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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗
DOE OSTI2025

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Nichol, J. Jake [Univ. of New Mexico, Albuquerque,↗
DOE OSTI2022

Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.

Preston, Victoria↗
DOE OSTI2019

Altersbestimmung an thermalen Tiefenwässern im Oberjura des Molassebeckens mittels Krypton-Isotopen

81 Kr (half-life 229,000 years) is an ideal tracer for old groundwater. The Upper Jurassic rock in the deep Molasse Basin is an outstanding geothermal groundwater reservoir (with temperatures up to 140 °C). However, due to the complex groundwater evolution (ion and isotope exchange, gas flux, etc.), comprehensive hydrogeological studies completed to date, including 14 C–DIC and He isotopes, could not resolve the recharge dynamics and residence times. Nine geothermal wells were therefore sampled for 81 Kr/ 85 Kr employing the laser-based atom trap tracer analysis technique (ATTA). In the western and central basin, the results reveal predominant groundwater recharge during the last glacial period with one sample influenced by infiltration during the earlier glacial period. Recharge signatures and 81 Kr-model-ages fit very well to subglacial recharge with cross-formational flow through the sedimentary cover (600 to >3000 m deep). In the eastern basin, the results point to the Cromerian complex, indicating a slower flow system with less influence from recharge during glacial periods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In-Situ SEM High Strain Rate Testing of Large Diameter Micropillars Followed by TEM and EBSD Postmortem Analysis

Background Dislocation dynamic simulations are intended as a tool to understand and predict the mechanical behavior of metallic materials, but its prediction has never been directly verified by experiments due to differences in specimen strain rate and size. Objective In this work, a comprehensive experimental framework is proposed to attempt direct comparison between experiments and discrete dislocation dynamics (DDD) modelling. Methods By integrating high-throughput sample fabrication and a customized testing apparatus, the sample size and strain rate typically employed in DDD simulations are explored experimentally. Constitutive properties such as stress-strain response are measured, and microstructural information is obtained from transmission electron microscopy (TEM) imaging, electron backscatter diffraction (EBSD), and TEM-based orientation mapping. Results Magnesium and copper were selected, as case studies, to demonstrate the newly developed experimental procedure. Measured stress-strain responses for Mg are consistent with those obtained with a miniaturized Hopkison bar experiments. Furthermore, by exploiting the validated workflow, the effect of strain rate on micropillar heterogeneous deformation and associated dislocation plasticity were revealed. Conclusion The work establishes a methodology for the systematic study of not only metals but also other materials and structures at the microscale and high strain rates.

42 ENGINEERING↗
DOE OSTI2021

Rapid preparation of nanodiscs for biophysical studies

Nanodiscs, which are disc-shaped entities that contain a central lipid bilayer encased by an annulus of amphipathic helices, have emerged as a leading native-like membrane mimic. The current approach for the formation of nanodiscs involves the creation of a mixed-micellar solution containing membrane scaffold protein, lipid, and detergent followed by a time consuming process (3–12 h) of dialysis and/or incubation with sorptive beads to remove the detergent molecules from the sample. In contrast, the methodology described herein provides a facile and rapid procedure for the preparation of nanodiscs in a matter of minutes (<15 min) using Sephadex® G-25 resin to remove the detergent from the sample. A panoply of biophysical techniques including analytical ultracentrifugation, dynamic light scattering, gel filtration chromatography, circular dichroism spectroscopy, and cryogenic electron microscopy were employed to unequivocally confirm that aggregates formed by this method are indeed nanodiscs. We believe that this method will be attractive for time-sensitive and high-throughput experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A graph theoretic framework for representation, exploration and analysis on computed states of physical systems

