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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Divergent selection and climate adaptation fuel genomic differentiation between sister species of Sphagnum (peat moss)

Abstract Background and Aims New plant species can evolve through the reinforcement of reproductive isolation via local adaptation along habitat gradients. Peat mosses (Sphagnaceae) are an emerging model system for the study of evolutionary genomics and have well-documented niche differentiation among species. Recent molecular studies have demonstrated that the globally distributed species Sphagnum magellanicum is a complex of morphologically cryptic lineages that are phylogenetically and ecologically distinct. Here, we describe the architecture of genomic differentiation between two sister species in this complex known from eastern North America: the northern S. diabolicum and the largely southern S. magniae. Methods We sampled plant populations from across a latitudinal gradient in eastern North America and performed whole genome and restriction-site associated DNA sequencing. These sequencing data were then analyzed computationally. Key Results Using sliding-window population genetic analyses we find that differentiation is concentrated within ‘islands’ of the genome spanning up to 400 kb that are characterized by elevated genetic divergence, suppressed recombination, reduced nucleotide diversity and increased rates of non-synonymous substitution. Sequence variants that are significantly associated with genetic structure and bioclimatic variables occur within genes that have functional enrichment for biological processes including abiotic stress response, photoperiodism and hormone-mediated signalling. Demographic modelling demonstrates that these two species diverged no more than 225 000 generations ago with secondary contact occurring where their ranges overlap. Conclusions We suggest that this heterogeneity of genomic differentiation is a result of linked selection and reflects the role of local adaptation to contrasting climatic zones in driving speciation. This research provides insight into the process of speciation in a group of ecologically important plants and strengthens our predictive understanding of how plant populations will respond as Earth’s climate rapidly changes.

58 GEOSCIENCES↗
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
DOE OSTI2021

A capillary-based microfluidic device enables primary high-throughput room-temperature crystallographic screening

A novel capillary-based microfluidic strategy to accelerate the process of small-molecule-compound screening by room-temperature X-ray crystallography using protein crystals is reported. The ultra-thin microfluidic devices are composed of a UV-curable polymer, patterned by cleanroom photolithography, and have nine capillary channels per chip. The chip was designed for ease of sample manipulation, sample stability and minimal X-ray background. 3D-printed frames and cassettes conforming to SBS standards are used to house the capillary chips, providing additional mechanical stability and compatibility with automated liquid- and sample-handling robotics. These devices enable an innovative in situ crystal-soaking screening workflow, akin to high-throughput compound screening, such that quantitative electron density maps sufficient to determine weak binding events are efficiently obtained. This work paves the way for adopting a room-temperature microfluidics-based sample delivery method at synchrotron sources to facilitate high-throughput protein-crystallography-based screening of compounds at high concentration with the aim of discovering novel binding events in an automated manner.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Iterative Bragg peak removal on X-ray absorption spectra with automatic intensity correction

This study introduces a novel iterative Bragg peak removal with automatic intensity correction (IBR-AIC) methodology for X-ray absorption spectroscopy (XAS), specifically addressing the challenge of Bragg peak interference in the analysis of crystalline materials. The approach integrates experimental adjustments and sophisticated post-processing, including an iterative algorithm for robust calculation of the scaling factor of the absorption coefficients and efficient elimination of the Bragg peaks, a common obstacle in accurately interpreting XAS data, particularly in crystalline samples. The method was thoroughly evaluated on dilute catalysts and thin films, with fluorescence mode and large-angle rotation. The results underscore the technique's effectiveness, adaptability and substantial potential in improving the precision of XAS data analysis. While demonstrating significant promise, the method does have limitations related to signal-to-noise ratio sensitivity and the necessity for meticulous angle selection during experimentation. Overall, IBR-AIC represents a significant advancement in XAS, offering a pragmatic solution to Bragg peak contamination challenges, thereby expanding the applications of XAS in understanding complex materials under diverse experimental conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Crystal structure solution and high-temperature thermal expansion in NaZr 2 (PO 4 ) 3 -type materials

