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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Influence of intrinsic and extrinsic attributes on neonate survival in an invasive large mammal

Abstract Understanding factors influencing survival of neonates for wild species is important for successful management, particularly for determining drivers of population dynamics. Wild pigs ( Sus scrofa ) are invasive and populations are rapidly increasing in part due to high reproductive capacity. Survival of adults is generally high, however, survival of piglets, and particularly neonates, is largely unknown. We located neonates at the natal nest and quantified survival in relation to individual and maternal biological attributes, and environmental variables. During 2017–2020, we captured 50 neonates from 13 litters and documented 28 mortalities (56%) over six weeks. Survival was positively influenced by pelage coloration, likely as a form of camouflage from predators. Male neonates had higher survival. They were born larger than females, which could be beneficial for thermoregulation and competition for milk. Neonates born to larger sows had lower survival. Sow size was positively correlated with litter size, and this finding may reflect the increased nutritional demands of sustaining large litters, or difficulties in defending more neonates against predators. Neonates born in warmer months had higher survival than those born in cooler months. Neonates are inefficient thermoregulators, thus being born in warmer months could be beneficial for maintaining homeostasis as well as access to more food resources. These are the largest and most complete data for neonate wild pig survival and will inform population models for the development of management strategies to reduce negative impacts of this destructive invasive species on native ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structural insight into host plasma membrane association and assembly of HIV-1 matrix protein

Oligomerization of Pr55 Gag is a critical step of the late stage of the HIV life cycle. It has been known that the binding of IP6, an abundant endogenous cyclitol molecule at the MA domain, has been linked to the oligomerization of Pr55 Gag . However, the exact binding site of IP6 on MA remains unknown and the structural details of this interaction are missing. Here, we present three high-resolution crystal structures of the MA domain in complex with IP6 molecules to reveal its binding mode. Additionally, extensive Differential Scanning Fluorimetry analysis combined with cryo- and ambient-temperature X-ray crystallography and GNM-based transfer entropy calculations identify the key residues that participate in IP6 binding. Our data provide novel insights about the multilayered HIV-1 virion assembly process that involves the interplay of IP6 with PIP2, a phosphoinositide essential for the binding of Pr55 Gag to membrane. IP6 and PIP2 have neighboring alternate binding sites within the same highly basic region (residues 18–33). This indicates that IP6 and PIP2 bindings are not mutually exclusive and may play a key role in coordinating virion particles’ membrane localization. Based on our three different IP6-MA complex crystal structures, we propose a new model that involves IP6 coordination of the oligomerization of outer MA and inner CA domain’s 2D layers during assembly and budding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Thermo-mechanical characterization of shale using nanoindentation

Abstract Shale can be a potential buffer for high-level radioactive nuclear wastes. To be an effective buffer while subject to waste heat, shale's mechanical response at elevated temperature must be known. Many researchers have experimentally characterized the mechanical behavior of various shales at different length scales in adiabatic conditions. However, its mechanical performance at elevated temperatures at the nano-scale remains unknown. To investigate the temperature dependency of nanomechanical properties of shale, we conducted both experimental and numerical studies. In this study, we measured mechanical and fracture properties of shale, such as hardness, elastic modulus, anisotropy, and fracture toughness from 25 °C up to 300 °C at different bedding planes. Statistical analysis of the results suggests that hardness and fracture toughness significantly increased at temperatures from 100 to 300 °C; while, temperature does not have a significant impact on elastic modulus. Data also shows that the bedding plane orientations have a substantial impact on both mechanical and fracture properties of shale at the nano-scale leading to distinct anisotropic behavior at elevated temperature below 100 °C. Additionally, we numerically investigated the mechanical performance of the shale samples at room temperature to gain an insight into its mechanical response through the thickness. Numerical results were validated against the experimental results, confirming the simulation can be used to predict shale deformation at the nano-scale or potentially be used in multi-scale simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of catalytic nitrile decomposition in tricopper complex mediated direct partial oxidation of methane to methanol

