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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structural characterization of tin in toothpaste by dynamic nuclear polarization enhanced 119 Sn solid-state NMR spectroscopy

Stannous fluoride (SnF 2 ) is an effective fluoride source and antimicrobial agent that is widely used in commercial toothpaste formulations. The antimicrobial activity of SnF 2 is partly attributed to the presence of Sn(II) ions. However, it is challenging to directly determine the Sn speciation and oxidation state within commercially available toothpaste products due to the low weight loading of SnF 2 (0.454 wt% SnF 2 , 0.34 wt% Sn) and the amorphous, semi-solid nature of the toothpaste. Here, we show that dynamic nuclear polarization (DNP) enables 119 Sn solid-state NMR experiments that can probe the Sn speciation within commercially available toothpaste. Solid-state NMR experiments on SnF 2 and SnF 4 show that 19 F isotropic chemical shift and 119 Sn chemical shift anisotropy (CSA) are highly sensitive to the Sn oxidation state. DNP-enhanced 119 Sn magic-angle turning (MAT) 2D NMR spectra of toothpastes resolve Sn(II) and Sn(IV) by their 119 Sn chemical shift tensor parameters. Fits of DNP-enhanced 1D 1 H → 119 Sn solid-state NMR spectra allow the populations of Sn(II) and Sn(IV) within the toothpastes to be estimated. This analysis reveals that three of the four commercially available toothpastes contained at least 80% Sn(II), whereas one of the toothpaste contained a significantly higher amount of Sn(IV).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Dynamic Power Network State Estimation with Asynchronous Measurements: Preprint

The operation of distribution networks is becoming increasingly volatile, due to fast variations of renewables and, hence, net-loading conditions. To perform a reliable state estimation under these conditions, this paper considers the case where measurements from meters, phasor measurement units, and distributed energy resources are collected and processed in real time to produce estimates of the state at a fast time scale. Streams of measurements collected in real time and at heterogenous rates render the underlying processing asynchronous, and poses severe strains on workhorse state estimation algorithms. In this work, a real-time state estimation algorithm is proposed, where data are processed on the fly. Starting from a regularized least-squares model, and leveraging appropriate linear models, the proposed scheme boils down to a linear dynamical system where the state is updated based on the previous estimate and on the measurement gathered from a few available sensors. The estimation error is shown to be always bounded under mild condition. Numerical simulations are provided to corroborate the analytical findings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Modelling and analysis of nuclear reactor system coupled with a liquid metal battery

Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.

25 ENERGY STORAGE↗
DOE OSTI2021

Predicted success of prophylactic antiviral therapy to block or delay SARS-CoV-2 infection depends on the drug's mechanism of action

Repurposed drugs that are safe and immediately available constitute a first line of defense against new viral infections. Despite limited antiviral activity against SARS-CoV-2, several drugs are being tested as medication or as prophylaxis to prevent infection. Using a stochastic model of early phase infection, we evaluate the success of prophylactic treatment with different drug types to prevent viral infection. We find that there exists a critical efficacy that a treatment must reach in order to block viral establishment. Treatment by a combination of drugs reduces the critical efficacy, most effectively by the combination of a drug blocking viral entry into cells and a drug increasing viral clearance. Below the critical efficacy, the risk of infection can nonetheless be reduced. Drugs blocking viral entry into cells or enhancing viral clearance reduce the risk of infection more than drugs that reduce viral production in infected cells. The larger the initial inoculum of infectious virus, the less likely is prevention of an infection. In our model, we find that as long as the viral inoculum is smaller than 10 infectious virus particles, viral infection can be prevented almost certainly with drugs of 90% efficacy (or more). Even when a viral infection cannot be prevented, antivirals delay the time to detectable viral loads. The largest delay of viral infection is achieved by drugs reducing viral production in infected cells. A delay of virus infection flattens the within-host viral dynamic curve, possibly reducing transmission and symptom severity. Thus, antiviral prophylaxis, even with reduced efficacy, could be efficiently used to prevent or alleviate infection in people at high risk.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Insights into Sorption and Molecular Transport of Aqueous Glucose into Zeolite Nanopores

