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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A collective elastic fluctuation mechanism for decoupling and stretched relaxation in glassy colloidal and molecular liquids

We propose a microscopic theory for the decoupling of self-diffusion and structural relaxation in glass-forming liquids within the Elastically Collective Nonlinear Langevin Equation (ECNLE) activated dynamics framework. Our central hypothesis is that the heterogeneity relevant to this problem is static fluctuations of local density on the scale of 3–4 particle diameters and how this changes local packing correlations. These fluctuations modify the degree of dynamical cage expansion that mechanistically couples intracage large amplitude hopping and longer range collective elasticity in ECNLE theory. Decoupling only emerges in the deeply supercooled regime where the strongly temperature dependent elastic barrier becomes non-negligible relative to its noncooperative local analog. The theory makes predictions for various aspects of the decoupling phenomenon, including apparent fractional power law Stokes-Einstein behavior, that appear to be consistent with experiments and simulations on hard sphere fluids and molecular liquids. Of central importance is a microscopic connection between the barrier fluctuation variance and most probable barrier height. Sensible results are also obtained for the nonexponential stretching of a generic relaxation time correlation function and its temperature evolution. Nonuniversality can arise from the relative importance of the local and collective barriers (related to fragility) and the precise magnitude of the length scale that defines the transition from local cage to elastic physics. Comparison is made with a traplike model based on a Gaussian distribution of barriers.

Xie, Shi-Jie (ORCID:0000000333174720)↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
DOE OSTI2020

Enhanced pedestal H-mode at low edge ion collisionality on NSTX

The Enhanced Pedestal (EP) H-mode regime is an attractive wide-pedestal high-βp scenario for the National Spherical Torus Experiment Upgrade (NSTX-U) and next-step devices as it achieves enhanced energy confinement (H98y,2 > 1.5), large normalized pressure (βN > 5), and significant bootstrap fraction (fBS > 0.6) at Ip/BT = 2 MA/T. This regime is realized when the edge ion collisionality becomes sufficiently small that a positive feedback interaction occurs between a reduction in the ion neoclassical energy transport and an increase in the particle transport from pressure-driven edge instabilities. The EP H-mode was most often observed as a transition following a large edge-localized mode in conditions with low edge neutral recycling. It is hypothesized that the onset of pressure-driven instabilities prior to the full recovery of the neutral density leads to a temporary period with an elevated ion temperature gradient that triggers the transition to EP H-mode. Linear CGYRO and M3D-C1 calculations are compared to beam emission spectroscopy and magnetic spectroscopy in order to describe the evolution of the edge particle transport mechanisms during the ELM recovery and the saturated EP H-mode state. The observations are consistent with the hypothesis that the onset of pressure-driven edge instabilities, such as the kinetic ballooning mode and kink-peeling mode, can be responsible for the increased particle transport in EP H-mode.

Battaglia, D. J. (ORCID:0000000188979740)↗
DOE OSTI2020

Data-driven materials research enabled by natural language processing and information extraction

Given the emergence of data science and machine learning throughout all aspects of society, but particularly in the scientific domain, there is increased importance placed on obtaining data. Data in materials science are particularly heterogeneous, based on the significant range in materials classes that are explored and the variety of materials properties that are of interest. This leads to data that range many orders of magnitude, and these data may manifest as numerical text or image-based information, which requires quantitative interpretation. The ability to automatically consume and codify the scientific literature across domains - enabled by techniques adapted from the field of natural language processing - therefore has immense potential to unlock and generate the rich datasets necessary for data science and machine learning. This review focuses on the progress and practices of natural language processing and text mining of materials science literature and highlights opportunities for extracting additional information beyond text contained in figures and tables in articles. Here, we discuss and provide examples for several reasons for the pursuit of natural language processing for materials, including data compilation, hypothesis development, and understanding the trends within and across fields. Current and emerging natural language processing methods along with their applications to materials science are detailed. We, then, discuss natural language processing and data challenges within the materials science domain where future directions may prove valuable.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spatially resolved photoluminescence brightening in individual single-walled carbon nanotubes

Single-molecule microscopy was used to image photoluminescence (PL) brightening of individual sodium-dodecyl-sulfate (SDS)-wrapped single-walled carbon nanotubes (SWCNTs) upon the addition of dithiothreitol (DTT). PL enhancement varied for each nanotube (NT), with some brightening by 16% and others by a factor of about 7. Interestingly, NTs that displayed lower initial QY values showed the largest increases in PL enhancement. SDS-SWCNTs longer than the diffraction limit were studied in order to spatially resolve the brightening phenomenon. Quite unexpectedly, a uniform, single-step PL brightening along the NT was consistently observed, suggesting that the PL enhancement is the result of a non-localized process. Furthermore, the even PL brightening seen over SWCNTs that are micrometers long implies that single point defect sites, which are known to be largely responsible for exciton nonradiative decay, play no significant role in the brightening process. Interestingly, affixing the SWCNT strongly to the substrate surface mitigated the PL brightening response, consistent with a hypothesis that surfactant reorganization upon the addition of DTT is responsible for exciton PL brightening.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Self-compensation in heavily Ge doped AlGaN: A comparison to Si doping

