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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Plutonium in Manhattan Project workers: Using autopsy data to evaluate organ content and dose estimates based on urine bioassay with implications for radiation epidemiology

Purpose Radiation dose estimates in epidemiology typically rely on intake predictions based on urine bioassay measurements. The purpose of this article is to compare the conventional dosimetric estimates for radiation epidemiology with the estimates based on additional post-mortem tissue radiochemical analysis results. Methods The comparison was performed on a unique group of 11 former Manhattan Project nuclear workers, who worked with plutonium in the 1940s, and voluntarily donated their bodies to the United States Transuranium and Uranium Registries. Results Post-mortem organ activities were predicted using different sets of urine data and compared to measured activities. Use of urinalysis data collected during the exposure periods overestimated the systemic (liver+skeleton) deposition of 239 Pu by 155±134%, while the average bias from using post-exposure urinalyses was –4±50%. Committed effective doses estimated using early urine data differed from the best estimate by, on average, 196±193%; inclusion of follow-up urine measurements in analyses decreased the mean bias to 0.6±36.3%. Cumulative absorbed doses for the liver, red marrow, bone surface, and brain were calculated for the actual commitment period. Conclusion On average, post-exposure urine bioassay results were in good agreement with post-mortem tissue analyses and were more reliable than results of urine bioassays collected during the exposure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Laser Spectroscopy of Exotic Atoms and Molecules Containing Octupole-Deformed Nuclei

This project investigated the nuclear, atomic, and molecular structure of exotic atoms and molecules containing actinide isotopes. These short-lived radioactive systems are challenging to produce and study in the laboratory, yet they offer unique opportunities for fundamental science. These nuclei are predicted to exhibit pear-shaped (octupole) deformation, a rare collective nuclear behavior that dramatically enhances their sensitivity to fundamental physics phenomena such as time-reversal- and parity-violating effects. Such enhancements make them ideal probes for exploring open questions in our understanding of the universe such as the origin of the matter–antimatter asymmetry of the universe. To realize these measurements, the project led the development of a new laser spectroscopy experiment at MIT, and later commissioned at the Facility for Rare Isotope Beams (FRIB) at Michigan State University: the Resonance Ionization Spectroscopy Experiment (RISE). RISE combines the spectroscopic precision of collinear laser spectroscopy with the sensitivity of particle-detection techniques, enabling measurements of rare isotopes produced at rates as low as a few ions per second. The beamline was designed, built, and installed, and was successfully commissioned at FRIB during the grant period. RISE is now a permanent capability of the FRIB facility, and has produced several results on the study of rare atoms and molecules for nuclear structure and fundamental symmetries. In parallel with the FRIB program, the project contributed to the first precision laser-spectroscopy measurements of short-lived radioactive molecules. Working with international collaborators at CERN's ISOLDE facility, the team conducted pioneering experiments on radium monofluoride (RaF) and actinium monofluoride (AcF). The results from this work have been published in major journals of science, including Nature, Science, Nature Physics, Nature Communications, and Physical Review Letters. These findings have guided future experiments on the laser cooling of radioactive molecules, opening a new platform for precision tests of fundamental symmetries.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development

Global expansion of hydropower resources has increased in recent years to meet growing energy demands and fill worldwide gaps in electricity supply. However, hydropower induces significant environmental impacts on river ecosystems - impacts that are addressed through environmental impact assessment (EIA) processes. The need for effective EIA processes is increasing as environmental regulations are either stressed in developing countries undertaking rapid expansion of hydropower capacity or time- and resource-intensive in developed countries. Part of the challenge in implementing EIAs lies in reaching a consensus among stakeholders regarding the most important environmental factors as the focus of impact studies. To help address this gap, we developed a weight-of-evidence approach (and toolkit) as a preliminary and coarse assessment of the most relevant impacts of hydropower on primary components of the river ecosystem, as identified using river function indicators. Through a science-based questionnaire and predictive model, users identify which environmental indicators may be impacted during hydropower development as well as those indicators that have the highest levels of uncertainty and require further investigation. Furthermore, an assessment tool visualizes inter-dependent indicator relationships, which help formulate hypotheses about causal relationships explored through environmental studies. We apply these tools to four existing hydropower projects and one hypothetical new hydropower project of varying sizes and environmental contexts. We observed consistencies between the output of our tools and the Federal Energy Regulatory Commission licensing process (inclusive of EIAs) but also important differences arising from holistic scientific evaluations (our toolkit) versus regulatory policies. The tools presented herein are aimed at increasing the efficiency of the EIA processes that engender environmental studies without loss of rigor or transparency of rationale necessary for understanding, considering, and mitigating the environmental consequences of hydropower.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Attribution of North American Subseasonal Precipitation Prediction Skill

