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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multi-Modal Ptychography: Recent Developments and Applications

Utilisation of partially coherent X-rays in Coherent X-ray Diffraction Imaging (CDI) and X-ray ptychography have brought intense research and developments for the past few years. Generally, data reconstructions convergence can be achieved by considering to incorporate partially coherent X-rays in ptychographic iterative reconstructions algorithms. Recently, exploration of both X-ray probe and sample modes have been studied and it was suggested that sufficient constraints are required to achieve accurate data reconstructions due to the mathematical inseparability of multiple sample modes that are superimposed into a single set of ptychographic intensity patterns. Here we review the multi-modal ptychography that can be used as a new emerging technique to investigate dynamical sample systems with partially coherent X-rays by recent experimental and simulated demonstrations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Dynamic X-ray diffraction sampling for protein crystal positioning

A sparse supervised learning approach for dynamic sampling (SLADS) is described for dose reduction in diffraction-based protein crystal positioning. Crystal centering is typically a prerequisite for macromolecular diffraction at synchrotron facilities, with X-ray diffraction mapping growing in popularity as a mechanism for localization. In X-ray raster scanning, diffraction is used to identify the crystal positions based on the detection of Bragg-like peaks in the scattering patterns; however, this additional X-ray exposure may result in detectable damage to the crystal prior to data collection. Dynamic sampling, in which preceding measurements inform the next most information-rich location to probe for image reconstruction, significantly reduced the X-ray dose experienced by protein crystals during positioning by diffraction raster scanning. The SLADS algorithm implemented herein is designed for single-pixel measurements and can select a new location to measure. In each step of SLADS, the algorithm selects the pixel, which, when measured, maximizes the expected reduction in distortion given previous measurements. Ground-truth diffraction data were obtained for a 5 µm-diameter beam and SLADS reconstructed the image sampling 31% of the total volume and only 9% of the interior of the crystal greatly reducing the X-ray dosage on the crystal. Furthermore, by using in situ two-photon-excited fluorescence microscopy measurements as a surrogate for diffraction imaging with a 1 µm-diameter beam, the SLADS algorithm enabled image reconstruction from a 7% sampling of the total volume and 12% sampling of the interior of the crystal. When implemented into the beamline at Argonne National Laboratory, without ground-truth images, an acceptable reconstruction was obtained with 3% of the image sampled and approximately 5% of the crystal. The incorporation of SLADS into X-ray diffraction acquisitions has the potential to significantly minimize the impact of X-ray exposure on the crystal by limiting the dose and area exposed for image reconstruction and crystal positioning using data collection hardware present in most macromolecular crystallography end-stations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Entropy-driven Optimal Sub-sampling of Fluid Dynamics for Developing Machine-learned Surrogates

Optimal sub-sampling of large datasets from fluid dynamics simulations is essential for training reduced-order machine learned models. A method using Shannon entropy was developed to weight flow features according to their level of information content, such that the most informative features can be extracted and used for training a surrogate model. The method is demonstrated in the canonical flow over a cylinder problem simulated with OpenFOAM. Both time-independent predictions and temporal forecasting were investigated as well as two types of prediction targets: local per-grid-point predictions and global per-time-step predictions. When tested on training a surrogate model, results indicate that our entropy-based sampling method typically outperforms random sampling and yields more reproducible results in less iterations. Finally, the method was used to train a surrogate model for modeling turbulence in magnetohydrodynamic flows, which revealed various challenges and opportunities for future research.

Brewer, Wes↗
DOE OSTI2016

Uncertainty quantification for generalized Langevin dynamics

Here, we present efficient finite difference estimators for goal-oriented sensitivity indices with applications to the generalized Langevin equation (GLE). In particular, we apply these estimators to analyze an extended variable formulation of the GLE where other well known sensitivity analysis techniques such as the likelihood ratio method are not applicable to key parameters of interest. These easily implemented estimators are formed by coupling the nominal and perturbed dynamics appearing in the finite difference through a common driving noise or common random path. After developing a general framework for variance reduction via coupling, we demonstrate the optimality of the common random path coupling in the sense that it produces a minimal variance surrogate for the difference estimator relative to sampling dynamics driven by independent paths. In order to build intuition for the common random path coupling, we evaluate the efficiency of the proposed estimators for a comprehensive set of examples of interest in particle dynamics. These reduced variance difference estimators are also a useful tool for performing global sensitivity analysis and for investigating non-local perturbations of parameters, such as increasing the number of Prony modes active in an extended variable GLE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Attosecond Transient Grating Spectroscopy with Near-Infrared Grating Pulses and an Extreme Ultraviolet Diffracted Probe

