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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Strongly Correlated Ion Dynamics in Plastic Ionic Crystals and Polymerized Ionic Liquids

Understanding the mechanisms controlling ionic conductivity is critical for the development of the next generation of batteries and supercapacitors. This paper discusses the significant role played by ionic correlations in conductivity of concentrated ionic systems. Our studies of an organic ionic plastic crystal reveal that correlations in ions dynamics suppress conductivity by 25–100 times in comparison to the expected uncorrelated ionic conductivity estimated from the Nernst–Einstein relationship. Furthermore, additional analysis also demonstrates that ionic correlations suppress conductivity in polymerized ionic liquids and gel by ~10 times. Thus, ionic correlations, usually neglected in many studies, play a very important role in conductivity of concentrated ionic systems. These results cannot be explained by a diffusion of ion pairs because all these systems are essentially single ion conductors. In contrast, strongly correlated motions of mobile ions with the same charge (cation–cation or anion–anion correlations) are the major mechanism suppressing the ionic conductivity in these systems. Finally, on the basis of these results, we emphasize that charge transport rather than ion diffusion is critical for electrolyte performance and suggest the potential design of plastic crystals and polymer electrolytes with enhanced ionic conductivity.

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

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Black and gray box learning of amplitude equations: Application to phase field systems

Here, we present a data-driven approach to learning surrogate models for amplitude equations and illustrate its application to interfacial dynamics of phase field systems. In particular, we demonstrate learning effective partial differential equations describing the evolution of phase field interfaces from full phase field data. We illustrate this on a model phase field system, where analytical approximate equations for the dynamics of the phase field interface (a higher-order eikonal equation and its approximation, the Kardar-Parisi-Zhang equation) are known. For this system, we discuss data-driven approaches for the identification of equations that accurately describe the front interface dynamics. When the analytical approximate models mentioned above become inaccurate, as we move beyond the region of validity of the underlying assumptions, the data-driven equations outperform them. In these regimes, going beyond black box identification, we explore different approaches to learning data-driven corrections to the analytically approximate models, leading to effective gray box partial differential equations.

42 ENGINEERING↗
DOE OSTI2020

Test Plan to Investigate the Optimal Cure Schedule of an Epoxy Resin for Its Application as an Adhesive in Optocouplers

Epoxy resins are polymeric materials that are known for their versatility, low cost, and strong thermal and mechanical properties. They can be used as coatings, adhesives, automotive primers, and have become staples in electronics, aerospace composites and semiconductor encapsulates. Recently, LANL has experienced a decline in the quality of a specialized electronic device, called an optocoupler, which is normally acquired from commercial suppliers. Because of this, a group at LANL is exploring the feasibility of in-house manufacturing of an optocoupler that is better suited for their application. The current prototype has resulted in adhesive failure after being exposed to several thermal cycles. There is a need to investigate the possible reasons for this failure, particularly how the cure schedule of the epoxy might affect its adhesive strength over time and in cycled temperatures. To assist in this investigation, a test plan has been developed involving multiple experiments using thermomechanical analysis (TMA), dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Driving Force Dependent Photoinduced Charge Transfer Dynamics in Polymer-Wrapped Semiconducting Single-Walled Carbon Nanotubes

Here, we investigate the thermodynamic driving-force dependences of photoinduced charge separation (CS) and subsequent charge transfer dynamics in single-walled carbon nanotube (SWNT)–perylenediimide (PDI) donor–acceptor (D–A) superstructures. Pump–probe spectroscopy reveals that [SWNT (•+)n ]-(PDI –• ) n CS states form on an ∼100 fs time scale following photoexcitation; these dynamics are invariant across an ∼400 mV driving force range, indicating that SWNT hole polaron formation time scales are determined by nanotube lattice and solvent relaxation. These CS states feature SWNT hole polarons adjacent to (geminate) and nearby (nongeminate) PDI radical anions. Analysis of the free energy dependence for charge recombination (CR) of [SWNT •+ ] geminate -(PDI –• ) CS states highlights an ∼2 meV value for D–A electronic coupling (HAB) and ∼0.93 eV for the total reorganization energy (λ T ). A corresponding driving force dependence of the CR dynamics for [SWNT •+ ] nongeminate -(PDI –• ) CS states indicates a diminished H AB value (∼0.6 meV) and a larger λ T (∼1.1 eV), consistent with larger transfer distances. SWNT excitons that persist following photoinduced CS drive photooxidation of PDI –• components of [SWNT (•+)n ]-(PDI –• ) n CS states ( 1 SWNT* + PDI •– → PDI + SWNT •– ); this reaction manifests a significantly reduced λ T value (∼0.67 eV) as the initially prepared SWNT reduced state bears the character of a conduction band injected electron ([SWNT •– ] CB ). This reaction thus gives rise to relaxed, nongeminate SWNT electron and hole polarons on the same nanotube; these polarons react on a 10 2 ps time scale independent of the electronic structure of these SWNT-PDI superstructures.