The skill of NOAA’s official monthly U.S. precipitation forecasts (issued in the middle of the prior month) has historically been low, having shown modest skill over the southern United States, but little or no skill over large portions of the central United States. The goal of this study is to explain the seasonal and regional variations of the North American subseasonal (weeks 3–6) precipitation skill, specifically the reasons for its successes and its limitations. The performances of multiple recent-generation model reforecasts over 1999–2015 in predicting precipitation are compared to uninitialized simulation skill using the atmospheric component of the forecast systems. This parallel analysis permits attribution of precipitation skill to two distinct sources: one due to slowly evolving ocean surface boundary states and the other to faster time-scale initial atmospheric weather states. A strong regionality and seasonality in precipitation forecast performance is shown to be analogous to skill patterns dictated by boundary forcing constraints alone. The correspondence is found to be especially high for the North American pattern of the maximum monthly skill that is achieved in the reforecast. The boundary forcing of most importance originates from tropical Pacific SST influences, especially those related to El Niño–Southern Oscillation. Furthermore, we discuss physical constraints that may limit monthly precipitation skill and interpret the performance of existing models in the context of plausible upper limits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Microphysical Sensitivity of Superparameterized Precipitation Extremes in the Contiguous United States Due to Feedbacks on Large-Scale Circulation

Superparameterized (SP) global climate models have been shown to better simulate various features of precipitation relative to conventional models, including its diurnal cycle as well as its extremes. While various studies have focused on the effect of differing microphysics parameterizations on precipitation within limited-area cloud-resolving models, we examine here the effect on contiguous U.S. (CONUS) extremes in a global SP model. We vary the number of predicted moments for hydrometeor distributions, the character of the rimed ice species, and the representation of raindrop self-collection and breakup. Using a likelihood ratio test and accounting for the effects of multiple hypothesis testing, we find that there are some regional differences, particularly during spring and summer in the Southwest and the Midwest, in both the current climate and a warmer climate with uniformly increased sea surface temperatures. These differences are most statistically significant and widespread when the number of moments is changed. To determine whether these results are due to (fast) local effects of the different microphysics or the (slower) ensuing feedback on the large-scale atmospheric circulation, we run a series of short, 5-day simulations initialized from reanalysis data. We find that the differences largely disappear in these runs and therefore infer that the different parameterizations impact precipitation extremes indirectly via the large-scale circulation. Finally, we compare the present-day results with hourly rain gauge data and find that SP underestimates extremes relative to observations regardless of which microphysics scheme is used given a fixed model configuration and resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Discovering the nature of neutrinos: How nuclear theory will advance this grand endeavor. Final report

Why is there more matter than antimatter in the visible universe? Are neutrinos their own antiparticles? These are some of the most profound questions in science. The detection of a (0vββ) neutrinoless doublebeta decay and the measurement of its half-life is one of the most important experimental endeavors in science because it will answer these questions. Paramount to these measurements are accurate predictions with quantified uncertainties of the 0vββ-decay nuclear transitions, especially for the germanium-76 and xeon-136 nuclei, two candidate materials for building the next generation of DOE funded 0vββ-decay experiments. These predictions will guide the Office of Science’s down selection of experiments as well as aid in determining how much material is needed by these experiments. We studied a specific difficult to assess source of uncertainty that will impact coming microscopic predictions, uncertainty which arises from an incomplete yet systematically improvable model of interactions between nucleons and of nucleons with the other particles involved in decay process, referred to as the theory uncertainty. We employed a surrogate model of the germanium-76 nucleus, and its decay products, just detailed enough to adequately mimic the decaying nucleons need for a reasonable measure of this uncertainty and nothing more, avoiding the immense computational expense required in quantifying uncertainties of the full calculation. We found that microscopic calculations of this process can be expected to achieve relative theory uncertainties around 30%, including an unconstrained contribution that had the potential to spoil microscopic calculations. This results in a nearly order of magnitude on previously estimated uncertainties for this rare decay process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Wide range of possible trajectories of North Atlantic climate in a warming world