A graph theoretic perspective is taken for a range of phenomena in continuum physics in order to develop representations for analysis of large scale, high-fidelity solutions to these problems. Of interest are phenomena described by partial differential equations, with solutions being obtained by computation. The motivation is to gain insight that may otherwise be difficult to attain because of the high dimensionality of computed solutions. We consider graph theoretic representations that are made possible by low-dimensional states defined on the systems. These states are typically functionals of the high-dimensional solutions, and therefore retain important aspects of the high-fidelity information present in the original, computed solutions. Our method is rooted in regarding each state as a vertex on a graph and identifying edges via processes that are induced either by numerical solution strategies, or by the physics. Correspondences are drawn between the sampling of stationary states, or the time evolution of dynamic phenomena, and the analytic machinery of graph theory. A collection of computations is examined in this framework and new insights to them are presented through analysis of the corresponding graphs.

42 ENGINEERING↗
DOE OSTI2023

Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties

Here we introduce a denoising diffusion algorithm to discover microstructures with nonlinear fine-tuned properties. Denoising diffusion probabilistic models are generative models that use diffusion-based dynamics to gradually denoise images and generate realistic synthetic samples. By learning the reverse of a Markov diffusion process, we design an artificial intelligence to efficiently manipulate the topology of microstructures to generate a massive number of prototypes that exhibit constitutive responses sufficiently close to designated nonlinear constitutive behaviors. To identify the subset of microcstructures with sufficiently precise fine-tuned properties, a convolutional neural network surrogate is trained to replace high-fidelity finite element simulations to filter out prototypes outside the admissible range. Results of this study indicate that the denoising diffusion process is capable of creating microstructures of fine-tuned nonlinear material properties within the latent space of the training data. More importantly, this denoising diffusion algorithm can be easily extended to incorporate additional topological and geometric modifications by introducing high-dimensional structures embedded in the latent space. Numerical experiments are conducted on the open-source mechanical MNIST data set (Lejeune, 2020). Consequently, this algorithm is not only capable of performing inverse design of nonlinear effective media, but also learns the nonlinear structure–property map to quantitatively understand the multiscale interplay among the geometry, topology, and their effective macroscopic properties.

42 ENGINEERING↗
DOE OSTI2018

Synchrotron-based measurement of aluminum agglomerates at motor conditions

Solid rocket propellant combustion is hindered by agglomeration of aluminum particles on its burning surface and determining the particle size has been a problem for half a century. The actual size of the agglomerates at motor pressures is unknown due to the opacity of the combustion plume, particularly at the elevated pressures seen in operational rocket motors. Sampling techniques can provide data at elevated pressure but may be biased due to the sampling method and do not provide information on the dynamics of agglomerate formation. Here this study uses time-resolved synchrotron x-ray imaging (with both absorption and phase contrast) to view aluminum agglomerate formation in situ at relevant rocket pressures. Finally, we have for the first time observed agglomerate formation at motor-relevant pressures in real time with unprecedented fidelity, providing critical data for understanding the combustion of aluminized solid rocket propellants.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Molecular Dynamics of Combustion Reactions in Supercritical Carbon Dioxide. Part 5: Computational Study of Ethane Dissociation and Recombination Reactions C 2 H 6 ⇌ CH 3 + CH 3

