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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

time-resolved spectroscopy fit (trspecfit) v0.01

Analyze 2D time- and energy-resolved data, such as from a pump-probe spectroscopy experiment. User can select and input different peak shapes/ functions and background types to first fit a ground state/ unperturbed spectrum. This would be similar to how standard spectroscopy data is fit. Subsequently, to describe the time domain, users can choose functions that describe the temporal dynamics of one or more spectral features, such as a peak amplitude, peak position, etc. These time dynamics functions can be added or convoluted (e.g. describing an instrument response function) with each other. Functionality to integrate implicit variables leading to distributions of certain parameters/ functions is in development. Alternatively, 2D data can be analyzed one time step at a time to get an idea of the time dynamics of the system before deploying the global 2D fit described above. Typically people write custom software for this purpose. During my PhD I've seen five internal LBL and external researchers write one-off code in different languages to analyze time- and energy-resolved spectra. While this was specifically was for a laser pump - X-ray probe spectroscopy experiment, I'm trying to write a general package for the time-resolved spectroscopy community.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2021

Non-Adiabatic Excited State Molecular Dynamics Methodologies: comparison and convergence

Direct atomistic simulation of nonadiabatic molecular dynamics is a challenging goal that allows important insights into fundamental physical phenomena. A variety of frameworks, ranging from fully quantum treatment of nuclei to semiclassical and mixed quantum–classical approaches, were developed. These algorithms are then coupled to specific electronic structure techniques. Such diversity and lack of standardized implementation make it difficult to compare the performance of different methodologies when treating realistic systems. Here, we compare three popular methods for large chromophores: Ehrenfest, surface hopping, and multiconfigurational Ehrenfest with ab initio multiple cloning (MCE-AIMC). These approaches are implemented in the NEXMD software, which features a common computational chemistry model. The resulting comparisons reveal the method performance for population relaxation and coherent vibronic dynamics. Finally, we study the numerical convergence of MCE-AIMC algorithms by considering the number of trajectories, cloning thresholds, and Gaussian wavepacket width. Our results provide helpful reference data for selecting an optimal methodology for simulating excited-state molecular dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

pH‐Mediated Strong Metal‐Support Interaction Construction Through Dynamic Fermi Level Tuning

The metal–support interface is central to governing catalytic transformations. While strong metal–support interaction (SMSI) is an established strategy to tailor the morphology and electronic properties of supported metal catalysts, the role of interfacial charge redistribution in SMSI formation remains poorly understood and rarely leveraged. Here, in this study, we report a dual-stimuli approach that combines pH modulation with ultrasonication to mediate SMSI construction in aqueous solution through dynamic Fermi level tuning. By leveraging in situ pH-driven charge redistribution at the metal–support interface, we achieve controllable SMSI encapsulation of metal nanoparticles, as verified by electrochemical analysis, work function measurements, and x-ray-based techniques. The resulting catalysts exhibit tunable SMSI features and deliver enhanced activity and selectivity in hydrogenation reactions. This work establishes a facile strategy to modulate catalyst structure and electronic properties by exploiting Fermi level variation as a driving force, thereby advancing rational SMSI design and catalytic performance across diverse environments.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Localized Phonon Densities of States at Grain Boundaries in Silicon

Since it is now possible to record vibrational spectra at nanometer scales in the electron microscope, it is of interest to explore whether extended defects in crystals such as dislocations or grain boundaries will result in measurable changes of the phonon densities of states (dos) that are reflected in the spectra. Phonon densities of states were calculated for a set of high angle grain boundaries in silicon. The boundaries are modeled by supercells with up to 160 atoms, and the vibrational densities of states were calculated by taking the Fourier transform of the velocity–velocity autocorrelation function from molecular dynamics simulations with larger supercells doubled in all three directions. In selected cases, the results were checked on the original supercells by comparison with the densities of states obtained by diagonalizing the dynamical matrix calculated using density functional theory. Near the core of the grain boundary, the height of the optic phonon peak in the dos at 60 meV was suppressed relative to features due to acoustic phonons that are largely unchanged relative to their bulk values. This can be attributed to the variation in the strength of bonds in grain boundary core regions where there is a range of bond lengths.

