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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Microbiological, Physicochemical, and Immunological Analysis of a Commercial Cashew Nut-Based Yogurt

Nut-based milks and yogurts are gaining popularity, but may not offer the same benefits as dairy yogurts to consumers. Cashew nuts often cause severe allergic reactions, and cashew nut allergens are stable to several types of processing. To compare its characteristics to dairy yogurt and characterize the effects of fermentation on the Ana o 1–3 cashew nut allergens, a commercial yogurt made from cashew nuts (Cashewgurt) was evaluated for microbiological, physiochemical, and immunological properties. Average counts for lactobacilli and Streptococcus thermophilus were greater than 10 million colony forming units per milliliter, indicating the capacity to provide a health benefit. Cashewgurt pH and viscosity values were comparable to cow milk yogurts, and it was off white in color. SDS-PAGE analysis indicated a clear reduction in Ana o 1 and 2, and immuno-assay with polyclonal anti-cashew IgG antibody and cashew-allergic IgE indicated an overall reduction in allergen content. In contrast, SDS-PAGE, mass spectrometry, immunoblot, and ELISA all revealed that Ana o 3 was relatively unaffected by the fermentation process. In conclusion, Ana o 1 and Ana o 2 are sensitive to degradation, while Ana o 3 survives lactic acid bacterial fermentation during yogurt production. The analysis presented here indicates that cashew nut yogurt is not suitable for those with cashew nut allergy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Microbiomes of commercially-available pine nuts and sesame seeds

Metagenomic analysis of food is becoming more routine and can provide important information pertaining to the shelf life potential and the safety of these products. However, less information is available on the microbiomes associated with low water activity foods. Pine nuts and sesame seeds, and food products which contain these ingredients, have been associated with recalls due to contamination with bacterial foodborne pathogens. The objective of this study was to identify the microbial community of pine nuts and sesame seeds using targeted 16S rRNA sequencing technology. Ten different brands of each seed type were assessed, and core microbiomes were determined. A total of 21 and 16 unique taxa with proportional abundances >1% in at least one brand were identified in the pine nuts and sesame seeds, respectively. Members of the core pine nut microbiome included the genera Alishewanella, Aminivibrio, Mycoplasma, Streptococcus, and unassigned OTUs in the families of Desulfobacteraceae and Xanthomonadaceae. For sesame seeds, the core microbiome included Aminivibrio, Chryseolina, Okibacterium, and unassigned OTUs in the family Flavobacteriaceae. The microbiomes of these seeds revealed that these products are dominated by environmental bacterial genera commonly isolated from soil, water, and plants; bacterial genera containing species known as commensal organisms were also identified. Understanding these microbiomes can aid in the risk assessment of these products by identifying food spoilage potential and community members which may co-enrich with foodborne bacterial pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Complement to thermodynamics of dyonic Taub-NUT-AdS spacetime

We examine the thermodynamics of Euclidean dyonic Taub-Nut/Bolt-AdS 4 black holes for a variety of horizon geometries to understand how gauge field regularity conditions influence the thermodynamic relations. We find several distinct features that distinguish the NUT-charged case from its dyonic Reissner-Nordstrom counterpart. For the Nut solution, the gauge field vanishes at the horizon and so regularity is ensured. For the Bolt solution we find that the norm of the gauge field is required to vanish at the horizon in order to satisfy both regularity and the first law of thermodynamics. This regularity condition yields a constraint on the electric and magnetic charges and so reduces cohomogeneity of the system; for spherical horizons, the regularity condition removing the Misner string singularity further reduces cohomogeneity. We observe that bolt solutions with increasing electric charge have positive heat capacity, but upon turning on the magnetic charge to make the solution dyonic, we find that the properties of the uncharged one are retained, having both positive and negative heat capacity. We also study the extremal Bolt solution, finding that Misner string disappears at the horizon in the zero temperature limit. We find that the extremal solution has finite-temperature-like behaviour, with the electric potential playing a role similar to temperature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Aligned fields double copy to Kerr-NUT-(A)dS

We find Abelian gauge fields that double copy to a large class of black hole spacetimes with spherical horizon topology known as the Kerr-NUT-(A)dS family. Using a multi-Kerr-Schild prescription, we extend the previously-known double copy structure for arbitrarily rotating general dimension black holes, to include NUT charges and an arbitrary cosmological constant. In all cases, these single copy gauge fields are ‘aligned fields’, because their nonzero components align with the principal tensor which generates the Killing structure of the spacetime. In five dimensions, we additionally derive the same single-copy field strengths via the Weyl double copy procedure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficacy of long-lasting insecticide-incorporated nets on 2 scolytinae pests, the coffee berry borer Hypothenemus hampei and tropical nut borer Hypothenemus obscurus under laboratory conditions