Fossil fuel oxy-combustion is an emerging technology where the habitual nitrogen diluent is replaced by high-pressure supercritical CO 2 (sCO 2 ), which increases the efficiency of energy conversion. In this study, the chemical kinetics of the combustion reaction C 2 H 6 ⇌ CH 3 + CH 3 in the sCO 2 environment is predicted at 30–1000 atm and 1000–2000 K. We adopt a multiscale approach, where the reactive complex is treated quantum mechanically in rigid rotor/harmonic oscillator approximation, while environment effects at different densities are taken into account by the potential of mean force, produced with classical molecular dynamics (MD). Here, we used boxed MD, where enhanced sampling of infrequent events of barrier crossing is accomplished without application of the bias potential. The multistate empirical valence bond model is applied to describe free radical formation accurately at the cost of the classical force field. Predicted rates at low densities agree well with the literature data. In conclusion, rate constants at 300 atm are 2.41 × 10 14 T –0.20 exp(–77.03 kcal/mol/RT) 1/s for ethane dissociation and 8.44 × 10 –19 T 1.42 exp(19.89 kcal/mol/RT) cm 3 /molecule/s for methyl–methyl recombination.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymer brushes are well-known for their exceptional resistance to unwanted protein adsorption. While experimental studies have extensively characterized protein–polymer brush interactions, computational investigations remain limited, largely due to the challenges in accurately modeling and integrating these complex, multicomponent systems without resorting to oversimplification. To address this challenge, this study presents one of the most comprehensive and realistic model systems to date, comprising the substrate, grafted polymers, and proteins. Our work interprets the interactions between polymer brush and protein based on realistic modeling without simplification. In particular, this study utilizes molecular modeling and simulation of polycationic and polyzwitterionic brushes─poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)─grafted onto α-quartz substrates via polymerization initiator linkers. The brush models were developed to closely replicate experimentally synthesized samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. Cα root-mean-square deviation and interaction pattern analyses further reveal that the PSBMA brush also induces the most significant destabilization of lysozyme, while the PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush, primarily due to electrostatic repulsion caused by a short carbon spacer length. Hydration analysis reveals that both the PSBMA brush and the lysozyme interacting with it exhibit the most rapid dehydration, attributed to the brush’s high local chain density, which results in the greatest lysozyme destabilization and the highest adsorption force. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics. In conclusion, this study provides valuable insights for the rational design of next-generation antifouling materials and offers a framework for realistic model development in complex multicomponent systems.

Adsorption↗
DOE OSTI2021

A near-infrared optical nanosensor for measuring aerobic respiration in microbial systems

We developed a ratiometric oxygen-sensitive nanosensor and demonstrated application in monitoring metabolic oxygen consumption in microbial samples over time. Based on a near-infrared (NIR) emitting oxygen-quenched luminophore, platinum(II) octaethylporphine ketone (PtOEPK), along with a stable dioctadecyl dicarbocyanine reference dye (DiD), this nanosensor system provides an advantageous approach for overcoming imaging issues in biological systems, such as autofluorescence and optical scattering in the visible wavelength region. The dyes are encapsulated within a polymer-based nanoparticle matrix to maintain them at a constant ratio in biological samples, precluding the need for complex synthetic approaches. With this constant ratio of the two dyes, the nanosensor response can be measured as a ratio of their two signals, accounting for nanosensor concentration artifacts in measurements. The nanosensors are reversible, which enabled us to temporally monitor systems in which dissolved oxygen concentrations both increase and decrease. These sensors were applied for the monitoring of oxygen in samples of Saccharomyces cerevisiae (brewing yeast) in a 96-well optical fluorescence plate reader format over 60 h. By mixing the nanosensors directly into the sample well with the yeast, we were able to dynamically track metabolic activity changes over time due to varying cell concentration and exposure to an antimicrobial agent. As a result, this system could be a potential platform for high-throughput screening of various species or variants of microbes with unknown metabolic rates in response to external stimuli (antimicrobials, metabolites, etc.).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Intracellular performance of tailored nanoparticle tracers in magnetic particle imaging

