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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Recent trends in all-organic polymer dielectrics for high-temperature electrostatic energy storage capacitors

Electrostatic energy storage (EES) capacitors are critical for renewable energy and high-power systems, driving the search for dielectric materials that combine superior electrical insulation, mechanical flexibility, low density, cost-effectiveness, and processability. Polymer-based dielectrics have emerged as leading candidates, particularly for high electric field applications. However, conventional polymers often fail to meet the demands of high-temperature environments due to increased electrical conductivity and reduced discharged energy density at elevated temperatures, resulting in energy loss and reduced performance. High glass transition temperature (T g) polymers show promise but require further optimization to enhance their energy storage capabilities under thermal and electrical stress. This review provides a comprehensive update on recent advancements in high-T g polymer-based dielectrics for EES capacitors, focusing on both intrinsic polymers and all-organic composites. It outlines key design principles, critical performance parameters, and innovative strategies—such as nanofiller doping, layered architectures, physical blending, and chemical crosslinking—to improve electrical, thermal, and mechanical properties. The review also highlights emerging trends, including the integration of machine learning algorithms to explore novel polymer structures and expand the chemical design space. By bridging the gap between academic research and industrial application, this review aims to accelerate the development of next-generation dielectric materials capable of balancing multiple performance metrics for high-temperature EES capacitors.

Xie, Zongliang↗
DOE OSTI2020

Review of heat transfer enhancement techniques for single phase flows

The thermal energy exchange between a flowing fluid and its confining channel is a ubiquitous process in modern society. To enhance the fluid-to-wall or wall-to-fluid heat transfer, several techniques have been developed to maximize the contact area between the fluid and the inner wall and/or disrupt the flow to enhance circulation or induce turbulence. Deployment of channels having features capable of enhancing heat transfer enables the reduction of heat exchanger size while maintaining performance. Reduction in equipment size is critical due to the ability to minimize the required volume of costly working fluids and to mitigate potential safety concerns associated with total system fluid volume. Here, a comprehensive review of single-phase heat transfer enhancement techniques is presented. The article provides a thorough comparison by analyzing the heat transfer rate, pressure drop, and other operational aspects. Single-phase heat transfer enhancement methods are divided into active and passive techniques. Active methods such as electrohydrodynamic (EHD), magnetohydrodynamics (MHD), or mechanical motion require external power to create enhancement. Passive methods such as dimples, fins, or tape inserts do not require external input and rely only on surface modification. Although active methods are more expensive and difficult to implement compared to passive techniques, it enables active control of heat transfer augmentation. Finally, this review develops and summarizes key learning data for design optimization enabled by additive manufacturing and machine learning algorithms, helping to inform these next-generation heat exchanger design methodologies for a plethora of modern applications such as electrification of vehicles, computing, and classical industries.

42 ENGINEERING↗
DOE OSTI2017

Toward a systematic exploration of nano-bio interactions

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability. - Highlights: • Nanomaterials studies make non-systematic alterations to nanoparticle properties. • Vast nanomaterials property spaces require systematic studies of nano-bio interactions. • Experimental design and modelling are efficient ways of exploring materials spaces. • We advocate systematic modification and computational analysis to probe nano-bio interactions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Neural network reconstruction of the DIII-D tokamak plasma boundary using a reduced set of diagnostics

This study investigates the feasibility of reconstructing the last closed flux surface in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of $0.04$ m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to $0.03$ m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in fusion power plants due to diagnostic constraints imposed by the presence of blankets and shielding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advanced Method Optimization for Sampling and Analysis Instrumentation

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. In conclusion, this strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical technique.

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

Universal Nuclear Accident Dosimeter

The Lawrence Livermore National Laboratory (LLNL) Universal Nuclear Accident Dosimetry (UNAD) project is a four-year initiative aimed at advancing nuclear accident dosimetry methods. This article presents an overview of the research, key findings, and the progress made throughout the project. The primary goals included a background into the history of nuclear accident dosimetry, consolidating current dosimetry techniques within the NNSA/DOE complex, fostering collaboration among subject matter experts, and exploring novel technologies for potential implementation. The technical focus centered on investigating new and novel technologies, instrumentation methods, and analysis methods to develop recommendations for a potential nuclear accident dosimeter (NAD) to be universally deployed through the DOE complex. A multilaboratory and multinational Usergroup was established, conducting periodic meetings to facilitate knowledge exchange. The UNAD team has participated in two international nuclear accident dosimetry intercomparison exercises and one characterization exercise, where the existing LLNL NAD and a prototype alanine electron paramagnetic dosimeter NAD were deployed. Ongoing improvements are being made to the prototype NAD based on results from the exercises, laboratory studies, and collaboration with other laboratories. A machine learning algorithm to optimize the geometry and conversion factors of the current LLNL NAD is being implemented, and the resulting design will be tested in the next exercise. In conclusion, key lessons learned and future directions for the project are discussed.

