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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Simulation budgeting for hybrid effective field theories

In this work, we forecast the number of, and requirements on, N-body simulations needed to train hybrid effective field theory (HEFT) emulators for a range of use cases, using a hybrid of HMcode and perturbation theory as a surrogate model. Our accuracy goals, determined with careful consideration of statistical and systematic uncertainties, are 1% accurate in the high-likelihood range of cosmological parameters, and 2% accurate over a broader parameter space volume for k < 1 h Mpc -1 and z < 3. Focusing in part on the 8-parameter w 0 w a CDM+ m ν cosmological model, we find that < 225 simulations are required to meet our error goals over our wide parameter space, including models with rapidly evolving dark energy, given our simulation and emulator recommendations. For a more restricted parameter space volume, as few as 80 simulations are sufficient. We additionally present simulation forecasts for example use cases, and make the code used in our analyses publicly available. These results offer practical guidance for efficient emulator design and simulation budgeting in future cosmological analyses.

cosmological parameters from LSS↗
DOE OSTI2024

Fast Photoactuation Driven by Supramolecular Polymers Integrated into Covalent Networks

Abstract The design of robotic soft matter capable of emulating the complex movements of living organisms such as mechanical actuation, shape transformation, and autonomous translation remains a grand challenge in soft materials science. Functionalized hydrogels are excellent candidates for such materials since they can operate in water and are highly responsive to their environment, but their response times can be slow. This work investigates fast photoactuation of hybrid bonding hydrogels composed of peptide amphiphile (PA) supramolecular nanofibers bonded covalently to merocyanine‐based (MCH + ) photoresponsive networks. By incorporating ionizable acrylic acid (AA) co‐monomers in these networks, photoactuation at nearly neutral pH is observed, which in turn enables a new mechanism to accelerate the response by triggering the bundling of supramolecular nanofibers by rapid proton exchange reactions. Furthermore, this rapid response and its consequent large shape transformations lead to hydrogels capable of spontaneously tracking external light sources inspired by pedicellariae, defensive organs present in echinoderms like the starfish and the sea urchin. This work suggests that hybrid bonding polymers (HBPs), which leverage the interplay between supramolecular assemblies and covalent networks, offer novel strategies to design rapidly actuating soft robotic materials.

Cezan, S. Doruk↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2022

First-principles comparative study of Cr migration in O3 and O/P hybrid-phased NaCrO 2

In layered Na transition-metal (TM) oxides, TM migration usually occurs at highly charged states and severely deteriorates the capacity and reversibility. Meanwhile, the formation of hybrid phases with the intergrowth of octahedral (O-type) and prismatic (P-type) Na layers also takes place at highly charged states. These hybrid phases are often more stable than simple O3 or P3 stackings. However, little is known about the mechanism and impact of TM migration in these hybrid phases. In this work, a comparative first-principles study is performed to understand the connections between structural changes and Cr migration in layered O3 and hybrid-phased NaCrO 2 . After Cr migration, the hybrid-phased NaCrO 2 suffers from greater layer shrinkage than the O3 phase. Three factors affect the Cr migration energy E mig : the Na concentration, local 3D configurations, and 2D in-plane geometries. Low Na concentrations and certain local 3D configurations facilitate the Cr migration. The Cr migration barriers in both O3 and hybrid-phased NaCrO 2 are positively correlated with the Cr E mig . The Cr migration in 17 doped O3 and hybrid-phased NaCrO 2 is surveyed. In these doped NaCrO 2 , a more uniform distribution of the Cr–O bond lengths usually suggests suppressed Cr migration. Optimal dopants for suppressing Cr migration are identified by considering E mig for both Cr and the dopant. Finally, our comparative study on Cr migration in O3 and hybrid-phased NaCrO 2 reveals the significant role of hybrid-phased structures in the development of layered cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural Diversity and Tunable Emission in Hybrid Organic–Inorganic Copper(I) Bromides

Recently, hybrid organic−inorganic copper(I) metal halides have attracted global attention due to their intriguing optical properties and low-cost solution processability. In this work, we report three hybrid organic−inorganic copper(I) bromides, [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ], synthesized through a slow evaporation method using trimethylphenylammonium (TMPA + ) as the organic cation. By precise control of the CuBr and TMPABr precursors, different copper halide [Cu 2 Br 4 ] 2− , [Cu 6 Br 10 ] 4− , and [Cu 4 Br 6 ] 2− structural units can be obtained. [TMPA] 2 [Cu 2 Br 4 ], [TMPA] 4 [Cu 6 Br 10 ], and [TMPA] 2 [Cu 4 Br 6 ] demonstrate distinct blue, orange, and greenish-yellow light emission, respectively. The first two compounds have zero-dimensional (0D) crystal structures in centrosymmetric triclinic space group P-1 and monoclinic space group P2 1 /n. In contrast, [TMPA] 2 [Cu 4 Br 6 ] features a unique one-dimensional (1D) structure and crystallizes in the centrosymmetric monoclinic space group P2 1 /c. Consequently, the observed greenish-yellow emission of [TMPA] 2 [Cu 4 Br 6 ] is also unique, in contrast to the typical orange-red emission of 0D [Cu 4 Br 6 ]-based compounds. This work provides insights into the design of copper halide light emitters and emphasizes the influence of structural dimensionality on photoluminescence. The tunable optical properties suggest the potential of these materials for multicolor photopatterning, information encryption, and anticounterfeiting applications.