The NaZr 2 P 3 O 12 family of materials have shown low and tailorable thermal expansion properties. In this study, SrZr 4 P 6 O 24 (SrO·4ZrO 2 ·3P 2 O 5 ), CaZr 4 P 6 O 24 (CaO·4ZrO 2 ·3P 2 O 5 ), MgZr 4 P 6 O 24 (MgO·4ZrO 2 ·3P 2 O 5 ), NaTi 2 P 3 O 12 [½(Na 2 O·4TiO 2 ·3P 2 O 5 )], NaZr 2 P 3 O 12 [½(Na 2 O·4ZrO 2 ·3P 2 O 5 )], and related solid solutions were synthesized using the organic–inorganic steric entrapment method. The samples were characterized by in-situ high-temperature X-ray diffraction from 25 to 1500°C at the Advanced Photon Source and National Synchrotron Light Source II. The average linear thermal expansion of SrZr 4 P 6 O 24 and CaZr 4 P 6 O 24 was between −1 × 10 −6 per °C and 6 × 10 −6 per °C from 25 to 1500°C. The crystal structures of the high-temperature polymorphs of CaZr 4 P 6 O 24 and SrZr 4 P 6 O 24 with R 3 c symmetry were solved by Fourier difference mapping and Rietveld refinement. This polymorph is present above ∼1250°C. This work measured thermal expansion coefficients to 1500°C for all samples and investigated the differences in thermal expansion mechanisms between polymorphs and between compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bi-fidelity Gradient-Based Approach for Nonlinear Well Logging Inverse Problems

Solving a non-linear inverse problem is challenging in computational science and engineering. Sampling based methods require a large number of model valuations; gradientbased methods require fewer model evaluations but only find the local minima. Multifidelity optimization combines the low fidelity model and the high-fidelity model to achieve both high accuracy and high efficiency. In this paper, we present a bi-fidelity approach to solve non-linear inverse problems. In the bi-fidelity inversion method, the low-fidelity model is used to acquire a good initial guess, and the high-fidelity model is used to locate the global minimum. Combined with a multi-start optimization scheme, the proposed approach significantly increases the possibility of finding the global minimum for nonlinear inverse problems with many local minima. The method is tested with two toy problems and then applied to an electromagnetic well logging inverse problem, which is difficult to solve using traditional gradient-based methods. The bi-fidelity method provides promising inversion results and can be easily applied to traditional gradient-based methods.

42 ENGINEERING↗
DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Allocation and Operation of A Hydropneumatic Energy Storage with Building Microgrid

Behind-meter application for a recently invented hydropneumatic Ground-Level Integrated Diverse Energy Storage (GLIDES) is focused in this research. Considering possible uncertainties from solar radiation and electricity load in a building microgrid, two-stage stochastic programming is adopted for 15-min operation and a mixed-integer nonlinear stochastic model is built with integrated energy systems of GLIDES, solar panel and power generating unit. In order to make the model computationally tractable, we use sample average approximate method to decrease associated complexity with sample size and improve solving efficiency. The experimental results in deterministic and stochastic operation indicate that best candidate solution from SAA are superior to the solution from expected value problem in terms of optimality gap and variance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Establishing reference ranges for circulating biomarkers of drug‐induced liver injury in healthy human volunteers 1

Aims The potential of mechanistic biomarkers to improve prediction of drug‐induced liver injury (DILI) and hepatic regeneration is widely acknowledged. We sought to determine reference intervals for new biomarkers of DILI and regeneration, as well as to characterize their natural variability and impact of diurnal variation. Methods Serum samples from 227 healthy volunteers were recruited as part of a cross‐sectional study; of these, 25 subjects had weekly serial sampling over 3 weeks, while 23 had intensive blood sampling over a 24h period. Alanine aminotransferase (ALT), MicroRNA‐122 (miR‐122), High Mobility Group Box‐1 (HMGB1), total Keratin‐18 (K18), caspase‐cleaved Keratin‐18 (ccK18), Glutamate Dehydrogenase (GLDH) and Macrophage Colony‐Stimulating Factor‐1 (CSF‐1) were assayed. Results Reference intervals were established for each biomarker based on the 97.5% quantile (90% CI) following the assessment of fixed effects in univariate and multivariable models. Intra‐individual variability was found to be non‐significant, and there was no significant impact of diurnal variation. Conclusion Reference intervals for novel DILI biomarkers have been described. An upper limit of a reference range might represent the most appropriate mechanism to utilize these data. These data can now be used to interpret data from exploratory clinical DILI studies and to assist their further qualification as required by regulatory authorities.