anions↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2022

Accurate quantification of lattice temperature dynamics from ultrafast electron diffraction of single-crystal films using dynamical scattering simulations

In ultrafast electron diffraction (UED) experiments, accurate retrieval of time-resolved structural parameters, such as atomic coordinates and thermal displacement parameters, requires an accurate scattering model. Unfortunately, kinematical models are often inaccurate even for relativistic electron probes, especially for dense, oriented single crystals where strong channeling and multiple scattering effects are present. This article introduces and demonstrates dynamical scattering models tailored for quantitative analysis of UED experiments performed on single-crystal films. As a case study, we examine ultrafast laser heating of single-crystal gold films. Comparison of kinematical and dynamical models reveals the strong effects of dynamical scattering within nm-scale films and their dependence on sample topography and probe kinetic energy. Applying to UED experiments on an 11 nm thick film using 750 keV electron probe pulses, the dynamical models provide a tenfold improvement over a comparable kinematical model in matching the measured UED patterns. Also, the retrieved lattice temperature rise is in very good agreement with predictions based on previously measured optical constants of gold, whereas fitting the Debye–Waller factor retrieves values that are more than three times lower. Altogether, these results show the importance of a dynamical scattering theory for quantitative analysis of UED and demonstrate models that can be practically applied to single-crystal materials and heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Umbilical Fatigue Analysis for a Wave Energy Converter

Unique umbilical designs for wave energy converters (WECs), including the ability to handle significantly larger motions and loads over long deployments, are often required when conventional marine umbilical designs for offshore oil and gas and offshore wind may not meet the design and cost needs of wave energy technologies. This study details a fatigue analysis of a dynamic power umbilical attached to a two-body floating point absorber WEC system, using the sea states provided for the PacWave testing facilities. The 6 degrees of freedom motion time history for the WEC was simulated, and the motions of the attachment point for the umbilical on the WEC and respective sea states were used to analyze the dynamic motions and fatigue of the connected power umbilical to predict the fatigue life. The results show that the fatigue damage observed is more significant in shallow water, and extensive fatigue damage may occur because of the larger curvature response of the umbilical. The umbilical configurations departing at 90 deg off incoming waves were found to have the highest fatigue life attributed to less extension or compression of the umbilical. However, additional bend stiffener/limiter features may need to be incorporated into the buoyancy section and touchdown regions to minimize curvature-induced fatigue.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2026

Spectrometer-free quantitative vapor sensing and classification via spatiotemporal imaging of porous silicon metasurfaces

Metasurfaces offer a compact platform for optical vapor sensing, but their practical deployment has been limited by weak evanescent light–matter interactions and reliance on spectrally resolved instrumentation. Here, we report porous silicon (pSi) metasurfaces for spectrometer-free quantitative detection of volatile organic compounds (VOCs) with strongly enhanced light–matter interaction. The engineered porosity increases sensitivity by >100× relative to non-porous dielectric metasurfaces, enabling limits of detection of 1.65 ppm for methanol and 9.1 ppm for ethanol across a broad dynamic range (<10 ppm to >103 ppm). Imaging-based readout provides a lightweight, spectrometer-free pathway for real-time quantitative sensing. Beyond quantitative detection, the mesoporous architecture introduces adsorption–desorption kinetics as an additional information channel. Analysis of the resulting spatiotemporal signatures enables kinetic fingerprinting without reliance on infrared spectral features or surface functionalization, and a lightweight machine-learning classifier differentiates acetone, methanol, ethanol, and isopropanol with 91.6% accuracy. These results establish porous metasurfaces as spatiotemporal sensing elements that couple quantitative vapor detection with kinetic fingerprinting through real-time dynamical responses, enabling low-cost, high-performance optical sensors.

Dash, Tomoshree [Clemson University]↗
DOE OSTI2020

PredCom: A Predictive Approach to Collecting Approximated Communication Traces

Communication traces collected from MPI applications are an important source of information for performance optimization as they can help analysts determine communication patterns and identify inefficiencies. However, their collection, especially at scale, is time consuming, since it usually requires running the complete target application on a large number of nodes. In this work, we present PredCom, a tool-chain to generate a predictive communication proxy based on information gathered from a few small scale runs, which allows us to extract approximate communication traces with an accuracy high enough for most analysis goals. For this, we combine LLVM passes on the original source code (to capture static program structure) with parameter prediction (to capture dynamic and scaling behavior). This approach drastically reduces the time needed for collecting the communication traces, even for traces on large numbers of MPI processes. Here, we demonstrate that PredCom generates communication traces of various applications up to 1612x faster with an accuracy loss of 0.11 on average compared to the original large-scale traces, and we show that the generated traces can be used to optimize process placement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Inference of the neutron down-scatter ratio using neutron time-of-flight data for DT-layered implosions on the OMEGA laser