Synthetic homogeneous system known to date performing methane to methanol conversion using O 2 as terminal oxidant is unique and based on copper complex with piperazine-based ligand (Cu 3 L in Fig. 1) in a medium of acetonitrile. Prior work have shown that in order to achieve catalytic turnover, hydrogen peroxide is needed to regenerate the active site. We show in this paper that reaction solvent based on organic nitrile decompose concurrently with methane activation and that in the absence of either acetonitrile, Cu complex or hydrogen peroxide, the catalytic turnover does not happen. We show in this manuscript that the direct methane oxidation to methanol might have been mediated by catalytic Radziszewski oxidation between acetonitrile and H 2 O 2 . Additionally we have discovered that in the absence of methane, peroxide mediated acetonitrile decomposition also makes methanol via a background reaction which was hitherto unknown.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients

Abstract Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Photosystem stoichiometry adjustment is a photoreceptor-mediated process in Arabidopsis

Abstract Plant growth under spectrally-enriched low light conditions leads to adjustment in the relative abundance of the two photosystems in an acclimatory response known as photosystem stoichiometry adjustment. Adjustment of photosystem stoichiometry improves the quantum efficiency of photosynthesis but how this process perceives light quality changes and how photosystem amount is regulated remain largely unknown. By using a label-free quantitative mass spectrometry approach in Arabidopsis here we show that photosystem stoichiometry adjustment is primarily driven by the regulation of photosystem I content and that this forms the major thylakoid proteomic response under light quality. Using light and redox signaling mutants, we further show that the light quality-responsive accumulation of photosystem I gene transcripts and proteins requires phytochrome B photoreceptor but not plastoquinone redox signaling as previously suggested. In far-red light, the increased acceptor side limitation might deplete active photosystem I pool, further contributing to the adjustment of photosystem stoichiometry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structure of the pre-mRNA leakage 39-kDa protein reveals a single domain of integrated zf-C3HC and Rsm1 modules

In Saccharomyces cerevisiae, the pre-mRNA leakage 39-kDa protein (ScPml39) was reported to retain unspliced pre-mRNA prior to export through nuclear pore complexes (NPCs). Pml39 homologs outside the Saccharomycetaceae family are currently unknown, and mechanistic insight into Pml39 function is lacking. Here we determined the crystal structure of ScPml39 at 2.5 Å resolution to facilitate the discovery of orthologs beyond Saccharomycetaceae, e.g. in Schizosaccharomyces pombe or human. The crystal structure revealed integrated zf-C3HC and Rsm1 modules, which are tightly associated through a hydrophobic interface to form a single domain. Both zf-C3HC and Rsm1 modules belong to the Zn-containing BIR (Baculovirus IAP repeat)-like super family, with key residues of the canonical BIR domain being conserved. Features unique to the Pml39 modules refer to the spacing between the Zn-coordinating residues, giving rise to a substantially tilted helix αC in the zf-C3HC and Rsm1 modules, and an extra helix αAB' in the Rsm1 module. Conservation of key residues responsible for its distinct features identifies S. pombe Rsm1 and Homo sapiens NIPA/ZC3HC1 as structural orthologs of ScPml39. Based on the recent functional characterization of NIPA/ZC3HC1 as a scaffold protein that stabilizes the nuclear basket of the NPC, our data suggest an analogous function of ScPml39 in S. cerevisiae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Floquet engineering of tilted and gapped Dirac bandstructure in 1T$$^\prime$$-MoS$$_2$$

Abstract We have developed a rigorous theoretical formalism for Floquet engineering, investigating, and subsequently tailoring most crucial electronic properties of 1T $$^\prime$$ ′ -MoS $$_2$$ 2 by applying an external high-frequency dressing field within the off-resonance regime. It was recently demonstrated that monolayer semiconducting 1T $$^\prime$$ ′ -MoS $$_2$$ 2 exhibits tunable and gapped spin- and valley-polarized tilted Dirac bands. The electron-photon dressed states depend strongly on the polarization of the applied irradiation and reflect a full complexity of the low-energy Hamiltonian for non-irradiated material. We have calculated and analyzed the properties of the electron dressed states corresponding to linear and circular polarization of a dressing field by focusing on their symmetry, anisotropy, tilting, direct and indirect band gaps. Circularly polarized dressing field is known for transition into a new electronic state with broken time-reversal symmetry and a non-zero Chern number, and therefore, the combination of these topologically non-trivial phases and transitions between them could reveal some truly unique and previously unknown phenomena and applications. We have also computed and discussed the density of states for various types of 1T $$^\prime$$ ′ -MoS $$_2$$ 2 materials and its modification in the presence of a dressing field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The onset of rare earth metallosis begins with renal gadolinium-rich nanoparticles from magnetic resonance imaging contrast agent exposure