Materials Science↗
DOE OSTI2024

In-Situ Species Concentration Measurements In Ammonia-Mix Flames Using Ftir Spectroscopy

Hydrogen and ammonia represent two carbon-free fuel sources that could be used in place of current fossil energy sources in combustion systems. To develop optimized ammonia combustion systems, validated modeling tools are needed. In the open literature, it has been shown that the complex chemistry associated with fuel-bound nitrogen contained in ammonia differs greatly from natural gas or hydrogen combustion. As a result, several new chemical kinetic mechanisms have been developed. Many of these mechanisms have been validated experimentally, however this has primarily focused on bulk parameters such as laminar flame speed and ignition delay time. Critically, high quality measurements of species concentrations are needed under controlled conditions which are easily represented by simple models. In this paper, direct, in-situ measurements of species concentrations and gas temperature are performed in a laminar flat-flame burner. This arrangement enables comparison with 1D model predictions, better isolating chemical kinetics from the fluid dynamics. Quantitative species concentrations are determined by absorption spectroscopy using an FTIR spectrometer. Fuel compositions representative of cracked ammonia (NH 3 /H 2 ) and ammonia-natural gas (NH 3 /CH 4 ) are considered for rich and lean equivalence ratios. A major focus of the paper is on the selection of spectral features for nitric oxide and ammonia and correcting for large amounts of baseline H 2 O absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Phase-based design of CO 2 capture, transport, and storage infrastructure via SimCCS 3.0

The design of optimal infrastructure is essential for the deployment of commercial and large-scale carbon capture and storage (CCS) technology. During the design process, it is important to consider CO 2 capture and storage locations and CO 2 transportation pipelines to minimize the total project cost. SimCCS, first introduced in 2009, is an integrated open-source tool to optimize CCS infrastructure. The core CCS infrastructure design problem in SimCCS is structured as a mixed-integer linear programming problem by selecting the optimal pipeline routes, searching CO 2 source capture and storage locations, and determining the corresponding CO 2 amounts to meet desired capture targets. Multiple important and practical features have been developed to the latest version of SimCCS, SimCCS 3.0 . One of these features is phase-based modeling which enables users to dynamically design the CCS infrastructure. We demonstrate the phased-based modeling capability using two CCS infrastructure optimization case studies. The results from these case studies reveal that the phase-based modeling capability in SimCCS is particularly useful to optimize the dynamic deployment of CCS projects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Ten years of spasers and plasmonic nanolasers

Ten years ago, three teams experimentally demonstrated the first spasers, or plasmonic nanolasers, after the spaser concept was first proposed theoretically in 2003. An overview of the significant progress achieved over the last 10 years is presented here, together with the original context of and motivations for this research. After a general introduction, we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers. This is followed by an overview of crucial technological progress, including lasing threshold reduction, dynamic modulation, room-temperature operation, electrical injection, the control and improvement of spasers, the array operation of spasers, and selected applications of single-particle spasers. Research prospects are presented in relation to several directions of development, including further miniaturization, the relationship with Bose–Einstein condensation, novel spaser-based interconnects, and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Predicting CaO-(MgO)-Al2O3-SiO2 glass reactivity in alkaline environments from force field molecular dynamics simulations

In this investigation, force field-based molecular dynamics (MD) simulations have been employed to generate detailed structural representations for a range of amorphous quaternary CaO-MgO-Al{sub 2}O{sub 3}-SiO{sub 2} (CMAS) and ternary CaO-Al{sub 2}O{sub 3}-SiO{sub 2} (CAS) glasses. Comparison of the simulation results with select experimental X-ray and neutron total scattering and literature data reveals that the MD-generated structures have captured the key structural features of these CMAS and CAS glasses. Based on the MD-generated structural representations, we have developed two structural descriptors, specifically (i) average metal oxide dissociation energy (AMODE) and (ii) average self-diffusion coefficient (ASDC) of all the atoms at melting. Both structural descriptors are seen to more accurately predict the relative glass reactivity than the commonly used degree of depolymerization parameter, especially for the eight synthetic CAS glasses that span a wide compositional range. Hence these descriptors hold great promise for predicting CMAS and CAS glass reactivity in alkaline environments from compositional information.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deciphering the structure of Arabidopsis thaliana 5-enol-pyruvyl-shikimate-3-phosphate synthase: An essential step toward the discovery of novel inhibitors to supersede glyphosate

Glyphosate interferes with plant aromatic metabolism through the inhibition of 5-enol-pyruvyl-shikimate-3-phosphate (EPSP) synthase [EPSPS, EC 2.5.1.19]. For this reason, EPSPS has been extensively studied in a vast array of organisms. This notwithstanding, up to date, the crystal structure of the protein has been solved exclusively in a few prokaryotes, while that of the plant enzyme has been only deduced in silico by similarity. This study aimed at determining the structure of EPSPS from the plant model species Arabidopsis thaliana, which has been cloned, heterologously expressed and affinity-purified. The kinetic properties of the enzyme have been determined, as well as its susceptibility to the inhibition brought about by glyphosate. The crystal structure of the protein has been resolved at high resolution (1.4 Å), showing open conformation of the enzyme, which is the state ready for substrate/inhibitor binding. This provides a framework for the structure-based design of novel EPSPS inhibitors. Surface regions near the active-site cleft entrance or at the interdomain hinge appear promising for inhibitor selectivity, while bound chloride near the active site is a potential placeholder for anionic moieties of future herbicides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Pyoverdine binding aptamers and label-free electrochemical detection of pseudomonads