Transient grating spectroscopy has become a mainstay among metal and semiconductor characterization techniques. Here, we extend the technique toward the shortest achievable time scales by using tabletop high-harmonic generation of attosecond extreme ultraviolet (XUV) pulses that diffract from transient gratings generated with sub-5 fs near-infrared (NIR) pulses. We demonstrate the power of attosecond transient grating spectroscopy (ATGS) by investigating the ultrafast photoexcited dynamics in an Sb semimetal thin film. ATGS provides an element-specific, background-free signal unfettered by spectral congestion, in contrast to transient absorption spectroscopy. With ATGS measurements in Sb polycrystalline thin films, we observe the generation of coherent phonons and investigate the lattice and carrier dynamics. Among the latter processes, we extract carrier thermalization, hot carrier cooling, and electron-hole recombination, which are on the order of 20 fs, 50 fs, and 2 ps time scales, respectively. Furthermore, the simultaneous collection of transient absorption and transient grating data allows us to extract the total complex dielectric constant in the sample dynamics with a single measurement, including the real-valued refractive index, from which we are also able to investigate carrier-phonon interactions and longer-lived phonon dynamics. The outlined experimental technique expands the capabilities of transient grating spectroscopy and attosecond spectroscopies by providing a wealth of information concerning carrier and lattice dynamics with an element-selective technique at the shortest achievable time scales.

Quintero-Bermudez, Rafael↗
DOE OSTI2020

Influence of TMAO as co-solvent on the gelation of silica-PNIPAm core-shell nanogels at intermediate volume fractions

We study the structure and dynamics of poly(N-isopropylacrylamide) (PNIPAm) core-shell nanogels dispersed in aqueous trimethylamine N-oxide (TMAO) solutions by means of smallangle X-ray scattering and X-ray photon correlation spectroscopy (XPCS). Upon increasing the temperature above the lower critical solution temperature of PNIPAm at 33°C, a colloidal gel is formed as identified by an increase of I(q) at small q as well as a slowing down of sample dynamics by various orders of magnitude. With increasing TMAO concentration the gelation transition shifts linearly to lower temperatures. Above a TMAO concentration of approximately 0.40 mol/L corresponding to a 1:1 ratio of TMAO and NIPAm groups, collapsed PNIPAm states are found for all temperatures without any gelation transition. This suggests that reduction of PNIPAm-water hydrogen bonds due to the presence of TMAO results in a stabilisation of the collapsed PNIPAm state and suppresses gelation of the nanogel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Correlation of dynamic dielectric measurements with viscosity in polymeric resin systems

Dynamic dielectric analysis has been used to study curing polymer systems and thermoplastics. Measurements have been made over a frequency range of six decades. This wide range of frequencies increases the amount of information which can be obtained. The data are analyzed in terms of the frequency dependence of the specific conductivity sigma and the relaxation time tau, parameters which are characteristic of the cure state of the material and independent of size of the sample. Dynamic dielectric measurements have been correlated with viscosity for the polysulfone thermoplastic UDEL-P1700 and with viscosity and ultrasonic measurements on the DGEBA-type epoxy Epon 828 cured with Agent U. The experimental results suggest that when ionic processes dominate the dielectric response, the intensive property sigma is a good monitor of the resin's viscosity. The results show that the dielectric relaxation time tau can be used to monitor the state of the system and the extent and rate of the reaction.