Liquid-phase heterogeneous catalysis using zeolites is important for biomass conversion to fuels and chemicals. There is a substantial body of work on gas-phase sorption in zeolites with different topologies; however, studies investigating the diffusion of complex molecules in liquid medium into zeolitic nanopores are scarce. Here, we present a molecular dynamics study to understand the sorption and diffusion of aqueous β-d-glucose into β-zeolite silicate at T = 395 K and P = 1 bar. Through 2-μs-long molecular dynamics trajectories, we reveal the role of the solvent, the kinetics of the pore filling, and the effect of the water model on these properties. We find that the glucose and water loading is a function of the initial glucose concentration. Although the glucose concentration increases monotonically with the initial glucose concentration, the water loading exhibits a nonmonotonic behavior. At the highest initial concentration (~20 wt %), we find that the equilibrium loading of glucose is approximately five molecules per unit cell and displays a weak dependence on the water model. Glucose molecules follow a single-file diffusion in the nanopores due to confinement. Here, the dynamics of glucose and water molecules slows significantly at the interface. The average residence time for glucose molecules is an order of magnitude larger than that in the bulk solution, while it is about twice as large for the water molecules. Our simulations reveal critical molecular details of the glucose molecule’s local environment inside the zeolite pore relevant to catalytic conversion of biomass to valuable chemicals.

09 BIOMASS FUELS↗
DOE OSTI2026

Ionic-content-driven restructuring of spirobisindane ionene networks: implications for mechanics, self-healing, and gas transport

Polymers of intrinsic microporosity (PIMs) offer exceptional gas permeability but remain brittle and susceptible to physical aging, limiting their durability in separation applications. Here, we introduce a reconfigurable microporous polymer network that uniquely integrates permanent PIM microporosity with autonomous, intrinsic self-healing driven by imidazolium-based ionic motifs. Spirobisindane units generate the intrinsic free-volume architecture, while an imidazolium-containing polyamide ionene supplies dynamic ionic and hydrogen-bonding interactions that reorganize under mild activation. Incorporation of imidazolium-based ionic liquids further tunes cohesion, mobility, and densification, enabling the network to relax, re-associate, and retain microporosity without structural collapse. Through a comprehensive multiscale approach combining spectroscopy, scattering, thermal and mechanical characterization with all-atom molecular dynamics and density functional theory calculations, we elucidate how ionic content, as a single control parameter that reshapes free-volume distributions, modulates local coordination environments, and governs relaxation and healing kinetics. At intermediate ionic loadings, the networks achieve rapid, repeatable self-healing while maintaining CO$_2$ selectivity, demonstrating an optimal balance between segmental mobility and structural integrity. By establishing how hierarchical ionic interactions couple structure, dynamics, and transport in microporous ionene networks, this work provides generalizable design rules for adaptive soft-matter systems that require simultaneous mechanical resilience, reconfigurability, and selective gas transport.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of local structure and stoichiometry on the dynamic behavior of bi-metal interfaces

Molecular dynamics (MD) simulations are performed to investigate the failure behavior of Al/Al, Al/Fe, and Al/AlFe bi-layers under high strain-rate shock loading. Additional simulations are performed to invesigate solely the tensile response of these systems and to isolate the effects of loading history on damage and failure. Specifically, the compression stage of the shock is excluded from the tensile simulations. This study shows that local structure variation, including the introduction of serrations, plays different roles in controlling damage nucleation depending on the specific loading condition. Under shock loading, the stress for void nucleation and the resulting void distribution is insensitive to an interface structure, whereas under pure tensile loading, the opposite is true, even though the strain rate is comparable. The above difference can be explained based on the corresponding change in the total deformation prior to damage nucleation under shock loading, which is missing from the pure tensile loading. Therefore, it is concluded that whether local interface structure matters under high strain-rate loading depends on the specific loading history and the stress state: the local structure variation matters only when it alters the deformation behavior, and subsequently, damage nucleation and evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dataset of simulated vibrational density of states and X-ray diffraction profiles of mechanically deformed and disordered atomic structures in Gold, Iron, Magnesium, and Silicon

This dataset is comprised of a library of atomistic structure files and corresponding X-ray diffraction (XRD) profiles and vibrational density of states (VDoS) profiles for bulk single crystal silicon (Si), gold (Au), magnesium (Mg), and iron (Fe) with and without disorder introduced into the atomic structure and with and without mechanical loading. Included with the atomistic structure files are descriptor files that measure the stress state, phase fractions, and dislocation content of the microstructures. All data was generated via molecular dynamics or molecular statics simulations using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. This dataset can inform the understanding of how local or global changes to a materials microstructure can alter their spectroscopic and diffraction behavior across a variety of initial structure types (cubic diamond, face-centered cubic (FCC), hexagonal close-packed (HCP), and body-centered cubic (BCC) for Si, Au, Mg, and Fe, respectively) and overlapping changes to the microstructure (i.e., both disorder insertion and mechanical loading).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