Pyoverdines are iron-chelating siderophores employed by various pseudomonads to promote their growth in iron-limited environments, facilitating both beneficial and detrimental interactions with co-inhabiting microbes or hosts, including plants and animals. The fluorescent pseudomonads produce fluorescent pyoverdines comprised of a conserved central chromophore and a unique strain-specific peptidic side chain produced by non-ribosomal peptide synthetases. Pyoverdine Pf5 (PVD-Pf5) is produced by Pseudomonas protegens Pf-5, a species known for supporting plant growth and its involvement in plant pathogen control. To develop a means of exploring the dynamics of P. protegens activity in soil and in the rhizosphere, we selected DNA aptamers that specifically recognize PVD-Pf5 with high affinities. Two selected aptamers with only 16% identity in sequence were examined for structure and function. We found evidence that both aptamers form structures in their apo-forms and one aptamer has structural features suggesting the presence of a G-quadruplex. Although their tertiary structures are predicted to be different, both aptamers bind the target PVD-Pf5 with similar affinities and do not bind other siderophores, including the related pyoverdine, pseudobactin, produced by Pseudomonas sp. B10. One aptamer binds the pyoverdine peptide component and may also interact with the chromophore. This aptamer was integrated into a nanoporous aluminum oxide biosensor and demonstrated to successfully detect PVD-Pf5 and not to detect other siderophores that do not bind to the aptamer when evaluated in solution. This sensor provides a future opportunity to track the locations of P. protegens around plant roots and to monitor PVD-Pf5 production and movement through the soil.

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

Solid-State, Single-Ion Conducting, Polymer Blend Electrolytes with Enhanced Li + Conduction, Electrochemical Stability, and Limiting Current Density

The development of solid-state polymer electrolytes with high lithium conductivity is crucial to improve lithium-ion battery performance and ameliorate the safety challenges associated with current solvent-based electrolytes. Unfortunately, sluggish polymer segmental dynamics are known to constrain conductivity enhancements in solid-state polymer electrolyte systems, limiting overall performance. In this work, a glassy single-ion-conducting polymer, poly[lithium sulfonyl(trifluoromethane sulfonyl)imide methacrylate] (PLiMTFSI), was blended with a flexible polymer, poly(oligo-oxyethylene methyl ether methacrylate) (POEM), and the impact of PLiMTFSI molecular weight and ion concentration on the thermal and ion-conducting behavior of blend electrolytes was investigated. High ionic conductivities approaching 1 × 10 -2 S/cm at 150 °C were realized in this polymer blend electrolyte system as a result of decoupling Li + transport from polymer segmental dynamics. The decoupled ion transport was attributed to the packing frustration of the glassy PLiMTFSI – sufficient percolating free volume was generated to produce effective ion diffusion pathways. This decoupling was tunable as the ion transport could be altered from being closely coupled to the polymer segmental dynamics (Vogel–Tammann–Fulcher-like) to hopping (Arrhenius-like) by increasing the PLiMTFSI molecular weight and ion concentration. Moreover, the immobilized TFSI anion resulted in high Li + selectivity (Li + transference number = 0.9), high electrochemical stability (up to 4.7 V against Li + / Li), and limiting current density of 1.8 mA/cm 2 (electrolyte thickness = 0.05 cm). These features suggest that this single-ion-conducting, polymer blend electrolyte might be a promising alternative to a benchmark system – salt-doped polyethylene oxide. Moreover, the above characteristics can support the battery operation at higher voltages using energy-dense Li metal anodes, with faster charging rates and enhanced energy/power densities. Altogether, the results suggest that polymer chain packing frustration can be exploited to overcome the constraints of slow polymer segmental relaxations to achieve rapid and highly selective ion transport and enhanced performance in solid-state polymer electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Compartmentalization and Photo-Regulating Pathways for Incompatible Tandem Catalysis