Self-compensation in Ge- and Si-doped Al 0.3 Ga 0.7 N has been investigated in terms of the formation of III vacancy and donor-vacancy complexes. Both Ge- and Si-doped AlGaN layers showed a compensation knee behavior with impurity compensation (low doping regime), compensation plateau (medium doping regime), and self-compensation (high doping regime). A maximum free carrier concentration of 4–5 × 10 19 cm –3 was obtained by Ge doping, whereas Si doping resulted in only half of that value, ~2 × 10 19 cm –3 . A DFT calculation with the grand canonical thermodynamics model was developed to support the hypothesis that the difference in self-compensation arises from the difference in the formation energies of the V III -n•donor complexes relative to their onsite configurations. The model suggested that the V III -2•donor and V III -3•donor complexes were responsible for self-compensation for both Ge- and Si-doped AlGaN. However, a lower free carrier concentration in Si-doped samples was due to a high V III -3•Si concentration, resulting from a lower energy of formation of V III -3•Si.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Observations of a plectonemic configuration in a stable magnetized plasma jet

Astrophysical jets are collimated high-speed outflows emerging from spinning and accreting matter around celestial objects and may spontaneously result from self-organized processes. Magnetic self-organization is commonly observed in laboratory plasma physics experiments; however, they require close-fitting flux conservers to constrain and stabilize the toroidal or cylindrical structures. Here we report the first observations of a long, stable, free-boundary plasma jet far from chamber walls, embedding a double-helix magnetic structure resembling a force-free plectonemic Taylor state. The jets arise from an experimental setup that mimics an accretion disk and has no close-fitting solid flux conserver. The results support the hypothesis that self-organization could be a universal, intrinsic explanation for jet formation, collimation, and stability and may help explain double-helix features in celestial observations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Positive charging of grains in an afterglow plasma is enhanced by ions drifting in an electric field

In a plasma, the polarity of a dust grain's charge is typically negative, but it can reverse and become positive in an afterglow, when the power sustaining the plasma is switched off. This positive charging, which occurs in the afterglow's first few milliseconds, is studied for grains much larger than a few nm. It is hypothesized that the positive charging is enhanced by the presence of a dc electric field, which causes ions to drift through the neutral gas. A larger value of the reduced electric field E/N leads to a larger ion kinetic energy and thus a greater collection of positive charge on a grain. The maximum possible positive charge is attained if the grain's surface potential rises to match the ion kinetic energy, at a time before ions have departed and the grain's charge becomes frozen. Thereafter, when vacuum conditions prevail, the grain will retain its positive residual charge. In an experiment, dust grains were electrically levitated in a capacitively coupled plasma until the power was abruptly turned off. In the afterglow, grains fell faster than expected due to gravity alone, indicating a downward electric force, in the presence of a remaining dc electric field. Acceleration measurements yielded repeatable results for the residual charge's value, which was of the order +10 4 e and increased with E/N, supporting the hypothesis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Effect of relative humidity on the nonlinear elastic response of granular media

We study the influence of relative humidity (RH) on the nonlinear elastic response of granular media. Previous work has shown that the nonlinear elastic response of consolidated granular media like rocks likely arises from two distinct mechanisms; however, we do not have a clear understanding of their physical origins at the microscopic scale. Here, we conduct dynamic acousto-elastic testing (DAET) on samples of glass beads under dry (~10%), ambient (~60%), and humid (~100%) conditions at room temperature and a constant static stress of 4 MPa. DAET allows us to retrieve the full nonlinear elastic response, including transient softening and hysteretic effects. We find that the elastic nonlinearity of humid samples is an order of magnitude larger than dry samples. Moreover, we find that all extracted nonlinear parameters increase with RH. This overall increase in nonlinearity is consistent with findings from previous studies and with the hypothesis that water adsorption on the grains makes the contact junctions weaker and prone to greater disturbances when subjected to dynamic stressing. Our results also suggest that, if indeed both mechanisms coexist, they are affected in a similar fashion in these glass bead samples and cannot be distinguished by varying RH.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reactive burn model calibration using high-throughput initiation experiments at sub-millimeter length scales