A data-driven linear formulation of the optimal demand response scheduling problem for an industrial air separation unit

Demand response (DR) has become a key element in balancing the power grid as the contribution of time-varying renewable power generation increases. Chemical plants are appealing candidates for DR programs as they offer large, concentrated and flexible loads. DR participation calls for frequent production rate changes over time scales that overlap with the dominant dynamics of the plant. Production scheduling should therefore consider the process dynamics explicitly. Here we present a data-driven approach for modelling the scheduling-relevant dynamics based on historical closed-loop operating data using autoregressive with extra inputs (ARX) models. We introduce a new, linear scheduling problem formulation based on the ARX representation, and demonstrate its implementation on an industrial air separation unit.

42 ENGINEERING↗
DOE OSTI2022

Differentiation of static and dynamic interfacial area in the structured packed column

Effective mass-transfer area plays a key role for post-combustion carbon capture. The static area, an inactive part relative to mass transfer, needs to be differentiated in the interfacial area. In the present study, CFD method is employed to investigate static and dynamic interfacial areas in Mellapak 500.Y structured packing. Here, a wide range of physical properties and loading conditions are included to understand their effects on both areas. The result shows that the relationship between interfacial area and interfacial velocity at the local scale can be described by gamma distribution with good accuracy. The influences of various physical properties derived from simulation are compared with existing experiment-based correlations for both static and interfacial areas. The result shows the dominant influence of viscosity on the static area, and the most importance of the influence of contact angle on the interfacial area. This study also highlights the importance of viscosity, which is usually ignored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture

Meshfree discretizations of state-based peridynamic models are attractive due to their ability to naturally describe fracture of general materials. However, two factors conspire to prevent meshfree discretizations of state-based peridynamics from converging to corresponding local solutions as resolution is increased: quadrature error prevents an accurate prediction of bulk mechanics, and the lack of an explicit boundary representation presents challenges when applying traction loads. Herein, we develop a reformulation of the linear peridynamic solid (LPS) model to address these shortcomings, using improved meshfree quadrature, a reformulation of the nonlocal dilatation, and a consistent handling of the nonlocal traction condition to construct a model with rigorous accuracy guarantees. In particular, these improvements are designed to enforce discrete consistency in the presence of evolving fractures, whose a priori unknown location render consistent treatment difficult. In the absence of fracture, when a corresponding classical continuum mechanics model exists, our improvements provide asymptotically compatible convergence to corresponding local solutions, eliminating surface effects and issues with traction loading which have historically plagued peridynamic discretizations. When fracture occurs, our formulation automatically provides a sharp representation of the fracture surface by breaking bonds, avoiding the loss of mass. We provide rigorous error analysis and demonstrate convergence for a number of benchmarks, including manufactured solutions, free-surface, nonhomogeneous traction loading, and composite material problems. Finally, we validate simulations of brittle fracture against a recent experiment of dynamic crack branching in soda-lime glass, providing evidence that the scheme yields accurate predictions for practical engineering problems.

42 ENGINEERING↗
DOE OSTI2020

Comparison of continuum and cross-core theories of dynamic strain aging

Dynamic strain aging (DSA) is the process of solute atoms segregating around dislocations on the timescale of loading. Continuum theories of DSA derived from elasticity theory have been shown to severely overpredict both the timescale and strengthening of DSA. Recently, cross-core theory was developed to reconcile this gap, invoking a special single-atomic-hop diffusion mechanism across the core of an extended dislocation. In this work, we show that the classical continuum theory expression for the rate of solute segregation is in error. After correcting this error, we show that continuum theory predictions match cross-core theory when the elevated diffusivity near the dislocation core is accounted for. Finally, our findings indicate that continuum theory is still a useful tool for studying dislocation-solute interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A conceptual model for how to design for building envelope characteristics. Impact of thermal comfort intervals and thermal mass on commercial buildings in U.S. climates