Electron paramagnetic resonance spectroscopy↗
DOE OSTI2022

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events

Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. Here, the resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Discovering a Transferable Charge Assignment Model Using Machine Learning

Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics. Machine learning has emerged as a powerful tool for modeling chemistry at unprecedented computational speeds given accurate reference data. However, certain tasks, such as charge assignment, do not have a unique solution. We use a machine learning algorithm to discover a new charge assignment model by learning to replicate molecular dipole moments across a large, diverse set of nonequilibrium conformations of molecules containing C, H, N, and O atoms. The new model, called Affordable Charge Assignment (ACA), is computationally inexpensive and predicts dipoles of out-of-sample molecules accurately. Furthermore, dipole-inferred ACA charges are transferable to dipole and even quadrupole moments of much larger molecules than those used for training. We apply ACA to dynamical trajectories of biomolecules and produce their infrared spectra. Additionally, we find that ACA assigns similar charges to Charge Model 5 but with greatly reduced computational cost.

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

Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules

This letter announces the Virtual Excited State Reference for the Discovery of Electronic Materials Database (VERDE materials DB), the first database to include downloadable excited-state structures (S 0 , S 1 , T 1 ) and photophysical properties. Furthermore, VERDE materials DB is searchable, open-access via www.verdedb.org , and focused on light-responsive π-conjugated organic molecules with applications in green chemistry, organic solar cells, and organic redox flow batteries. It includes results of our active and past virtual screening studies; to date, more than 13 000 density functional theory (DFT) calculations have been performed on 1 500 molecules to obtain frontier molecular orbitals and photophysical properties, including excitation energies, dipole moments, and redox potentials. To improve community access, we have made VERDE materials DB available via an integration with the Materials Data Facility. We are leveraging VERDE materials DB to train machine learning algorithms to identify new materials and structure–property relationships between molecular ground- and excited-states. We present a case-study involving photoaffinity labels, including predictions of new diazirine-based photoaffinity labels anticipated to have high photostabilities.

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

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

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

Correlating Protein Dynamics and Catalytic Activity of a Model Hydrogenase Using Paramagnetic and Biological Nuclear Magnetic Resonance Spectroscopy

Rational catalyst design remains a significant challenge, with electronic structure, steric, and electrostatic effects known to contribute to activity. Recently, dynamics has been recognized as another factor that impacts catalysis, though identifying and predicting these effects has remained out of reach. Nickel-substituted rubredoxin (NiRd), a protein-based mimic of a hydrogenase enzyme, serves as a model catalytic system in which dynamics can be systematically investigated with respect to activity. While over 30 secondary-sphere mutants of NiRd have been shown to be catalytically active, no significant correlation was observed between the rates and catalytic overpotential or electronic structure, prompting questions about the protein-derived factors that modulate activity. Here, in this work, NMR spectroscopy was used to investigate the roles of substrate accessibility, protein dynamics, and protein stability in controlling catalysis. Significant paramagnetic effects from the nickel center (S = 1) isolate the methylene proton resonances of the metal-coordinating cysteine residues. The sensitivity of resonance positions and linewidths to local environment offers an opportunity to study dynamical molecular changes around the metal center with high resolution. Machine learning algorithms were employed to identify correlations between the catalytic activity and the paramagnetic NMR spectra. These analyses revealed spectroscopic features of specific cysteine protons that report on catalytic overpotential and increased turnover rates, which are further supported by the results obtained using high-field NMR techniques. Collectively, these studies indicate the potential for multifrequency NMR techniques to resolve key contributors to catalytic activity and highlight the importance of local and outer-sphere dynamics.