Abstract Several pests affect coffee (Coffea spp., Rubiaceae) and macadamia, Macadamia integrifolia Maiden & Betche (Proteaceae) in Hawaii. The coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), is the most damaging to coffee, while the tropical nut borer, Hypothenemus obscurus (Fabricius) (Coleoptera: Curculionidae: Scolytinae), is one of the worst pests of macadamia. This paper investigates the potential efficacy of a long-lasting insecticide-incorporated net (LLIN) under laboratory conditions to manage these pests. The LLIN (40 denier with mesh size 625 knots/in²), incorporated with α-cypermethrin (0.34%), was excised into 100 mm circles and inserted in 100 mm Petri dishes. Nets with the same quality but without insecticides were used as control treatments. Twenty beetles (H. obscurus or H. hampei) each were placed on the treated and non-treated netting at 4 treatment or exposure hours—1, 6, 12, and 24—with 5 replicates. Subsequently, the beetles were ranked alive, affected, or dead. The results showed that the LLIN with α-cypermethrin had significant lethal and sub-lethal effects on both Hypothenemus species, causing over 90% mortality after 24 h of exposure and paralysis after 1, 6, and 12 h of exposure. The highest lethality value was recorded after 24 h of exposure for both H. obscurus and H. hampei. The LT50 of H. obscurus and H. hampei was 18.78 min and 2.15 h, respectively, while the LT90 values were 32.11 and 20.67 h. These results imply the potential effectiveness of LLINs with α-cypermethrin for management of H. obscurus and H. hampei, but field studies are warranted for optimization.

Entomology↗
DOE OSTI2025

A nut-and-bolt assembly of the bimodular large progenitor botulinum neurotoxin complex

Botulinum neurotoxin serotype A (BoNT/A) is naturally produced by bacteria along with four nontoxic neurotoxin-associated proteins (NTNH, HA70, HA33, and HA17), forming a bimodular large progenitor toxin complex (L-PTC). The BoNT/A–NTNH complex protects the toxin from adverse environment, while the complex consisting of HA proteins facilitates toxin absorption during oral intoxication. How these two independent modules assemble into the L-PTC remains unclear. Here, we report the crystal structure of the BoNT/A–NTNH–HA70 complex at ~2.9-Å resolution. The structure reveals that the BoNT/A–NTNH complex is anchored into a concentric double β-barrel channel of trimeric HA70 through a short β-hairpin of NTNH (termed nLoop), resembling a nut-and-bolt attachment. We find that the nLoop of NTNH is strictly conserved across HA-containing BoNT complexes and that NTNH–HA70 binding is interchangeable among them. Furthermore, we demonstrate that the nLoop functions as a minimal motif enabling attachment of a protein-of-interest to the HA complex, with potential applications in oral biologics delivery.

Science & Technology - Other Topics↗
DOE OSTI2023

Chapter 4: "Waste"-to-Energy for Decarbonization - Transforming Nut Shells Into Carbon-Negative Electricity

This chapter presents a study demonstrating waste pistachio nut shells as a renewable feedstock for climate-friendly electricity generation via industrial gasification technology. The study includes biomass feedstock characterization (i.e., pistachio waste critical material attributes), process variability (i.e., bulk material handling), and overall operational reliability and conversion performance through extended testing. Additionally, techno-economic analysis (TEA) and life cycle assessment (LCA) were performed to assess the economic feasibility and environmental impact of the technology to transform agricultural waste to biopower. For processing pistachio waste material, among critical material attributes, fines content in the biomass (<1/4") had the largest potential to reduce the operating time of the gasifiers due to plugging. Pelletizing fines and co-feeding them with the mixed pistachio waste increased the average feed density, feed rate, and biochar production. Compared to pine wood chips, mixed pistachio waste yielded higher biochar quantity but slightly reduced quality. In general, a systematic Quality by Design methodology is the preferred approach for designing preprocessing and material conveyance systems, where a downstream technology (end user) for the produced intermediate is specified at the outset. TEA results show that the biochar production rate and selling price had an overwhelming impact on the modeled Minimum Electricity Selling Price (MESP), which ranged from 35.5 to 39.9 cents/kWh for the cases studied (16 h/day operational basis). Moreover, LCA results show that the valorization of pistachio shells for biopower generation is a "carbon negative" process that can help decarbonize the U.S. electricity grid. The specific carbon intensity was -0.29 to -0.71 kg CO2e/kWh, compared to 0.45 kg CO2e/kWh for the average U.S. electricity mix. Biochar production from pistachio waste as a potential means for carbon sequestration was a significant driver for the LCA. The highly stable biochar permanently sequesters a considerable fraction of biochar carbon in the ground, more than enough to offset the life cycle emissions, and can be a complementary climate change mitigation strategy.

bio-char↗
DOE OSTI2023

Proteomic Analysis of Pecan ( Carya illinoinensis ) Nut Development

Pecan (Carya illinoinensis) nuts are an economically valuable crop native to the United States and Mexico. A proteomic summary from two pecan cultivars at multiple time points was used to compare protein accumulation during pecan kernel development. Patterns of soluble protein accumulation were elucidated using qualitative gel-free and label-free mass-spectrometric proteomic analyses and quantitative (label-free) 2-D gel electrophoresis. Two-dimensional (2-D) gel electrophoresis distinguished a total of 1267 protein spots and shotgun proteomics identified 556 proteins. Rapid overall protein accumulation occurred in mid-September during the transition to the dough stage as the cotyledons enlarge within the kernel. Pecan allergens Car i 1 and Car i 2 were first observed to accumulate during the dough stage in late September. While overall protein accumulation increased, the presence of histones diminished during development. Twelve protein spots accumulated differentially based on 2-D gel analysis in the weeklong interval between the dough stage and the transition into a mature kernel, while eleven protein spots were differentially accumulated between the two cultivars. These results provide a foundation for more focused proteomic analyses of pecans that may be used in the future to identify proteins that are important for desirable traits, such as reduced allergen content, improved polyphenol or lipid content, increased tolerance to salinity, biotic stress, seed hardiness, and seed viability.