Magnetic Particle Imaging (MPI) is a quantitative mass-sensitive, tracer-based imaging technique, with potential applications in various cellular imaging applications. The spatial resolution of MPI, in the first approximation, improves by decreasing the full width at half maximum (FWHM) of the field-derivative of the magnetization, dm/dH of the nanoparticle (NP) tracers. The FWHM of dm/dH depends critically on NPs’ size, size distribution, and their environment. However, there is limited information on the MPI performance of the NPs after their internalization into cells. In this work, 30 to 150 μg of the iron oxide NPs were incubated in a lysosome-like acidic buffer (0.2 ml, 20 mM citric acid, pH 4.7) and investigated by vibrating sample magnetometry, magnetic particle spectroscopy, transmission electron microscopy, and dynamic light scattering (DLS). The FWHM of the dm/dH curves of the NPs increased with incubation time and buffer to NPs ratio, consistent with a decrease in the median core size of the NPs from ∼20.1 ± 0.98 to ∼18.5 ± 3.15 nm. Further, these smaller degraded NPs formed aggregates that responded to the applied field by hysteretic reversal at higher field values and increased the FWHM. The rate of core size decrease and aggregation were inversely proportional to the concentration of the incubated NPs, due to their slower biodegradation kinetics. The results of this model experiment show that the MPI performance of the NPs in the acidic environments of the intracellular organelles (i.e., lysosomes and endosomes) can be highly dependent on their rate of internalization, residence time, and degradation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Phase-resolved two-dimensional terahertz spectroscopy including off-resonant interactions beyond the χ{sup (3)} limit

We present the first two-dimensional (2D) terahertz (THz) experiment with three phase-locked THz pulses and a fully phase-resolved detection of the nonlinearly emitted field by electrooptic sampling. In a prototype experiment we study the ultrafast dynamics of nonlinear two-phonon and two-photon interband coherences in the narrow-gap semiconductor InSb. Due to the extraordinarily large optical interband dipole of InSb the experiments were performed in the strongly nonperturbative regime of light-matter interaction allowing for impulsive off-resonant excitation of both two-phonon coherences and two-photon interband coherences, the ultrafast dynamics of which is experimentally observed as a function of the waiting time in the three-pulse 2D experiment. Our novel three-pulse 2D THz spectroscopy paves the way for the detailed investigation of nonlinear quantum coherences in solids and holds potential for an extension to other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Acoustoelectric transport at gigahertz frequencies in coated epitaxial graphene

Epitaxial graphene (EG) produced from SiC surfaces by silicon sublimation is emerging as a material for electronic applications due to its good electronic properties and availability over large areas on a semiconducting substrate. In this contribution, we report on the transport of charge carriers in EG on SiC using high-frequency (>1 GHz) surface acoustic waves (SAWs). In our devices, the EG is coated with hydrogen-silsesquioxane, SiO{sub 2}, and a ZnO layer. This allows the efficient generation of SAWs and is compatible with the deposition of a metal top gate. Measurements of frequency- and time-resolved power scattering parameters confirm the generation and propagation of SAWs with frequencies of up to more than 7 GHz. Furthermore, the ZnO coating enhances the acoustoelectric currents by two orders of magnitude as compared to our previous uncoated samples. These results are an important step towards the dynamic acoustic control of charge carriers in graphene at gigahertz frequencies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Broadband ultrafast terahertz spectroscopy in the 25 T Split Florida-Helix

In this paper, we describe the development of a broadband (0.3–10 THz) optical pump-terahertz probe spectrometer with an unprecedented combination of temporal resolution (≤200 fs) operating in external magnetic fields as high as 25 T using the new Split Florida-Helix magnet system. Finally, using this new instrument, we measure the transient dynamics in a gallium arsenide four-quantum well sample after photoexcitation at 800 nm.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Rapid diagnosis of disease states using less invasive, safer, and more clinically acceptable approaches than presently employed is an imperative goal for the field of medicine. While mass spectrometry (MS)-based proteomics approaches have attempted to meet these objectives, challenges such as the enormous dynamic range of protein concentrations in clinically relevant biofluid samples coupled with the need to address human biodiversity have slowed their employment. Herein, we report on the use of a new platform that addresses these challenges by coupling technical advances in rapid gas phase multiplexed ion mobility spectrometry (IMS) separations [1, 2] with liquid chromatography (LC) and MS to dramatically increase measurement sensitivity and throughput, further enabling future MS-based clinical applications. An initial application of the LC-IMS-MS platform for the analysis of blood serum samples from stratified post-liver transplant patients with recurrent fibrosis progression illustrates its potential utility for disease characterization and use in personalized medicine [3, 4].

Environmental Molecular Sciences Laboratory↗