Protein Engineering↗
DOE OSTI2018

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni 2 P from Nonmetal Surface Doping Interpreted via Machine Learning

The activity of Ni 2 P catalysts for the hydrogen evolution reaction (HER) is currently limited by strong H adsorption at the Ni 3 -hollow site. We investigate the effect of surface nonmetal doping on the HER activity of the Ni 3 P 2 termination of Ni 2 P(0001), which is stable at modest electrochemical conditions. Using density functional theory (DFT) calculations, we find that both 2p nonmetals and heavier chalcogens provide nearly thermoneutral H adsorption at moderate surface doping concentrations. We also find, however, that only chalcogen substitution for surface P is exergonic. For intermediate surface concentrations of S, the free energy of H adsorption at the Ni 3 -hollow site is -0.11 eV, which is significantly more thermoneutral than the undoped surface (-0.45 eV). We use the regularized random forest machine learning algorithm to discover the relative importance of structure and charge descriptors, extracted from the DFT calculations, in determining the HER activity of Ni 2 P(0001) under different doping concentrations. Here, we discover that the Ni–Ni bond length is the most important descriptor of HER activity, which suggests that the nonmetal dopants induce a chemical pressure-like effect on the Ni 3 -hollow site, changing its reactivity through compression and expansion.

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

Soil Organic Matter Temperature Sensitivity Cannot be Directly Inferred From Spatial Gradients

Developing and testing decadal-scale predictions of soil response to climate change is difficult because there are few long-term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal-scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction-network-based model of soil organic matter and microbes using measured SOC stocks from a 4,000-km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Q max , mean annual temperature, and net primary production, assessed using a machine-learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was –18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site-level SOC loss was 5%. Here, we conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long-term warming experiments, especially mineral fractions, are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

Abstract Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly varying large‐scale environment is in statistical equilibrium with a large number of small and short‐lived convective clouds. They fail to capture nonequilibrium transitions such as the diurnal cycle and the formation of mesoscale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud‐permitting model simulation, theory, and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds, with the goal of informing the representation of these interactions in global climate models. Fifteen wet seasons of precipitating cloud observations by a C‐band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equations relating large‐scale forcing, mass flux, the convective cell size distribution, and the stratiform area. Under realistic large‐scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a few convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Illumination of Thunderclouds by Lightning: 4. Volumetric Thunderstorm Imagery

Optical instruments such as the Geostationary Lightning Mapper (GLM) detect lightning based on transient changes in cloud illumination. The horizontal location of lightning is determined from the coordinates of the pixels on the imaging array illuminated during the flash. However, the vertical position of the lightning pulses (approximated by GLM “groups”) below the cloud top cannot be routinely measured from a single space-based instrument. In our prior work, we have developed a machine learning algorithm that can infer optical source altitude for a given pulse based on how the optical energy is distributed across the group footprint and the local Advanced Baseline Imager Cloud-Top Height (CTH). In this fourth part of our thundercloud illumination study, we leverage these source altitudes to generate volumetric GLM imagery of a Colombia thunderstorm. We find that 3D versions of the current GLM meteorological imagery products (that describe thunderstorm kinematics) and thundercloud imagery products (that depict how the flashes appear from space) provide additional insights into lightning activity in the thunderstorm that are lost in the vertical integration used to generate the current 2D GLM gridded products. This new volumetric imaging capability provides a more comprehensive picture of where lightning occurs in the storm, how its physical characteristics vary across three-dimensional space, and how its optical emissions interact with surrounding the cloud medium.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Observationally Trained Markov Model for MJO Propagation

A Markovian stochastic model is developed for studying the propagation of the Madden-Julian Oscillation (MJO). This model represents the daily changes in real time multivariate MJO (RMM) indices as random functions of their current state and background conditions. The probability distribution function of the RMM changes is obtained using a machine learning algorithm trained to maximize MJO forecast skills using observed daily indices of RMM and different modes of variability. Skillful forecasts are obtained for lead times between 8 and 27 days. Large ensemble simulations by the stochastic model show that with monsoonal changes in the background state, MJO propagation across the Maritime Continent (MC) is most likely to be disrupted in boreal spring and summer when MJO events propagate from favorable conditions over the Indian Ocean to unfavorable ones over the MC, and predictability is higher during spring and summer when MJO activity is away from the MC region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Sensitivity Analysis of Wind and Turbulence Predictions With Mesoscale‐Coupled Large Eddy Simulations Using Ensemble Machine Learning

Abstract Coupling between mesoscale models and large‐eddy simulation (LES) models is increasingly used to more realistically represent the wide range of scales of atmospheric motions affecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale‐to‐microscale coupled modeling frameworks are potentially affected by a large number of uncertain closure parameters. Here, we investigate the sensitivity associated with six closure parameters related to a 1.5‐order subgrid‐scale turbulence closure for an ensemble of mesoscale‐coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project. Machine learning algorithms are used to explore the sensitivity of LES predictions, considering the effects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coefficient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for several of the simulation response variables we examine.

54 ENVIRONMENTAL SCIENCES↗