First analysis of the neutron time of flight (nTOF) data is presented to infer the areal density using the neutron down-scatter ratio (DSR) for OMEGA deuterium–tritium (DT)-layered (cryogenic) implosions. The required very-high-dynamic range (nTOF) signal is constructed using an nTOF detector [Forrest et al., Rev. Sci. Ins. 83, 10D919 (2012)] with multiple time-gated photomultiplier tubes. The nTOF data are analyzed using a forward-fit technique to account for the detector responses and infer the DSR and areal density for DT-layered implosions. The areal densities inferred using this analysis are found to be in agreement with the areal densities measured using the magnetic recoil spectrometer detector that is located in a similar line-of-sight. This work establishes the feasibility of the DSR measurement using nTOF data on OMEGA and provides an additional areal-density measurement that will benefit the assessment of the implosion performance and 3D reconstruction of the imploded core.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

On the discretization error of the discrete generalized quantum master equation

The transfer tensor method (TTM) [Cerrillo and Cao, Phys. Rev. Lett. 112 , 110401 (2014)] can be considered a discrete-time formulation of the Nakajima–Zwanzig quantum master equation (NZ-QME) for modeling non-Markovian quantum dynamics. A recent paper [Makri, J. Chem. Theory Comput. 21 , 5037 (2025)] raised concerns regarding the consistency of the TTM discretization, particularly a spurious term at the initial time t = 0. Here, this work presents a detailed analysis of the discretization structure of the TTM, clarifying the origin of the initial-time correction and establishing a consistent relationship between the TTM discrete-time memory kernel K N and the continuous-time NZ-QME kernel $\mathscr{K}$( N Δ t ). This relationship is validated numerically using the spin-boson model, demonstrating convergence of reconstructed memory kernels and accurate dynamical evolution as Δ t → 0. While the TTM provides a consistent discretization, we note that alternative schemes are also viable, such as the midpoint derivative/midpoint integral scheme proposed in Makri’s work. The relative performance of various schemes for either computing accurate $\mathscr{K}$( N Δ t ) from exact dynamics or obtaining accurate dynamics from exact $\mathscr{K}$( N Δ t ) warrants further investigation.

Density-matrix↗
DOE OSTI2021

A rapid heating and high magnetic field thermal analysis technique

Abstract A new thermal analysis technique is described that allows measurements to be performed on bulk samples at extreme heating and cooling rates and in high magnetic fields. High heating rates, up to 1000 °C min −1 , are achieved through electromagnetic induction heating of a custom-built apparatus fitted with commercial thermal analysis heads and sensor. Rapid cooling rates, up to 100 °C min −1 , are enabled by gas quenching and the small thermal mass of the induction furnace. The custom apparatus is designed to fit inside a superconducting magnet capable of fields up to 9 Tesla. This study demonstrates that the instrument is capable of collecting accurate thermal analysis data in high magnetic fields and rapidly acquiring data for dynamic processes. While the full potential of the technique is still unrealized, currently, it can provide insight into phenomena at time scales relevant to heat treatment in many industrial processes and into little understood effects of high magnetic field processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Unlocking the unfolded structure of ubiquitin: Combining time-resolved x-ray solution scattering and molecular dynamics to generate unfolded ensembles

The unfolding dynamics of ubiquitin were studied using a combination of x-ray solution scattering (XSS) and molecular dynamics (MD) simulations. The kinetic analysis of the XSS ubiquitin signals showed that the protein unfolds through a two-state process, independent of the presence of destabilizing salts. In order to characterize the ensemble of unfolded states in atomic detail, the experimental XSS results were used as a constraint in the MD simulations through the incorporation of x-ray scattering derived potential to drive the folded ubiquitin structure toward sampling unfolded states consistent with the XSS signals. We detail how biased MD simulations provide insight into unfolded states that are otherwise difficult to resolve and underscore how experimental XSS data can be combined with MD to efficiently sample structures away from the native state. Our results indicate that ubiquitin samples unfolded in states with a high degree of loss in secondary structure yet without a collapse to a molten globule or fully solvated extended chain. Finally, we propose how using biased-MD can significantly decrease the computational time and resources required to sample experimentally relevant nonequilibrium states.