2D-gel↗
DOE OSTI2025

Single Kerr-Schild metric for Taub-NUT instanton

It is shown that a complex coordinate transformation maps the Taub-Newman-Unti-Tamburino instanton metric to a Kerr-Schild metric. This metric involves a semi-infinite line defect as the gravitational analog of the Dirac string, much like the original metric. Moreover, it facilitates three versions of classical double copy correspondence with the self-dual dyon in electromagnetism, one of which involves a nonlocal operator. The relevance to the Newman-Janis algorithm is briefly noted. Published by the American Physical Society 2025

Kim, Joon-Hwi (ORCID:000000025474123X)↗
DOE OSTI2024

MPI nuts and bolts and more [Slides]

MPI (Message-Passing Interface) is a message-passing library interface specification. All parts of this definition are significant. MPI addresses primarily the message-passing parallel programming model, in which data is moved from the address space of one process to that of another process through cooperative operations on each process. . . MPI is a specification, not an implementation; there are multiple implementations of MPI. This specification is for a library interface; MPI is not a language, and all MPI operations are expressed as functions, subroutines, or methods, according to the appropriate language bindings that, for C and Fortran, are part of the MPI standard. MPI Forum is the organization which is responsible for the MPI Specification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Resilient Solar PV Structures a.k.a. the "Nuts and Bolts" of PV Resilience

Solar photovoltaic (PV) systems can and has survived many extreme weather events. On occasion, however, solar PV systems have been damaged. This damage is related more to the features of individual PV systems rather than the magnitude of the weather events. Much of this damage can be avoided with relatively simple, cost-effective measures that include design, installation, inspection, and O&M best practices.

14 SOLAR ENERGY↗
DOE OSTI2023

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. In conclusion, compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Bayesian Inference with Latent Hamiltonian Neural Networks (L-HNNs)

When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and the No-U-Turn Sampler (NUTS). However, HMC and NUTS can require numerous numerical gradients of the target density and can prove slow in practice. We propose Hamiltonian neural networks (HNNs) with HMC and NUTS for solving Bayesian inference problems [1, 2]. Once trained, HNNs do not require gradients of the target density while sampling. Moreover, they satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well suited for use within HMC and NUTS because stationarity can be shown. We also propose an HNN extension called latent HNNs (L-HNNs), which predict latent variable outputs. Compared to HNNs, L-HNNs offer improved expressivity and a reduction in integration errors. Finally, we propose employing L-HNNs in NUTS with an online error monitoring scheme to prevent degeneracy of the sampling in regions of low probability density. We demonstrate L-HNNs in NUTS with online error monitoring by using several example cases involving complex, heavy-tailed, and high local curvature probability densities. Overall, L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring improved the effective sample size (ESS) per gradient by an order of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference

Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in practice. Recently, there has been a significant interest in physics-based machine learning of dynamical (or Hamiltonian) systems and Hamiltonian neural networks (HNNs) is a noteworthy architecture. But these types of architectures have not been applied to solve Bayesian inference problems efficiently. We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients. We introduce latent variable outputs to HNNs (L-HNNs) for improved expressivity and reduced integration errors. We integrate L-HNNs in NUTS and further propose an online error monitoring scheme to prevent sampling degeneracy in regions where L-HNNs may have little training data. We demonstrate L-HNNs in NUTS with online error monitoring consider several complex high-dimensional posterior densities and compare its performance to NUTS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Black holes in Klein space

The analytic continuation of the general signature (1, 3) Lorentzian Kerr-Taub-NUT black holes to signature (2, 2) Kleinian black holes is studied. Their global structure is characterized by a toric Penrose diagram resembling their Lorentzian counterparts. Kleinian black holes are found to be self-dual when their mass and NUT charge are equal for any value of the Kerr rotation parameter a. Remarkably, it is shown that the rotation a can be eliminated by a large diffeomorphism; this result also holds in Euclidean signature. The continuation from Lorentzian to Kleinian signature is naturally induced by the analytic continuation of the S-matrix. Indeed, we show that the geometry of linearized black holes, including Kerr-Taub-NUT, is captured by (2, 2) three-point scattering amplitudes of a graviton and a massive spinning particle. This stands in sharp contrast to their Lorentzian counterparts for which the latter vanishes kinematically and enables a direct link to the S-matrix.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