This contribution describes an advanced compartmentalized micellar nanoreactor that possesses a reversible photoresponsive feature and its application towards photo-regulating reaction pathways for incompatible tandem catalysis under aqueous conditions. The smart nanoreactor is based on multi-functional amphiphilic poly(2-oxazoline)s and covalently crosslinked with spiropyran upon micelle formation in water. It responds to light irradiation in a wavelength-selective manner switching its morphology as confirmed by dynamic light scattering and cryo-transition electron microscopy. The compartmental structure renders distinct nanoconfinements for two incompatible enantioselective transformations: a rhodium-diene complex-catalyzed asymmetric 1,4-addition occurs in the hydrophilic corona while a Rh-TsDPEN-catalyzed asymmetric transfer hydrogenation proceeds in the hydrophobic core. Control experiments and kinetic studies showed that the gated behavior induced by the photo-triggered reversible spiropyran to merocyanine transition in the cross-linking layer is key to discriminate among substrates/reagents during the catalysis. Here, the smart nanoreactor realized photo-regulation to direct the reaction pathway to give a multi-chiral product with high conversions and perfect enantioselectivities in aqueous media. Our SCM catalytic system, on a basic level, mimics the concepts of compartmentalization and responsiveness Nature uses to coordinate thousands of incompatible chemical transformations into streamlined metabolic processes.

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

Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes

A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Loève (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator. Such decomposed kernels have the potential to be very fast, and do not depend on the selection of a reduced set of inducing points. However KL decompositions lead to high dimensionality, and variable selection thus becomes paramount. This paper reports a new method of forward variable selection, enabled by the ordered nature of the basis functions in the KL expansion of the Bayesian Smoothing Spline ANOVA kernel (BSS-ANOVA), coupled with fast Gibbs sampling in a fully Bayesian approach. It quickly and effectively limits the number of terms, yielding a method with competitive accuracies, training and inference times for tabular datasets of low feature set dimensionality. Theoretical computational complexities are O ( N P 2 ) in training and O ( P ) per point in inference, where N is the number of instances and P the number of expansion terms. The inference speed and accuracy makes the method especially useful for dynamic systems identification, by modeling the dynamics in the tangent space as a static problem, then integrating the learned dynamics using a high-order scheme. The methods are demonstrated on two dynamic datasets: a ‘Susceptible, Infected, Recovered’ (SIR) toy problem, along with the experimental ‘Cascaded Tanks’ benchmark dataset. Comparisons on the static prediction of time derivatives are made with a random forest (RF), a residual neural network (ResNet), and the Orthogonal Additive Kernel (OAK) inducing points scalable GP, while for the timeseries prediction comparisons are made with LSTM and GRU recurrent neural networks (RNNs) along with the SINDy package.

Hayes, Kyle↗
DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗
DOE OSTI2023

Experimental results from the characterization of a 32-channels mixed-signal processor for the GAPS experiment

This work describes the experimental results obtained from the characterization of a 32-channels readout ASIC that is the core of a module composed by 4 Lithium-drifted Silicon, Si(Li), detectors. The module is the building block of a tracker in an upcoming balloon experiment. The activity is carried out within the General AntiParticle Spectrometer (GAPS) international collaboration, whose objective is the indirect detection of dark matter through the detection of antiparticles present in cosmic rays at low energies. The balloon flight is foreseen in 2024 from the McMurdo station in Antarctica. The ASIC is designed in a 180 nm CMOS technology and has 32 low-noise analog readout channels featuring a dynamic compression of the signal, an 11-bit SAR ADC, and a digital back-end section responsible for channel setting and for sending digital information to the data acquisition system of the tracker. This work has a twofold goal: it intends on the one hand to verify the performance of the circuit, on the other to select at least 300 good samples to be used for the assembly of the GAPS Si(Li) tracker.

Riceputi, Elisa↗
DOE OSTI2024

Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction

Traditional low-rank approximation is a powerful tool for compressing large data matrices that arise in simulations of partial differential equations (PDEs), but suffers from high computational cost and requires several passes over the PDE data. The compressed data may also lack interpretability thus making it difficult to identify feature patterns from the original data. Here, to address these issues, we present an online randomized algorithm to compute the interpolative decomposition (ID) of large-scale data matrices in situ. Compared to previous randomized IDs that used the QR decomposition to determine the column basis, we adopt a streaming ridge leverage score-based column subset selection algorithm that dynamically selects proper basis columns from the data and thus avoids an extra pass over the data to compute the coefficient matrix of the ID. In particular, we adopt a single-pass error estimator based on the non-adaptive Hutch++ algorithm to provide real-time error approximation for determining the best coefficients. As a result, our approach only needs a single pass over the original data and thus is suitable for large and high-dimensional matrices stored outside of core memory or generated in PDE simulations. A strategy to improve the accuracy of the reconstructed data gradient, when desired, within the ID framework is also presented. We provide numerical experiments on turbulent channel flow and ignition simulations, and on the NSTX Gas Puff Image dataset, comparing our algorithm with the offline ID algorithm to demonstrate its utility in real-world applications.

Column subset selection↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