Jorgensen, Andrea L. [Department of Health Data Sc↗
DOE OSTI2020

The ionic conductivity of Sm‐doped ceria

Abstract The oxygen ion conductivity of polycrystalline samples of Sm‐doped ceria and of Gd‐doped ceria is studied as a function of doping fraction and temperature using impedance spectroscopy allowing the separation of bulk and grain boundary conductivity. The introduction of a fine spacing for the Sm dopant fraction allows the clear identification of the dopant fraction leading to the largest bulk conductivity. At 267°C, the largest bulk conductivity is shown for Ce 0.93 Sm 0.07 O 1.965 . With increasing temperature, indications of an increase in the dopant fraction, which leads to the maximum in conductivity, are found. For the grain boundary conductivity, the maximum appears at larger dopant fractions compared to the bulk conductivity. The largest total conductivity for both dopants is again found for Sm‐doped ceria. In literature, different syntheses and sample preparation methods led to larger total conductivities for Gd‐doped ceria. In this work, we demonstrate that the variation of sintering conditions leads to scattering in the conductivity over one order of magnitude. Finally, we demonstrate that, in nominally pure cerium oxide, impurities dominate the ionic conductivity.

Koettgen, Julius↗
DOE OSTI2025

Sorted-cell proteomics reveals an AT1-associated epithelial cornification phenotype and suggests endothelial redox imbalance in human bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is a neonatal lung disease characterized by inflammation and scarring leading to long-term tissue damage. Previous whole tissue proteomics identified BPD-specific proteome changes and cell type shifts. Little is known about the proteome-level changes within specific cell populations in disease. Here, we sorted epithelial (EPI) and endothelial (ENDO) cell populations based on their differential surface markers from normal and BPD human lungs. Using a low-input compatible sample preparation method (MicroPOT), proteins were extracted and digested into peptides and subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteome analysis. Of the 4,970 proteins detected, 293 were modulated in abundance or detection in the EPI population and 422 were modulated in ENDO cells. Modulation of proteins associated with actin-cytoskeletal function, such as SCEL, LMO7, and TBA1B was observed in the BPD EPIs. Using confocal imaging and analysis, we validated the presence of aberrant multilayer-like structures comprising SCEL and LMO7, known to be associated with epidermal cornification, in the human BPD lung. This is the first report of the accumulation of cornification-associated proteins in BPD. Their localization in the alveolar parenchyma, primarily associated with alveolar type 1 (AT1) cells, suggests a role in the BPD postinjury response. In the ENDOs, redox balance and mitochondrial function pathways were modulated. Alternative mRNA splicing and cell proliferative functions were elevated in both populations, suggesting potential dysregulation of cell progenitor fate. This study characterized the proteome of epithelial and endothelial cells from the BPD lung for the first time, identifying population-specific changes in BPD pathogenesis.

BPD↗
DOE OSTI2024

Experimental investigation of heat treatments on the mechanical performance of grade 300 maraging steel strut-based lattices

Lightweight and strong components are essential for reducing energy consumption and enhancing efficiency. Lattice structures are one such geometry utilized to achieve weight reduction. This study investigates the mechanical properties of various lattice structures fabricated from Maraging Steel (EOS MS1) using the Direct Metal Laser Sintering (DMLS) method. The samples include three distinct cellular geometries: body-centered cubic (BCC), face-centered cubic (FCC), and octet truss configurations, which are subjected to tensile and compressive tests. The primary goal of this research is to evaluate the impact of heat treatment on the mechanical properties of cellular architecture under tensile and compressive loading conditions. Destructive, nondestructive testing, and simulation results were also obtained from different heat treatment processes. It was found that the age-hardened specimens performed the best overall in terms of ultimate tensile/compressive strength and elongation. The top-performing topologies in compression and tension were found to be the octet structure, as they were able to withstand the most loading and straining when compared to the other specimens.

