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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Diffusion characteristics of water molecules in a lamellar structure formed by triblock copolymers

The distribution and diffusion of water molecules are playing important roles in determining self-assembly and transport properties of polymeric systems. Small-angle neutron scattering (SANS) experiments and molecular dynamics (MD) simulation have been applied to understand the distribution of water molecules and their dynamics in the lamellar membrane formed by Pluronic L62 block copolymers. Penetration of water molecules into the polyethylene oxide (PEO) layers of the membranes has been estimated using scattering length density (SLD) profiles obtained from SANS measurements, which agree well with the molecular distribution observed from MD simulations. The water diffusion coefficient at different regions of the lamellar membrane was further investigated using MD simulation. The diffusion characteristic shows a transition from normal to anomalous diffusion as the position of the water molecule changes from the bulk to PEO and to the polypropylene oxide (PPO) layer. Here we find that water molecules within the PEO or PPO layers follow subdiffusive dynamics, which can be interpreted by the model of fractional Brownian motion.

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

Holistic Numerical Calibration of the iMESA Electrostatic Analyzer

A drifted Maxwellian velocity distribution is the most common model used to interpret the data from low-energy charged-particle instruments onboard spacecraft that are used to investigate the ambient plasma environment in the low Earth orbit (LEO). An original method is presented for determining the flow parameters (density, temperature, and flow energy) of such a distribution from the output of the integrated miniaturized electrostatic analyzer, which has been successfully flown on several LEO missions. Rather than attempting to deconvolve from the on-orbit data the analyzer’s response to an ideal, monoenergetic input, numerical simulation is used to predict and parameterize the response of the device to an input distribution that includes an isotropic, non-zero temperature, yielding a straightforward method for extracting the flow parameters from the spacecraft data. The method is computationally simple enough to be incorporated into a robust algorithm suitable for rapid batch processing or real-time analysis of data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Measurement of Thomson-scattering spectra with continuous angular resolution (invited)

Here, a novel Thomson-scattering diagnostic with continuous angular resolution over a span of 120° was developed for the characterization of plasmas produced at the Omega Laser Facility. Spectrally resolving light scattered from electron plasma wave features as a function of emission angle provides a means to efficiently probe a large range of plasma frequencies and k vectors. Together, these spectra contain critical constraints on the plasma-physics models used to interpret the data and enable experimental measurements of the electron-velocity distribution function over several orders of magnitude without assumptions about its mathematical form. Major components of the instrument include (1) a reflective collection objective that gathers light over a range of 120° × 12°; (2) a spatial-filter image relay for measurement localization; (3) cylindrical optics for producing a line image of the collection aperture; (4) a transmission grating spectrometer; and (5) a time-gated, image-intensified camera. Thomson-scattered light collected from an ~(50 – μM) 3 volume of plasma is recorded with 0.8-nm spectral and 1° angular resolution. Initial experiments examined the properties of the electron-velocity distribution in gas-jet-produced plasmas in the presence of heating via inverse bremsstrahlung absorption.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Mapping the frontiers of the nuclear mass surface

Nuclear masses play a central role in nuclear astrophysics, significantly impacting the origin of the elements and observables used to constrain ultradense matter. A variety of techniques are available to meet this need, varying in their emphasis on precision and reach from stability. Here I briefly summarize the status of and near-future for the time-of-flight magnetic-rigidity (TOF-Bρ) mass measurement technique, emphasizing the complementary and interconnectedness with higher-precision mass measurement methods. This includes of recent examples from TOF-Bρ mass measurements that map the evolution of nuclear structure across the nuclear landscape and significantly impact the results and interpretation of astrophysical model calculations. I also forecast expected expansion in the known nuclear mass surface from future measurement at the Facility for Rare Isotope Beams.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Continental-Scale Controls on Hyporheic Respiration Revealed by Knowledge-Guided Machine Learning

Hyporheic zone sediments regulate organic matter turnover and in-stream respiration, yet controls on sediment respiration remain poorly constrained across heterogeneous river networks, limiting prediction of stream metabolism and carbon processing at continental scales. Here, we integrate observations from ~90 river corridors across the United States in the WHONDRS consortium with a knowledge-guided machine learning (KGML) framework that couples thermodynamic rate theory with machine learning to identify dominant controls on hyporheic respiration. Diagnostic analyses show that organic matter concentration and thermodynamic favorability define an upper bound on respiration potential, whereas biological catalytic capacity and physical accessibility jointly govern realized respiration rates through interaction effects. To represent unmeasurable accessibility constraints, we use the mechanistic model as a scaffold for KGML, allowing machine learning to target residual structure not explained by process theory. This hybrid framework improves predictive skill relative to both the mechanistic model alone and fully data-driven models while preserving interpretability. These results indicate that variability in hyporheic respiration is largely mechanistically structured and demonstrate how integrating process theory with explainable AI enhances predictive performance while enabling scalable synthesis of river corridor observations.