The paper presents a simplified conceptual model for energy demand calculations based on building envelope characteristics, thermal mass and local climate. It is based on a network model and lumped analysis of the dynamic process. Characteristic parameters for the buildings are suggested; Driving temperature (DT), Driving temperature difference, (DTD), External Load Temperature (ELT), and Thermal Load Resistance (TLR). The Building Envelope Performance ( BEP 0 ), based on a controlled constant indoor temperature is introduced. Solution techniques using stable explicit forward differences based on analytical solutions are derived. The conceptual model has been used for mapping the Driving temperature difference and introduced two performance factors and . The first factor represents the effect of thermal comfort interval and thermal mass on the energy demand. The latter represents the ratio between cooling and heating energy demand. These three parameters and factors have been visualized on U.S. maps and enable a possibility to communicate the demand of energy, and cooling and the coupling to building characteristics, in a concise way.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Ionic Liquid Functionalizes the Metal Organic Framework for Microwave-Assisted Direct Air Capture of CO 2

Sorbents for direct air capture (DAC) of CO 2 typically employ a chemisorbing material that requires regeneration at elevated temperatures, which is an energy-intensive step. Here, we developed composites of metal organic framework (MOF) with a functional ionic liquid (IL) at 5, 20, and 35 wt % loading, capable of CO 2 chemisorption and regeneration by moisture-swing coupled microwave (MW) irradiation. In dynamic breakthrough measurements, CO 2 from synthetic air (500 ppm of CO 2 ) was selectively absorbed (0.5 mmol/g at 30 °C) and then rapidly released (2–4 min) by dielectric heating at 60 °C. The developed IL/MOF composites demonstrate (1) targeted energy transfer to the IL domains where CO 2 is released due to dielectric heating upon MW irradiation and (2) a fast desorption rate due to large surface area offered by the MOF architecture. Furthermore, this study establishes carbon capture through surface enhancement of a porous substrate with ILs for future DAC processes that can be powered by sustainable energy sources.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanical adaptation of brachiopod shells via hydration-induced structural changes

The function-optimized properties of biominerals arise from the hierarchical organization of primary building blocks. Alteration of properties in response to environmental stresses generally involves time-intensive processes of resorption and reprecipitation of mineral in the underlying organic scaffold. Here, we report that the load-bearing shells of the brachiopod Discinisca tenuis are an exception to this process. These shells can dynamically modulate their mechanical properties in response to a change in environment, switching from hard and stiff when dry to malleable when hydrated within minutes. Using ptychographic X-ray tomography, electron microscopy and spectroscopy, we describe their hierarchical structure and composition as a function of hydration to understand the structural motifs that generate this adaptability. Key is a complementary set of structural modifications, starting with the swelling of an organic matrix on the micron level via nanocrystal reorganization and ending in an intercalation process on the molecular level in response to hydration.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Material Fracturing and Failure Simulation Datasets

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visible-light H 2 evolution using dye-sensitized TiO 2 : effects of physicochemical properties of TiO 2 on excited carrier dynamics and activity

Dye-sensitized photocatalysts have emerged as promising materials for solar-driven water splitting due to their ability to utilize visible light, in contrast to conventional wide-band-gap semiconductors. However, the relationship between semiconductor properties and charge carrier dynamics remains insufficiently understood. In this study, we investigated Pt/TiO 2 systems sensitized with a visible-light-absorbing Ru(II) polypyridyl complex (RuP), focusing on how the crystal phase and specific surface area of TiO 2 influence excited carrier dynamics and H 2 evolution activity. To isolate the effects of TiO 2 properties, Pt and RuP loadings were standardized across samples. Emission lifetime analysis showed similarly efficient electron injection from RuP to TiO 2 in all cases, suggesting that injection efficiency does not account for observed differences in activity. Transient absorption measurements revealed that back electron transfer (BET) rates depended strongly on the TiO 2 phase, with anatase and P25 exhibiting slower BET and higher activity for H 2 evolution than rutile. The highest apparent quantum yield for H 2 evolution was 12.0% at 450 nm. Among anatase samples, larger surface areas correlated with higher activity, while smaller-area samples exhibited slower BET rates but still low H 2 evolution activity, implying a role for RuP dye–dye interactions in performance loss. This was further supported by improvements in H 2 evolution activity by lowering RuP loading or adding co-adsorbents. Overall, these results demonstrate that both BET suppression and control over RuP dye aggregation are essential for designing efficient dye-sensitized photocatalytic systems.

Harada, Kakeru [Institute of Science Tokyo (Japan)↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