaluated. With rapid advancements in sequencing technology, we have seen a recent increase in microbial studies that utilize macroecological frameworks. Here, we review and synthesize these macroecological microbial studies with two main objectives: (1) to determine to what extent macroecological rules explain the distribution of host-associated and free-living microorganisms, and (2) to understand which environmental factors and stochastic processes may explain these patterns among microbial clades (archaea, bacteria, fungi, and protists) and habitats (host-associated and free living; terrestrial and aquatic). Overall, 78% of microbial macroecology studies focused on free living, aquatic organisms. In addition, most studies examined macroecological rules at the community level with only 35% of studies surveying organismal patterns across space. At the community level microorganisms often tracked patterns of macroorganisms for island biogeography (74% confirm) but rarely followed Latitudinal Diversity Gradients (LDGs) of macroorganisms (only 32% confirm). However, when microorganisms and macroorganisms shared the same macroecological patterns, underlying environmental drivers (e.g., temperature) were the same. Because we found a lack of studies for many microbial groups and habitats, we conclude our review by outlining several outstanding questions and creating recommendations for future studies in microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Genomic Diversity Evaluation of Populus trichocarpa Germplasm for Rare Variant Genetic Association Studies

Genome-wide association studies are powerful tools to elucidate the genome-to-phenome relationship. In order to explain most of the observed heritability of a phenotypic trait, a sufficient number of individuals and a large set of genetic variants must be examined. The development of high-throughput technologies and cost-efficient resequencing of complete genomes have enabled the genome-wide identification of genetic variation at large scale. As such, almost all existing genetic variation becomes available, and it is now possible to identify rare genetic variants in a population sample. Rare genetic variants that were usually filtered out in most genetic association studies are the most numerous genetic variations across genomes and hold great potential to explain a significant part of the missing heritability observed in association studies. Rare genetic variants must be identified with high confidence, as they can easily be confounded with sequencing errors. In this study, we used a pre-filtered data set of 1,014 pure Populus trichocarpa entire genomes to identify rare and common small genetic variants across individual genomes. We compared variant calls between Platypus and HaplotypeCaller pipelines, and we further applied strict quality filters for improved genetic variant identification. Finally, we only retained genetic variants that were identified by both variant callers increasing calling confidence. Based on these shared variants and after stringent quality filtering, we found high genomic diversity in P. trichocarpa germplasm, with 7.4 million small genetic variants. Importantly, 377k non-synonymous variants (5% of the total) were uncovered. We highlight the importance of genomic diversity and the potential of rare defective genetic variants in explaining a significant portion of P. trichocarpa's phenotypic variability in association genetics. The ultimate goal is to associate both rare and common alleles with poplar's wood quality traits to support selective breeding for an improved bioenergy feedstock.

Genetics & Heredity↗
DOE OSTI2021

Modeling HIV-1 Within-Host Dynamics After Passive Infusion of the Broadly Neutralizing Antibody VRC01

VRC01 is a broadly neutralizing antibody that targets the CD4 binding site of HIV-1 gp120. Passive administration of VRC01 in humans has assessed the safety and the effect on plasma viremia of this monoclonal antibody (mAb) in a phase 1 clinical trial. After VRC01 infusion, the plasma viral load in most of the participants was reduced but had particular dynamics not observed during antiretroviral therapy. In this paper, we introduce different mathematical models to explain the observed dynamics and fit them to the plasma viral load data. Based on the fitting results we argue that a model containing reversible Ab binding to virions and clearance of virus-VRC01 complexes by a two-step process that includes (1) saturable capture followed by (2) internalization/degradation by phagocytes, best explains the data. This model predicts that VRC01 may enhance the clearance of Ab-virus complexes, explaining the initial viral decay observed immediately after antibody infusion in some participants. Because Ab-virus complexes are assumed to be unable to infect cells, i.e., contain neutralized virus, the model predicts a longer-term viral decay consistent with that observed in the VRC01 treated participants. By assuming a homogeneous viral population sensitive to VRC01, the model provides good fits to all of the participant data. However, the fits are improved by assuming that there were two populations of virus, one more susceptible to antibody-mediated neutralization than the other.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

TOI-1634 b: An Ultra-short-period Keystone Planet Sitting inside the M-dwarf Radius Valley