The leitmotifs of magnetic resonance imaging (MRI) contrast agent-induced complications range from acute kidney injury, symptoms associated with gadolinium exposure (SAGE)/gadolinium deposition disease, potentially fatal gadolinium encephalopathy, and irreversible systemic fibrosis. Gadolinium is the active ingredient of these contrast agents, a non-physiologic lanthanide metal. The mechanisms of MRI contrast agent-induced diseases are unknown. Mice were treated with a MRI contrast agent. Human kidney tissues from contrast-naïve and MRI contrast agent-treated patients were obtained and analyzed. Kidneys (human and mouse) were assessed with transmission electron microscopy and scanning transmission electron microscopy with X-ray energy-dispersive spectroscopy. MRI contrast agent treatment resulted in unilamellar vesicles and mitochondriopathy in renal epithelium. Electron-dense intracellular precipitates and the outer rim of lipid droplets were rich in gadolinium and phosphorus. We conclude that MRI contrast agents are not physiologically inert. The long-term safety of these synthetic metal–ligand complexes, especially with repeated use, should be studied further.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Extensive 3D mapping of dislocation structures in bulk aluminum

Abstract Thermomechanical processing such as annealing is one of the main methods to tailor the mechanical properties of materials, however, much is unknown about the reorganization of dislocation structures deep inside macroscopic crystals that give rise to those changes. Here, we demonstrate the self-organization of dislocation structures upon high-temperature annealing in a mm-sized single crystal of aluminum. We map a large embedded 3D volume ( $$100\times 300\times 300$$ 100 × 300 × 300 $$\upmu$$ μ m $$^3$$ 3 ) of dislocation structures using dark field X-ray microscopy (DFXM), a diffraction-based imaging technique. Over the wide field of view, DFXM’s high angular resolution allows us to identify subgrains, separated by dislocation boundaries, which we identify and characterize down to the single-dislocation level using computer-vision methods. We demonstrate how even after long annealing times at high temperatures, the remaining low density of dislocations still pack into well-defined, straight dislocation boundaries (DBs) that lie on specific crystallographic planes. In contrast to conventional grain growth models, our results show that the dihedral angles at the triple junctions are not the predicted 120 $$^{\circ }$$ ∘ , suggesting additional complexities in the boundary stabilization mechanisms. Mapping the local misorientation and lattice strain around these boundaries shows that the observed strain is shear, imparting an average misorientation around the DB of $$\approx$$ ≈ 0.003 to 0.006 $$^{\circ }$$ ∘ .

36 MATERIALS SCIENCE↗
DOE OSTI2023

The minimal SUF system is not required for Fe–S cluster biogenesis in the methanogenic archaeon Methanosarcina acetivorans

Abstract Iron–sulfur (Fe–S) proteins are essential for the ability of methanogens to carry out methanogenesis and biological nitrogen fixation (diazotrophy). Nonetheless, the factors involved in Fe–S cluster biogenesis in methanogens remain largely unknown. The minimal SUF Fe–S cluster biogenesis system (i.e., SufBC) is postulated to serve as the primary system in methanogens. Here, the role of SufBC in Methanosarcina acetivorans , which contains two sufCB gene clusters, was investigated. The CRISPRi-dCas9 and CRISPR-Cas9 systems were utilized to repress or delete sufC1B1 and sufC2B2 , respectively. Neither the dual repression of sufC1B1 and sufC2B2 nor the deletion of both sufC1B1 and sufC2B2 affected the growth of M. acetivorans under any conditions tested, including diazotrophy. Interestingly, deletion of only sufC1B1 led to a delayed-growth phenotype under all growth conditions, suggesting that the deletion of sufC2B2 acts as a suppressor mutation in the absence of sufC1B1 . In addition, the deletion of sufC1B1 and/or sufC2B2 did not affect the total Fe–S cluster content in M. acetivorans cells. Overall, these results reveal that the minimal SUF system is not required for Fe–S cluster biogenesis in M. acetivorans and challenge the universal role of SufBC in Fe–S cluster biogenesis in methanogens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Abstract Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Local Bayesian Dirichlet mixing of imperfect models

Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tripogon loliiformis tolerates rapid desiccation after metabolic and transcriptional priming during initial drying

Abstract Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis , a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties.

59 BASIC BIOLOGICAL SCIENCES↗