Zheng, Jianqiu↗
DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2019

Observation of a $μs$ isomer in ${134\atop{49}}$ In $_{85}$: Proton-neutron coupling "southeast" of ${132\atop{50}}$ Sn $_{82}$

In this paper, we report on the observation of a microsecond isomeric state in the single-proton-hole, three-neutron-particle nucleus 134 In . The nuclei of interest were produced by in-flight fission of a 238 U beam at the Radioactive Isotope Beam Factory at RIKEN. The isomer depopulates through a γ ray of energy 56.7(1) keV and with a half-life of $T_{1/2}$ = 3.5 (4) $μs$. Based on the comparison with shell-model calculations, we interpret the isomer as the $I^π$ = 5 - member of the $π0g{-1\atop{9/2}}Ⓧv1f{3\atop{7/2}}$ multiplet, decaying to the $I^π$ = 7 - ground state with a reduced-transition probability of $B (E$ 2; 5 - $\rightarrow$ 7 - = 0.53 (6) W. u. Observation of this isomer, and lack of evidence in the current work for a $I^π$ = 5 - isomer decay in 132 In , provides a benchmark of the proton-neutron interaction in the region of the nuclear chart “southeast” of 132 Sn , where experimental information on excited states is sparse.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

First spectroscopic study of odd-odd Cu 78

Nuclei in the vicinity of 78 Ni are important benchmarks for nuclear structure, which can reveal changes in the shell structure far from stability. Spectroscopy of the odd-odd isotope 78 CU was performed for the first time in an experiment with the EURICA setup at the Radioactive Isotope Beam Factory at RIKEN Nishina Center. Excited states in the neutron-rich isotope were populated following the β decay of 78 Ni produced by in-flight fission and separated by the BigRIPS separator. A level scheme based on the analysis of $\gamma$-$\gamma$ coincidences is presented. Tentative spin and parity assignments were made when possible based on the β -decay feeding intensities and $\gamma$-decay properties of the excited states. Time correlations between β and $\gamma$ decay show clear indications of an isomeric state with a half-life of 3.8(4) ms. Large-scale Monte Carlo shell-model calculations were performed using the A3DA-m interaction and a valence space comprising the full f p shell and the 1⁢g 9/2 and 2⁢d 5/2 orbitals for both protons and neutrons. The comparison of the experimental results with the shell-model calculations allows interpreting the excited states in terms of spin multiplets arising from the proton-neutron interaction. In conclusion, the results provide further insight into the evolution of the proton single-particle orbitals as a function of neutron number, and quantitative information about the proton-neutron interaction outside the doubly magic 78 Ni core.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Applications of a Rayleigh-Taylor model to direct-drive laser fusion

Here, this paper presents a simple physics-based model for the interpretation of key metrics in laser direct drive. The only input parameters required are target scale, in-flight aspect ratio, and beam-to-target radius, and the importance of each has been quantified with a tailored set of cryogenic implosion experiments. These analyses lead to compact and accurate predictions of the fusion yield and areal density as a function of hydrodynamic stability, and suggest new ways to take advantage of direct drive. To provide examples, we will discuss how the inferred mix width behaves relative to theory, then show how it could be exploited to perform a direct drive implosion with a Lawson metric or Χ no α of 0.24 ± 0.02—using a novel parameter space at high velocities and beam radii on the OMEGA laser—that projects to ignition at a laser energy ≤2.0 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Fragment Intrinsic Spins and Fragments’ Relative Orbital Angular Momentum in Nuclear Fission

In this work, we present the first fully unrestricted microscopic calculations of the primary fission fragment intrinsic spins and of the fission fragments’ relative orbital angular momentum for 236 U*, 240 Pu*, and 252 Cf using the time-dependent density functional theory framework. Within this microscopic approach, free of restrictions and unchecked assumptions and which incorporates the relevant physical observables for describing fission, we evaluate the triple distribution of the fission fragment intrinsic spins and of their fission fragments’ relative orbital angular momentum and show that their dynamics is dominated by their bending collective modes in contradistinction to the predictions of the existing phenomenological models and some interpretations of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Hierarchical morphologies in co-sputter deposited thin films

Co-depositions of immiscible alloy films at specific processing conditions have yielded hierarchical microstructures which consist of distinct features at multiple length scales, often agglomerates and concentration modulations on the sub-micrometer-scale and fine nanoprecipitates in a matrix on the nanoscale. The present work examined a series of immiscible alloy systems: Cu-Mo, Cu-Ag, Cu-Fe, Cu-Ta, Mo-Ag, Cu-Mo-Ag, to determine the kinetic conditions favorable for hierarchical organization and the formation mechanism of such structures. Thin films of six immiscible systems were sputter co-deposited over a range of deposition rates from 0.12 to 2 nm/s and various temperatures from 400 to 800°C. The resulting microstructures indicate that hierarchical structures form with sufficient disparity in kinetic energy between the constituent atoms, one species being highly mobile (A) and the other relatively immobile (B). This condition arises typically at elevated deposition temperatures and reduced deposition rates but is also alloy dependent. The hierarchical structures form during deposition via phase separation and self-organization processes across the multiple length scales. The adatoms diffuse on the film surface with the highly mobile species swiftly agglomerating into A-rich domains within which B-rich nanoprecipitates form, often self-organizing into periodic arrays. The smallest B-rich nanoprecipitates in the A-rich domains are found to be coherent and in a metastable crystal structure (B taking the structure of A), but coarser precipitates that exhibit the equilibrium structure of B element. The A-rich domains are surrounded by a B-rich matrix that phase-separates into a concentration modulated structure. In conclusion, the observations are interpreted via a model incorporating material properties and process parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatial biases of information influence global estimates of soil respiration: how can we improve global predictions?