Kranbuehl, D.↗
DOE OSTI2018

One-minute nano-tomography using hard X-ray full-field transmission microscope

Full field transmission X-ray microscope (TXM) is a powerful technique for non-destructive 3D imaging with nanometer-scale spatial resolution. However, to date, the typical acquisition time with the hard X-ray TXM at a synchrotron facility is > 10 minutes for a 3D nano-tomography data set with sub-50 nm spatial resolution. This is a significant limit on the types of 3D dynamics that can be investigated using this technique. Here, we present a demonstration of one-minute nano-tomography with sub-50 nm spatial resolution. This achievement is made possible with an in-house designed and commissioned TXM instrument at the Full-field X-ray Imaging (FXI) beamline at the National Synchrotron Light Source-II (NSLS-II) at Brookhaven National Laboratory. This capability represents an order of magnitude decrease in the time required for studying sample dynamics with 10s of nm spatial resolution.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Origin of Metal-Insulator Transition in Rare-Earth Nickelates

Rare-earth nickelates RNiO3 (R=rare-earth element) undergo coupled structural, metal-insulator, and magnetic changes as temperature is lowered. Because the metal-insulator transition often occurs together with a symmetry-lowering distortion, it is commonly viewed as lattice driven. Here we use QSGW calculations to separate the roles of spin and structure. In the high-symmetry Pbnm phase, imposing spin disproportionation already starts to open an electronic gap, although the undistorted lattice cannot stabilize a full insulating state in both spin channels. In the low-symmetry P21/n phase, removing the spin disproportionation destroys the insulating solution even though the bond disproportionation remains. These tests show that spin disproportionation is the primary electronic driver of gap formation, while structural disproportionation acts as the enabler that allows the inequivalent Ni states to become spatially separated and fully insulating. As an explicit finite-temperature example, machine-learned molecular dynamics for NdNiO3 at 220 K shows that a nominally high-symmetry Pbnm structure dynamically samples local Ni-O bond disproportionation, while retaining Pbnm symmetry on average. This illustrates a general structural channel by which thermal fluctuations in the high-symmetry phase can help stabilize locally spin-disproportionated states.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Evaluation of chromatographic resins for high efficacy separation of radium from chemical analogues

The separation of Radium (Ra) from interfering chemical analogues is of major importance for both the accurate measurement of Ra-226 in environmental samples and the preparation of radium targets destined for medical isotope production. The adsorption behavior of Ra, Barium (Ba), and Strontium (Sr) was investigated on multiple crown-ether-based proprietary extraction chromatographic resins developed by TrisKem Int. Among these, the TK101 resin, characterized by a novel room-temperature ionic liquid (RTIL) dual extractant–solvent system, demonstrated enhanced affinity and selectivity, particularly at low acid concentrations, achieving a D w of 3477 ± 73 for Ra in 0.04 M HNO₃, whereas the maximum Ra D w obtained with the traditional Sr resin in 8 M HNO 3 was only 12.33 ± 0.4. In 0.04 M HNO 3 , a Ra distribution ratio (D w ) of 776 was achieved within 1 min, indicating rapid uptake kinetics. The adsorption capacities were estimated at 19.6 ± 0.1 mg/g for Ba and 12.0 ± 0.2 mg/g for Sr. However, competing alkali metals significantly reduced alkaline earth metal uptake, indicating that the TK101 resin is best suited for radium purification following a preconcentration step to minimize matrix effects in high salinity samples. Dynamic column studies confirmed the strong chromatographic performance of the TK101 resin, with no significant Ba or Sr breakthrough in the Ra fraction and a Ra recovery of 87 ± 2.8%. These results demonstrate the potential of the TK101 resin for selective radium separation in analytical and radiochemical applications.

and nuclear chemistry↗
DOE OSTI2026

Elucidating Lithium Transport Mechanisms in Disordered LiF from Machine-Learning Molecular Dynamics Simulations

Lithium fluoride (LiF) is a ubiquitous component of solid- and cathode–electrolyte interphases, yet its functional role remains unclear under the structural and chemical heterogeneity typical of cycling batteries. Here, we systematically quantify how structural disorder, off-stoichiometry, and strain govern Li-ion transport in LiF. Using a machine-learning potential to enable extensive molecular-dynamics sampling, we compare crystalline and amorphous LiF, Li 0.95 F, and LiF 0.95 , and evaluate the impact of small homogeneous deformations. Defect-free crystalline LiF is effectively ion-blocking at 300−500 K, whereas amorphization generates free-volume–assisted percolation pathways that facilitates Li-ion diffusion. At elevated temperatures, thermodynamically driven crystallization disrupts these pathways, leading to non-Arrhenius behavior. In crystalline phases, Li deficiency activates vacancy-mediated diffusion, while in amorphous LiF, transport is governed primarily by network connectivity. Strain is found to have only a marginal effect on Li mobility in both crystalline and amorphous structures.