Studies of close-in planets orbiting M dwarfs have suggested that the M-dwarf radius valley may be well explained by distinct formation timescales between enveloped terrestrials and rocky planets that form at late times in a gas-depleted environment. This scenario is at odds with the picture that close-in rocky planets form with a primordial gaseous envelope that is subsequently stripped away by some thermally driven mass-loss process. These two physical scenarios make unique predictions of the rocky/enveloped transition’s dependence on orbital separation such that studying the compositions of planets within the M-dwarf radius valley may be able to establish the dominant physics. Here, we present the discovery of one such keystone planet: the ultra-short-period planet TOI-1634 b (P = 0.989 days, F=121F{sub ⊕}, r{sub p}=1.790{sub -0.081}{sup +0.080} R {sub ⊕}) orbiting a nearby M2 dwarf (K {sub s} = 8.7, R {sub s} = 0.450 R {sub ⊙}, M {sub s} = 0.502 M {sub ⊙}) and whose size and orbital period sit within the M-dwarf radius valley. We confirm the TESS-discovered planet candidate using extensive ground-based follow-up campaigns, including a set of 32 precise radial velocity measurements from HARPS-N. We measure a planetary mass of 4.91{sub -0.70}{sup +0.68} M {sub ⊕}, which makes TOI-1634 b inconsistent with an Earth-like composition at 5.9σ and thus requires either an extended gaseous envelope, a large volatile-rich layer, or a rocky composition that is not dominated by iron and silicates to explain its mass and radius. The discovery that the bulk composition of TOI-1634 b is inconsistent with that of Earth supports the gas-depleted formation mechanism to explain the emergence of the radius valley around M dwarfs with M{sub s}≲0.5 M {sub ⊙}.

36 MATERIALS SCIENCE↗
DOE OSTI2021

It’s Dust: Solving the Mysteries of the Intrinsic Scatter and Host-galaxy Dependence of Standardized Type Ia Supernova Brightnesses

The use of Type Ia supernovae (SNe Ia) as cosmological tools has motivated significant effort to understand what drives the intrinsic scatter of SN Ia distance modulus residuals after standardization, characterize the distribution of SN Ia colors, and explain why properties of the host galaxies of the SNe correlate with SN Ia distance modulus residuals. We use a compiled sample of ~1450 spectroscopically confirmed photometric light curves of SNe Ia and propose a solution to these three problems simultaneously that also explains an empirical 11 σ detection of the dependence of Hubble residual scatter on SN Ia color. We introduce a physical model of color where intrinsic SN Ia colors with a relatively weak correlation with luminosity are combined with extrinsic dust-like colors ( E ( B - V )) with a wide range of extinction parameter values ( R V ). This model captures the observed trends of Hubble residual scatter and indicates that the dominant component of SN Ia intrinsic scatter is variation in R V . We also find that the recovered E ( B - V ) and R V distributions differ based on global host-galaxy stellar mass, and this explains the observed correlation ( γ ) between mass and Hubble residuals seen in past analyses, as well as an observed 4.5 σ dependence of γ on SN Ia color. This finding removes any need to ascribe different intrinsic luminosities to different progenitor systems. Finally, we measure biases in the equation of state of dark energy ( w ) up to |Δ w | = 0.04 by replacing previous models of SN color with our dust-based model; this bias is larger than any systematic uncertainty in previous SN Ia cosmological analyses.

Astronomy & Astrophysics↗
DOE OSTI2023

The Early Light Curve of the Type Ia Supernova 2021hpr in NGC 3147: Progenitor Constraints with the Companion Interaction Model

The progenitor system of Type Ia supernovae (SNe Ia) is expected to be a close binary system consisting of a carbon/oxygen white dwarf (WD) and a nondegenerate star or another WD. Here, we present results from high-cadence monitoring observations of SN 2021hpr in a spiral galaxy, NGC 3147, and constraints on the progenitor system based on its early multicolor light-curve data. First, we classify SN 2021hpr as a normal SN Ia from its long-term photometric and spectroscopic data. More interestingly, we found a significant "early excess" in the light curve over a simple power-law ~t 2 evolution. The early light curve evolves from blue to red to blue during the first week. To explain this, we fitted the early part of the BVRI-band light curves with a two-component model consisting of ejecta–companion interaction and a simple power-law model. The early excess and its color can be explained by shock-cooling emission due to a companion star having a radius of 8.84 ± 0.58 R ⊙ . We also examined Hubble Space Telescope preexplosion images, finding no detection of a progenitor candidate, consistent with the above result. However, we could not detect signs of a significant amount of stripped mass from a nondegenerate companion star (≲0.003 M ⊙ for Hα emission). The early excess light in the multiband light curve supports a nondegenerate companion in the progenitor system of SN 2021hpr. At the same time, the nondetection of emission lines opens the door for other methods to explain this event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Fuzzball Paradigm