We report a first-of-its-kind model calibration was performed using Sandia National Laboratories’ high-throughput initiation (HTI) experiment for two types of vapor-deposited explosive films consisting of hexanitrostilbene (HNS) or pentaerythritol tetranitrate (PETN). These films exhibit prompt initiation, and they reach steady detonation at sub-millimeter length scales. Following prior work on HNS, we test the hypothesis of approximating these explosive films as fine-grained homogeneous solids with simple Arrhenius kinetics burn models. The model calibration process is described herein using a single-step as well as a two-step Arrhenius rate law, and it consists of systematic parameter sampling leading to a reduction in the model degrees of freedom. Multiple local minima are observed; results are given for seven different optimized parameter sets. Each model set is further evaluated in a two-dimensional simulation of the critical failure thickness for a sustained detonation. Overall, the two-step Arrhenius kinetics model captures the observed behavior for HNS; however, neither model produces a good fit to the PETN data. We hypothesize that the HTI results for PETN correspond to a heterogeneous response, owing to the smaller reaction zone of PETN compared to HNS (i.e., it does not homogenize the fine-grained hot spots as well). Future work should consider using the ignition and growth model for PETN, as well as other reactive burn models such as xHVRB, AWSD, PiSURF, and CREST.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Toward a machine-guided approach to energetic material discovery

In this article, we trained a machine learning (ML) model to connect microstructural details of an energetic material formulation to its performance for the purpose of guiding the discovery of new explosive formulations. Our hypothesis was that the algorithm would robustly learn the training data and produce an accurate surrogate model. Specifically, the algorithm learned the relationship between details of the void size distribution (VSD), initiating shock pressure, and the energetic material performance. We used realistic constraints on the VSD and a range of cases were ingested by a physically informed reactive flow model working within a hydrodynamic solver running on high-performance computing resources. The ML algorithm produced a surrogate model that accurately predicted known test points around the parameter space. In addition to the utility of the model and the process used for its development, we noted interesting comparisons between what we, the authors—subject matter experts, would heuristically conclude from the training data and the surrogate model predictions. We detected nuanced details that were missed by the surrogate model; however, these details are not important to an energetic material formulator. We concluded that the algorithm did indeed robustly learn the training data and produce an accurate surrogate model. We further concluded that the surrogate model is a powerful tool to guide the formulator in the absence of subject matter experts and limited-access computing resources.

42 ENGINEERING↗
DOE OSTI2022

Exploring implosion designs for increased compression on the National Ignition Facility using high density carbon ablators

It has long been recognized that high compression, and hence good confinement, is essential to achieving high yields in inertial confinement fusion implosions. In pursuit of multi-megajoule yields on the National Ignition Facility (NIF), a new campaign has begun aimed at testing the hypothesis that controlling hydrodynamic stability is key to achieving effective higher compression with the high density carbon ablators currently fielded on NIF. This campaign is built around a new implosion design, called SQ-n, that is derived from the uniquely stable Bigfoot design tested on NIF in 2016–2019. While very stable and with performance that was quite close to one-dimensional expectations, Bigfoot was a relatively high adiabat, and consequently lower compression design. The goal of SQ-n is then to evolve Bigfoot toward a higher compression design but without compromising its unique stability characteristics. Specifically, SQ-n adopts a ramped foot pulse shape to minimize early time Richtmyer–Meshkov instability growth and uses an ablator dopant distribution extending all of the way to the fuel–ablator interface that simulations suggest further reduces perturbation growth. In conclusion, this paper describes the design philosophy pursued with SQ-n, the results of instability modeling of the candidate design, and the experimental campaign planned to test these ideas in the near future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Use of a Mylar filter to eliminate vacuum ultraviolet pulse pileup in low-energy x-ray measurements

We describe a method to reduce vacuum ultraviolet (VUV) pulse pileup (PPU) in x-ray pulse-height Silicon Drift Detector (SDD) signals. An Amptek FAST SDD, with C1 (Si 3 N 4 ) window, measures bremsstrahlung emitted from PFRC-2 plasma to extract the electron temperature (T e ) and density (n e ). The C1 window has low transmissivity for photons with energy below 200 eV though will transmit some VUV and soft x-ray photons, which PFRC-2 plasmas abundantly emit. Multi-VUV-photon PPU contaminates the interpretation of x rays with energy > 100 eV, particularly in a low-energy exponential tail. The predicted low transmissivity of ~1 μm thick Mylar [polyethylene terephthalate (PET)] to photons of energy <100 eV led to the selection of Mylar as the candidate filter to reduce VUV PPU. Experiments were conducted on an x-ray tube with a graphite target and on a quasi-Maxwellian tenuous plasma (n e ~ 10 9 cm –3 ) with effective temperatures reaching 1500 eV. A Mylar filter thickness of 850 nm is consistent with the results. The Mylar-filter-equipped SDD was then used on the PFRC-2 plasma, showing a substantial reduction in the low-energy x-ray signal, supporting our hypothesis of the importance of VUV PPU. Here, we describe the modeling and experiments performed to characterize the effect of the Mylar filter on SDD measurements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Kinetic simulations of ignited mode cesium vapor thermionic converters