Engineering↗
DOE OSTI2022

Estimating epistemic uncertainty in soil parameters for nonlinear site response analyses: Introducing the Latin Hypercube Sampling technique

This study quantifies the effects of epistemic uncertainty in soil parameters on nonlinear (NL) site response analysis (SRA) results, validated against the data recorded at a well-instrumented geotechnical downhole array located in Japan. To this end, a one-dimensional soil column model of the Service Hall Array (SHA) near the Kashiwazaki-Kariwa Nuclear Power Plant (KKNPP) is developed using the finite element (FE) program LS-DYNA. The dynamic stress–strain relationship is characterized by a modified two-stage hyperbolic (MTH) NL backbone curve formulation capable of capturing soil behavior at both small- and large-shear strains. The model is then validated against the ground motion recordings to capture the model bias. The uncertainties associated with the shear-wave velocity profile (a small-strain soil property) and soil shear strength (a large-strain soil property) are incorporated in NL SRA to quantify their separate and joint randomization effects on the results. This study proposes using the Latin Hypercube Sampling (LHS) method as an efficient alternative to commonly used methods, such as Standard Monte Carlo (SMC), to account for uncertainty propagation in such reliability analysis. Both low-intensity and design-level records from the recordings at the SHA are applied to study the contribution of the small- and large-strain NL dynamic soil properties. Results from 46,200 NL FE analyses (23,100 per input motion) are presented. Measured and predicted site response, using recorded ground motions at this downhole array, is compared to assess the significance of soil parameter uncertainty on the observed ground motion dispersions. It is demonstrated that increasing the number of soft realizations and implementing higher level earthquake intensity lead to higher ground motion dispersion. Unlike past studies in randomization of Vs profiles with the SMC method, the LHS method is shown to have no significant effect on the predicted median surface response spectra and amplification factors (AFs) for this case study.

Engineering↗
DOE OSTI2024

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular “fingerprints”), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2020

Benchmark Solutions for Radiation Transport in Stochastic Media with Inhomogeneous Material Statistics

Accurately solving implicit Monte Carlo (IMC) thermal photon transport problems with mixed material cells is important in realistic applications. The production IMC package at LLNL treats mixed material cells arising from ALE remap and hydrodynamics using the same approximate model. The new Imp IMC thermal photon transport package currently under development has both a material interface reconstruction (MIR) algorithm and a Levermore-Pomraning (LP) stochastic medium algorithm for treating mixed material cells. Existing stochastic medium algorithms for treating mixed material cells in IMC lack a complete theoretical basis. The IMC LP algorithm implementation has been demonstrated to reproduce published deterministic LP solutions for the particular case of spatially homogeneous material statistics. Realistic simulations will include spatially inhomogeneous material statistics (material mean chord lengths). In a previous investigation, the LP-model for transport in binary stochastic media in rod geometry was generalized to accommodate spatially varying material chord lengths, i.e., the mixing statistics were allowed to be nonhomogeneous. Analytical solutions were obtained and used to produce a verifi cation suite for the Imp IMC Levermore-Pomraning implementation for different spatial variations of the chord lengths. However, the accuracy of the LP model when the mixing statistics are nonhomogeneous has not been assessed and leaves open the question of whether local accuracy is improved or further degraded when chord lengths are not uniform. This shortcoming is rectifi ed here by developing benchmark analytic solutions for transport in binary Markovian stochastic mixtures in rod geometry with nonhomogeneous mixing statistics, using spatially varying chord lengths considered in the previous investigation based on the LP model. Methods for sampling a nonhomogeneous Poisson process (NHPP) are first described and used to construct individual realizations of the binary mixtures in rod geometry. Analytic solutions are then obtained for the forward and backward directed fluxes on a given realization, now viewed as a deterministic medium with alternating layers of the two materials with known interface locations. Finally, material averaged scalar fluxes are obtained using these sampling schemes with spatially linear and quadratic chord lengths and used to assess the accuracy of the previously obtained LP-model results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