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in-situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions of the SRDB has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n=5173 and v5.0 n=10366) to determine how additional data influenced the global sum and uncertainty of Rs as predicted by a quantile regression forest model. A model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C yr-1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C yr-1, whereas parameterization using SRDBv5 yielded 96.5 Pg C yr-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C yr-1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C yr-1, respectively. Interestingly, SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) thus resulted in overall higher model uncertainty. The SRDBv5 is still biased towards northern latitudes and temperate zones, so we tested that an optimized global distribution of Rs measurements resulted in a global sum of 96.4 ± 21.4 Pg C yr-1 and lower model uncertainty. These results support current global estimates of Rs but highlight spatial inconsistencies that influence model parameterization and interpretation that should be addressed to improve regional- to global-scale predictions.

Stell, Emma↗
DOE OSTI2018

Linking variation in intrinsic water‐use efficiency to isohydricity: a comparison at multiple spatiotemporal scales

Summary Species‐specific responses of plant intrinsic water‐use efficiency ( iWUE ) to multiple environmental drivers associated with climate change, including soil moisture ( θ ), vapor pressure deficit ( D ), and atmospheric CO 2 concentration ( c a ), are poorly understood. We assessed how the iWUE and growth of several species of deciduous trees that span a gradient of isohydric to anisohydric water‐use strategies respond to key environmental drivers ( θ , D and c a ). iWUE was calculated for individual tree species using leaf‐level gas exchange and tree‐ring δ 13 C in wood measurements, and for the whole forest using the eddy covariance method. The iWUE of the isohydric species was generally more sensitive to environmental change than the anisohydric species was, and increased significantly with rising D during the periods of water stress. At longer timescales, the influence of c a was pronounced for isohydric tulip poplar but not for others. Trees’ physiological responses to changing environmental drivers can be interpreted differently depending on the observational scale. Care should be also taken in interpreting observed or modeled trends in iWUE that do not explicitly account for the influence of D .

Yi, Koong↗
DOE OSTI2022

Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods

Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐ Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The dynamics of SIV 2-LTR Circles in the Presence and Absence of CD8 + Cells

CD8 + cells play a key role in HIV/SIV infection, but their specific mechanism(s) of action in controlling the virus are unclear. 2-LTR circles are extrachromosomal products generated upon failed integration of HIV/SIV. To understand the specific effects of CD8 + cells on infected cells, we analyzed the dynamics of 2-LTR circles in SIVmac251-infected rhesus macaques (RM) treated with an integrase inhibitor (INT). Twenty RMs underwent CD8 + cell depletion, received RAL monotherapy or a combination of both. Blood, lymph nodes (LNs) and gut biopsies were routinely sampled. Plasma viral loads (pVLs) and 2-LTR circles from PBMCs and LN lymphocytes were measured with qRT-PCR. In the CD8 depletion group, an ~1 log increase in pVLs and a slow increase in PBMC 2-LTRs occurred following depletion. In the INT group, a strong decline in pVLs upon treatment initiation and no change in 2-LTR levels were observed. In the INT and CD8 + cell depletion group, a similar increase in pVLs following CD8 depletion was observed, with a modest decline following INT initiation, and 2-LTR circles significantly increased in PBMCs and LNs. Analyzing the 2-LTR data across all treatment groups with a mathematical model indicates that the data best supports an effect of CD8 + cells in killing cells prior to viral integration. Sensitivity analyses of these results confirm that effect, but also allow for additional effects, which the data does not discriminate well. Overall, we show that INT does not significantly increase the levels of 2-LTR circles. However, CD8 + cell depletion increases the 2-LTR levels, which are enhanced in the presence of an INT. CD8 + T cells play an essential role in controlling HIV and simian immunodeficiency virus (SIV) infection, but the specific mechanisms involved remain poorly understood. Due to failed viral infection, HIV and SIV can form 2-LTR extrachromosomal circles that can be quantified. We present novel data on the dynamics of these 2-LTR forms in a SIV-infected macaque model under three different treatment conditions: depletion of CD8 + cells; administration of the integrase inhibitor in a monotherapy, which favors the formation of 2-LTR circles; and combination of the two treatments. We used a new mathematical model to help interpret the data, and the results suggest that CD8 + cells exert a killing effect on infected cells prior to virus integration. These results provide new insights into the mechanisms of action of CD8 + cells in SIV infection. Here, confirmation of our results would be an important step in understanding immune control of HIV.

2-LTR circles↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