Cesium vapor thermionic converters are an attractive method of converting high-temperature heat directly to electricity, but theoretical descriptions of the systems have been difficult due to the multi-step ionization of Cs through inelastic electron–neutral collisions. This work presents particle-in-cell simulations of these converters, using a direct simulation Monte Carlo collision model to track 52 excited states of Cs. Here, these simulations show the dominant role of multi-step ionization, which also varies significantly based on both the applied voltage bias and pressure. The electron energy distribution functions are shown to be highly non-Maxwellian in the cases analyzed here. A comparison with previous approaches is presented, and large differences are found in ionization rates due especially to the fact that previous approaches have assumed Maxwellian electron distributions. Finally, an open question regarding the nature of the plasma sheaths in the obstructed regime is discussed. The one-dimensional simulations did not produce stable obstructed regime operation and thereby do not support the double-sheath hypothesis.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Adjustments to the law of the wall above an Amazon forest explained by a spectral link

Modification to the law of the wall represented by a dimensionless correction function ϕ RSL (z/h) is derived using atmospheric turbulence measurements collected at two sites in the Amazon in near-neutral stratification, where z is the distance from the forest floor and h is the mean canopy height. The sites are the Amazon Tall Tower Observatory for z/h∈ [1,2.3] and the Green Ocean Amazon (GoAmazon) site for z/h∈ [1,1.4]. Here, a link between the vertical velocity spectrum E ww (k) (k is the longitudinal wavenumber) and ϕ RSL is then established using a co-spectral budget (CSB) model interpreted by the moving-equilibrium hypothesis. The key finding is that ϕ RSL is determined by the ratio of two turbulent viscosities and is given as ν t,BL /ν t,RSL, where ν t,RSL = (1/A)∫$^{∞}_{0}$ τ(k)E ww (k)dk, ν t,BL = k v (z−d)u * , τ(k) is a scale-dependent decorrelation time scale between velocity components, A = C R /(1−C I ) = 4.5 is predicted from the Rotta constant C R = 1.8, and the isotropization of production constant C I = 3/5 given by rapid distortion theory, k v is the von Kármán constant, u * is the friction velocity at the canopy top, and d is the zero-plane displacement. Because the transfer of energy across scales is conserved in E ww (k) and is determined by the turbulent kinetic energy dissipation rate (ε), the CSB model also predicts that ϕ RSL scales with L BL /L d , where L BL is the length scale of attached eddies to z = d, and L d = u$_{*}^{3}$/ε is a macro-scale dissipation length.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Sucrose transport inside the phloem: Bridging hydrodynamics and geometric characteristics

In plants, the delivery of the products of photosynthesis is achieved through a hydraulic system labeled as phloem. This semi-permeable plant tissue consists of living cells that contract and expand in response to fluid pressure and flow velocity fluctuations. The Münch pressure flow theory, which is based on osmosis providing the necessary pressure gradient to drive the mass flow of carbohydrates, is currently the most accepted model for such sucrose transport. When this hypothesis is combined with the conservation of fluid mass and momentum as well as sucrose mass, many simplifications must be invoked to mathematically close the problem and to resolve the flow. Here this study revisits such osmotically driven flows by developing a new two-dimensional numerical model in cylindrical coordinates for an elastic membrane and a concentration-dependent viscosity. It is demonstrated that the interaction between the hydrodynamic and externally supplied geometrical characteristic of the phloem has a significant effect on the front speed of sucrose transport. These results offer a novel perspective about the evolutionary adaptation of plant hydraulic traits to optimize phloem soluble compounds transport efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Large-database cross-verification and validation of tokamak transport models using baselines for comparison

State-of-the-art 1D transport solvers ASTRA and TRANSP are verified, then validated across a large database of semi-randomly selected, time-dependent DIII-D discharges. Various empirical models are provided as baselines to contextualize the validation figures of merit using statistical hypothesis tests. For predicting plasma temperature profiles, no statistically significant advantage is found for the ASTRA and TRANSP simulators over a baseline empirical (two-parameter) model. For predicting stored energy, a significant advantage is found for the simulators over a baseline empirical model based on confinement time scaling. Uncertainty in the results due to diagnostic and profile fitting uncertainties is approximated and determined to be insignificant due in part to the large quantity of discharges employed in the study. Advantages are discussed for validation methodologies like this one that employ (1) large databases and (2) baselines for comparison that are specific to the intended use-case of the model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