Chemistry↗
DOE OSTI2023

End block dynamics in unentangled polymers by dielectric spectroscopy

Dielectric spectroscopy measures the dynamics of polymer melts over a broad frequency range. Developing a theory for the spectral shape can extend the analysis of dielectric spectra beyond determining relaxation times from the peak maxima and adds physical meaning to shape parameters determined with empirical fit functions. Toward this goal, we use the experimental results on unentangled poly(isoprene), and unentangled poly(butylene oxide), polymer melts, to test whether the concept of end blocks could be one reason for the Rouse model deviating from experimental data. These end blocks have been suggested by simulations and neutron spin echo spectroscopy and are a consequence of the monomeric friction coefficient depending on the position of the bead in the chain. The concept of an end block is an approximation which partitions the chain in a middle and two end blocks to avoid overparameterization by a continuous position dependent change of the friction parameter. Analysis of dielectric spectra shows that the deviations of the calculated from the experimental normal mode cannot be related to the end block relaxation. However, the results do not contradict an end block hiding below the segmental relaxation peak. Finally, it seems that the results are compatible with an end block being the specific part of the sub-Rouse chain interpretation close to the chain ends.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Porous Polymers as Universal Reversal Agents for Heparin Anticoagulants through an Inclusion–Sequestration Mechanism

Heparins are widely used anticoagulants for surgical procedures and extracorporeal therapies. However, all of them have bleeding risks. Protamine sulfate, the only clinically approved antidote for unfractionated heparin (UFH), has adverse effects. Moreover, protamine can only partially neutralize low-molecular-weight heparins (LMWHs) and is not effective for fondaparinux. In this report an inclusion–sequestration strategy for efficient neutralization of heparin anticoagulants by cationic porous supramolecular organic frameworks (SOFs) and porous organic polymers (POPs) is reported. Isothermal titration calorimetric and fluorescence experiments show strong binding affinities of these porous polymers toward heparins, whereas dynamic light scattering and zeta potential analysis confirm that the heparin sequences are adsorbed into the interior of the porous hosts. Activated partial thromboplastin time, anti-FXa, and thromboelastography assays indicate that their neutralization efficacies are higher than or as high as that of protamine for UFH and generally superior to protamine for LMWHs and fondaparinux, which is further confirmed by tail-transection model in mice and ex vivo aPTT or anti-FXa analysis in rats. Acute toxicity evaluations reveal that one of the SOFs displays outstanding biocompatibility. This work suggests that porous polymers can supply safe and rapid reversal of clinically used heparins, as protamine surrogates, providing an improved approach for their neutralization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hollow-structured Ni-N-C catalysts for highly selective CO 2 electroreduction

Atomically dispersed single-atom catalysts have emerged as promising non-precious catalyst alternatives to expensive Ag and Au catalysts for electrochemical CO 2 reduction reaction (CO 2 RR). In particular, nickel-nitrogen-carbon (Ni-N-C) catalysts have demonstrated a high faradaic efficiency (FE) toward CO formation at low overpotentials. Nonetheless, the exact nature of Ni active sites under CO 2 RR remains elusive and conventional Ni-N-C catalysts are limited by microporosity and low density of Ni single atoms, hindering performance in CO 2 electrolyzers. Here, we report the synthesis of hollow-structured Ni-N-C ( hs -Ni-N-C) catalysts via a post-synthesis modification (PSM) strategy using partial ligand exchange of 2-methylimidazole with 3-amino-1,2,4-triazole. This approach enables the formation of a hollow structure, resulting in more than a twofold increase in Ni atom density compared to regular Ni-N-C (r-Ni-N-C). In a zero-gap CO 2 electrolyzer, the optimized hs -Ni-N-C allows for achieving an FE CO of 97% at a current density of > 100 mA cm⁻ 2 , while maintaining high CO selectivity with stable performance over 100 h at 2.5 V. hs-Ni-N-C shows a more than sevenfold increase in the CO partial current density relative to r-Ni-N-C resulting from the combined effects of a higher density of Ni single-atom sites, improved kinetics, and lower transport resistance under the operating conditions, as indicated by electrochemical impedance spectra and distribution of relaxation times analysis. Operando high energy-resolution X-ray absorption spectroscopy (XAS) reveals that atop-bonded CO on Ni single sites induces dynamic transformations of the Ni–N coordination environment, leading to a symmetric coordination structure of hs -Ni-N-C. Under CO 2 RR, the catalysts undergo a more pronounced structural change and form a minor fraction of Ni nanoparticles. Density functional theory calculations are consistent with the XAS results and provide molecular insights showing that the interplay between protonation and CO adsorption leads to adsorbate-induced restructuring of the Ni single atom. This work demonstrates the synergistic role of hollow structure and high-density Ni atoms in governing CO 2 RR selectivity and provides mechanistic insights into the structural dynamics of single-atom catalysts under operating conditions.

36 MATERIALS SCIENCE↗