We describe the puzzles that arise in the quantum theory of black holes, and explain how they are resolved in string theory. We review how the Bekenstein entropy is obtained through the count of brane bound states. We describe the fuzzball construction of black hole microstates. These states have no horizon and radiate from their surface like a normal body, so there is no information puzzle. We explain how the semiclassical approximation is violated in gravitational collapse even though curvatures are low at the classical horizon. This violation happens because the collapse leads to a stretching of space that is {\it fast}: light does not have time to travel across the collapsing region to establish the `vecro' correlations needed in the quantum gravitational vacuum. These vecro correlations arise from the existence of virtual fuzzball fluctuations in the gravitational vacuum, and are significant because of the large degeneracy of fuzzball states implied by the Bekenstein entropy. It is plausible that similar effects of fast expansion may be responsible for effects like dark energy and the Early Dark Energy postulated to explain the Hubble tension.

FOS: Physical sciences↗
NASA NTRS1974

Reflectance of vegetation, soil, and water

The author has identified the following significant results. The Kubelka-Munk model, a regression model, and a combination of these models were used to extract plant, soil, and shadow reflectance components of vegetated surfaces. The combination model was superior to the others; it explained 86% of the variation in band 5 reflectance of corn and sorghum, and 90% of the variation in band 6 reflectance of cotton. A fractional shadow term substantially increased the proportion of the digital count sum of squares explained when plant parameters alone explained 85% or less of the variation. Overall recognition of 94 agricultural fields using simultaneously acquired aircraft and spacecraft MSS data was 61.8 and 62.8%, respectively; recognition of vegetable fields larger than 10 acres and taller than 25 cm, rose to 88.9 and 100% for aircraft and spacecraft, respectively. Agriculture and rangeland, were well discriminated for the entire county but level 2 categories of vegetables, citrus, and idle cropland, except for citrus, were not.

Wiegand, C. L.↗
NASA NTRS1975

Comments on "Venus' spectroscopic phase variation: Implications of the Mariner 10 photographs

From a misinterpretation of Mariner 10 pictures, Chamberlain (1974) constructed a model in which he used horizontal variations in the Venus atmosphere to explain the phase-variation of CO2 absorption at small phase angles. Published observations of spatial variations in CO2 absorption on Venus show that they are too small to explain the phase effect. The question of uniqueness that is, if more than one model can explain the phase-effect observations is discussed. Before this question can be answered, a realistic model that does account for the data must be constructed. No such calculations exist.

Young, A. T.↗
NASA NTRS1975

The ionosphere and upper atmosphere of Venus

A summary is presented of current understanding of the upper atmosphere and ionosphere of Venus and its interaction with the solar wind, based on data from the Mariner 5 and Mariner 10 fly-bys and on far UV spectra obtained in rocket experiments. The major constituent of the upper atmosphere is CO2. Minor constituents include H, He, O, C, and CO and probably N2, Cl, and S. Although the thermal escape rate is only about 10,000/sq cm/sec, the H content in the exosphere appears to be highly variable. A prominent peak in the ionosphere profile near 140 km, appearing both on the day and nightside, is identified as an F(1) layer. An E layer and possibly an F(2) layer are present at 125 and 170 km, respectively. The dayside ionosphere may be explained in terms of the absorption of solar radiation by CO2, O, and He. The transport of ions from day to nightside may be important in the formation of the nightside ionosphere; an additional source may be needed to explain the nightside E layer. There is observational evidence that the solar wind interacts directly with the Venusian atmosphere, resulting in the formation of a bow shock. This may in part be explained by a balance at the ionopause between the solar wind ram pressure and the planetary plasma pressure.

Kumar, S.↗
NASA NTRS1976

Thorium and uranium variations in Apollo 17 basalts, and K-U systematics

It is found that Apollo 11 low-K and in particular Apollo 17 mare basalts show a wide range of Th/U ratios unlike other rocks; such variations cannot be explained by near surface crystal fractionation. A two-stage fractional crystallization-partial melting model involving a clinopyroxene cumulate as the major phase can explain the variations in Th/U ratios. Due to the Sm-Nd systematics constraint, several source cumulates are invoked to explain the observed Th/U continuum.

Laul, J. C.↗