Batteries↗
DOE OSTI2025

PhaseT3M: 3D imaging at 1.6 Å resolution via electron cryo-tomography with nonlinear phase retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to an approximately 7 nm Co3O4 nanoparticle on an approximately 30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover ‘missing wedge’ information. Applied directly to biological particles, it enhances reconstruction quality and reduces artifacts, establishing a standard for routine 3D imaging with phase contrast.

Biophysics↗
DOE OSTI2017

Formation of diamonds in laser-compressed hydrocarbons at planetary interior conditions

The effects of hydrocarbon reactions and diamond precipitation on the internal structure and evolution of icy giant planets such as Neptune and Uranus have been discussed for more than three decades. Inside these celestial bodies, simple hydrocarbons such as methane, which are highly abundant in the atmospheres, are believed to undergo structural transitions that release hydrogen from deeper layers and may lead to compact stratified cores. Indeed, from the surface towards the core, the isentropes of Uranus and Neptune intersect a temperature–pressure regime in which methane first transforms into a mixture of hydrocarbon polymers, whereas, in deeper layers, a phase separation into diamond and hydrogen may be possible. Here in this paper, we show experimental evidence for this phase separation process obtained by in situ X-ray diffraction from polystyrene (C 8 H 8 ) n samples dynamically compressed to conditions around 150 GPa and 5,000 K; these conditions resemble the environment around 10,000 km below the surfaces of Neptune and Uranus. Our findings demonstrate the necessity of high pressures for initiating carbon–hydrogen separation and imply that diamond precipitation may require pressures about ten times as high as previously indicated by static compression experiments. In conclusion, our results will inform mass–radius relationships of carbon-bearing exoplanets, provide constraints for their internal layer structure and improve evolutionary models of Uranus and Neptune, in which carbon–hydrogen separation could influence the convective heat transport.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Extraction and biomolecular analysis of dermal interstitial fluid collected with hollow microneedles

Dermal interstitial fluid (ISF) is an underutilized information-rich biofluid potentially useful in health status monitoring applications whose contents remain challenging to characterize. Here, we present a facile microneedle approach for dermal ISF extraction with minimal pain and no blistering for human subjects and rats. Extracted ISF volumes were sufficient for determining transcriptome, and proteome signatures. We noted similar profiles in ISF, serum, and plasma samples, suggesting that ISF can be a proxy for direct blood sampling. Dynamic changes in RNA-seq were recorded in ISF from induced hypoxia conditions. Finally, we report the first isolation and characterization, to our knowledge, of exosomes from dermal ISF. The ISF exosome concentration is 12–13 times more enriched when compared to plasma and serum and represents a previously unexplored biofluid for exosome isolation. This minimally invasive extraction approach can enable mechanistic studies of ISF and demonstrates the potential of ISF for real-time health monitoring applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Active learning of neural network potentials for rare events

Developing an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling and assessing model transferability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2019

PageRank as a Collective Variable to Study Complex Chemical Transformations and Their Energy Landscapes

A reduced set of reaction coordinates are often employed in chemistry to describe the collective change between reactants and products within the context of rare event theories and in the exploration of energy landscapes. While incredibly powerful, use of these collective variables becomes increasingly challenging as the systems under study become more complex. Recent advancement of new descriptions of collective molecular coordinates have included PageRank (PR) analysis of the network of interactions about molecules and atoms within a system. Herein, PR is developed as a continuous variable that not only captures chemical transformations but can also be employed as a collective variable for a process that could span a highly dimensional energy surface. The derivatives of PR with respect to atomic coordinates are presented, and subsequently combined with forces during a chemical transformation within biased molecular dynamics sampling simulations to create the potential of mean force (PMF). This technique is demonstrated for two reactions: 1) the transformation of tetrahedral [Al(OH)4]-(aq) to octahedral [Al(OH)4(H2O)2]-(aq), and 2) the formation of contact ion pairs (CIPs) and solvent separated ion pairs (SSIPs) of Na+ and OH- in bulk water. Comparison of the behavior of the PR collective variable in both examples reveals that the ability of the PR to resolve local versus extended configurational attributes is tuned by the size of the network employed. This is a unique feature of PageRank that enables control over the resolution of the PMF that is produced.

chemistry, theories, PageRank↗