Decadal variability in the North Atlantic Ocean impacts regional and global climate, yet changes in internal decadal variability under anthropogenic radiative forcing remain largely unexplored. Here we use the Community Earth System Model 2 Large Ensemble under historical and the Shared Socioeconomic Pathway 3-7.0 future radiative forcing scenarios and show that the ensemble spread in northern North Atlantic sea surface temperature (SST) more than doubles during the mid-twenty-first century, highlighting an exceptionally wide range of possible climate states. Furthermore, there are strikingly distinct trajectories in these SSTs, arising from differences in the North Atlantic deep convection among ensemble members starting by 2030. We propose that these are stochastically triggered and subsequently amplified by positive feedbacks involving coupled ocean-atmosphere-sea ice interactions. Freshwater forcing associated with global warming seems necessary for activating these feedbacks, accentuating the impact of external forcing on internal variability. Further investigation on seven additional large ensembles affirms the robustness of our findings. By monitoring these mechanisms in real time and extending dynamical model predictions after positive feedbacks activate, we may achieve skillful long-lead North Atlantic decadal predictions that are effective for multiple decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predicting plant attractiveness to coccinellids with plant trait profiling, citizen science, and common garden surveys

Lady beetles are key predators in agricultural systems and their impact can be enhanced by plant resources. Facilitation of lady beetles is a potentially effective pest management strategy but a lack of data on which plant species attract and nurture them limits implementation. Here we investigate which plant species might optimize lady beetle impact by developing a profile of plant characteristics most likely to attract lady beetles and testing if the subset of “potentially preferred” plant families exhibiting those characteristics are significantly more attractive to lady beetles than other families. This testing encompassed a combination of observations submitted to a citizen science program, the Lost Ladybug Project, and two repeated site surveys. Traits reported to be most attractive to lady beetles included the presence of trichomes that provide protection from predation, nutritional resources such as nectar or prey, and visual or chemical cues associated with those traits. Significantly more lady beetles were observed on the three plant families which most often exhibit these traits, Apiaceae, Asteraceae, and Rosaceae, than on other families. Furthermore, strategies for incorporating plants from these families into production systems to attract lady beetles and limit pest damage are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Implementing a neural network interatomic model with performance portability for emerging exascale architectures

The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. Furthermore, we also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to ~50% and ~5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Magnetic character of the low-energy enhancement in 70 Zn

The transition of an atomic nucleus from an excited state to a state of lower energy is typically accompanied by the emission of photons. The likelihood of photon emission is a function of the photon energy and of properties of the initial and final nuclear states and is known as the photon (or γ-ray) strength function (γSF). Of the features present in the γSF, an enhancement in the low-energy region has been observed in some nuclei, but the electromagnetic nature of this enhancement remains an open question. This low-energy enhancement in the γSF significantly impacts our understanding of how elements are created in stars and the structure of the nucleus. Here we present the results from an experiment on the γSF of 70 Zn that show that the enhancement in the γSF of this nucleus is of magnetic nature. This observation helps address a long-open question about γSFs in atomic nuclei and directly impacts our predictive capabilities in nuclear science.

Physics - Nuclear physics and radiation physics↗
DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗
DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

BraggNN : fast X-ray Bragg peak analysis using deep learning

X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

36 MATERIALS SCIENCE↗
DOE OSTI2021

First-principles design of a single-atom–alloy propane dehydrogenation catalyst

The complexity of heterogeneous catalysts means that a priori design of new catalytic materials is difficult, but the well-defined nature of single-atom–alloy catalysts has made it feasible to perform unambiguous theoretical modeling and precise surface science experiments. Herein we report the theory-led discovery of a rhodium-copper (RhCu) single-atom–alloy catalyst for propane dehydrogenation to propene. Although Rh is not generally considered for alkane dehydrogenation, first-principles calculations revealed that Rh atoms disperse in Cu and exhibit low carbon-hydrogen bond activation barriers. Surface science experiments confirmed these predictions, and together these results informed the design of a highly active, selective, and coke-resistant RhCu nanoparticle catalyst that enables low-temperature nonoxidative propane dehydrogenation.

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

CORN (Crop Optimization Realized through Neuralnets)

Traditional models of predicting plant traits are limited because they often rely on linear assumptions that do not fully capture the complexity of biological interactions and DNA-based markers which are static across environments. This project generated a set of RNA-based data from large multi-environment field trials and combined it with advanced machine learning techniques to account for these complex interactions and improve the accuracy of predictions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Human Lung Fibroblast Response to HCoV-229E Infection, Top-down Proteomics of Histones (ACS-TZ-DP7)

The purpose of this experiment was to evaluate the human host cellular response to wild type human coronavirus strain 229E (HCoV-229E) infection, specifically how histones are modified following infection. Sample data was obtained from mock-infected and HCoV-229E-infected immortalized human lung fibroblasts (MRC-5) (MOI 3). Whole cell lysates were collected at 24 hours post infection and histones from all samples were enriched and were processed for proteoform identification analysis.

59 BASIC BIOLOGICAL SCIENCES↗