Anions↗
DOE OSTI2024

Efficient continuous Energy-Multigroup hybrid depletion scheme using the Shift Monte Carlo code. Part I: Energy condensation sensitivity analysis

Monte Carlo (MC) codes coupled to depletion solvers are increasingly used to provide high fidelity fuel cycle modeling capabilities. Here, these coupled depletion-MC tools produce accurate results in general but can experience nonphysical spatial oscillations when time steps are large or when a system’s dominance ratio approaches unity. Two substepping techniques have been developed previously to remedy and dampen these spatial oscillations without needing to reduce step sizes. The first approach relied on higher-order techniques to account for spectral changes within steps (extrapolation and interpolation techniques). The second approach used the first order perturbation (FOP) theory to account for the change in the one-group spatial flux distribution within steps. This paper develops a hybrid depletion methodology which, in a way, combines how the flux is handled in both substepping techniques. Specifically, the multigroup (MG) MC Shift code is used to update the flux distribution within steps rather than a one-group FOP solver. A fully reflected pincell is investigated, which is not spatially dependent in the MG representation. Thus, the analysis in this paper is an initial demonstration of hybrid depletion. An upcoming companion paper will focus on how the hybrid depletion dampens spatial oscillations. The hybrid depletion approach is verified to be consistent with previous constant extrapolation depletion (CED) methods. This paper finds that the hybrid CED exhibits some error in the eigenvalue and one group constants within macro steps. To address this discrepancy, a simple interpolation scheme (CELI) is investigated. This work found that CELI sufficiently addresses the discrepancy in spectrum for macro steps up to 100 days. Overall, this work demonstrates that the hybrid depletion method can significantly reduce the number of high fidelity MC executions in a MC-coupled depletion with an acceptable eigenvalue error.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Impact of Nanoparticle Size and Surface Chemistry on Peptoid Self-Assembly

Self-assembled organic nanomaterials can be generated by bottom-up assembly pathways where the structure is controlled by the organic sequence and altered using pH, temperature, and solvation. In contrast, self-assembled structures based on inorganic nanoparticles typically rely on physical packing and drying effects to achieve uniform superlattices. By combining these two chemistries to access inorganic–organic nanostructures, we aim to understand the key factors that govern the assembly pathway and structural outcomes in hybrid systems. In this work, we outline two assembly regimes between quantum dots (QDs) and reversibly binding peptoids. These regimes can be accessed by changing the solubility and size of the hybrid (peptoid-QD) monomer unit. The hybrid monomers are prepared via ligand exchange and assembled, and the resulting assemblies are studied using ex-situ transmission electron microscopy as a function of assembly time. In aqueous conditions, QDs were found to stabilize certain morphologies of peptoid intermediates and generate a final product consisting of multilayers of small peptoid sheets linked by QDs. The QDs were also seen to facilitate or inhibit assembly in organic solvents based on the relative hydrophobicity of the surface ligands, which ultimately dictated the solubility of the hybrid monomer unit. Increasing the size of the QDs led to large hybrid sheets with regions of highly ordered square-packed QDs. A second, smaller QD species can also be integrated to create binary hybrid lattices. Furthermore, these results create a set of design principles for controlling the structure and structural evolution of hybrid peptoid-QD assemblies and contribute to the predictive synthesis of complex hybrid matter.

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

Hybrid Cooling and Water Treatment for Resilient, Water-Self-Sufficient Data Centers

The continued growth of data center infrastructure is intensifying demand for freshwater resources, particularly in water-stressed regions, and is increasingly limiting sustainable capacity expansion. This work investigates a conceptual hybrid system that integrates freeze desalination with ultrasonic-assisted ice separation to enable on-site production of pure water from diverse sources, including seawater, brackish groundwater, and reclaimed industrial or agricultural wastewater. Simultaneously, the system produces low-temperature cooling streams that enhance heat removal in high power density computing environments. A process-level thermodynamic analysis is performed across a range of boundary and operating conditions, including variations in feed concentration, freezing temperature, and mass flow rate. The results are presented as performance curves relating feedwater concentration and target purified water output to the corresponding intake flow requirements, enabling estimation of source water demand per unit of Information Technology Equipment (ITE) energy consumption (kWh) across varying data center scales and operational scenarios. The corresponding electrical energy consumption for integrated cooling and water treatment is also evaluated as a function of system operating parameters and target water production levels. These results provide a basis for evaluating system feasibility across different conditions and for identifying parameter ranges in which integrated water treatment and cooling improve resource efficiency, thermal performance, and operational flexibility in data centers.

Elhefny, Aly [ORNL] (ORCID:0000000284907923)↗
DOE OSTI2023

Comparative Density Functional Theory Study of Magnetic Exchange Couplings in Dinuclear Transition-Metal Complexes

Multicenter transition-metal complexes (MCTMs) with magnetically interacting ions have been proposed as components for information-processing devices and storage units. For any practical application of MCTMs as magnetic units, it is crucial to characterize their magnetic behavior, and in particular, the isotropic magnetic exchange coupling, J, between its magnetic centers. Due to the large size of typical MCTMs, density functional theory is the only practical electronic structure method for evaluating the J coupling. Here, we assess the accuracy of different density functional approximations for predicting the magnetic couplings of eight dinuclear transition-metal complexes, including five dimanganese, two dicopper, and one divanadium with known reliable experimental J couplings spanning from ferromagnetic to strong antiferromagnetic. The density functionals considered include global hybrid functionals which mix semilocal density functional approximations and exact exchange with a fixed admixing parameter, six local hybrid functionals where the admixing parameters are extended to be spatially dependent, the SCAN and r 2 SCAN meta-generalized gradient approximations (GGAs), and two widely used GGAs. We found that global hybrids tested in this work have a tendency to over-correct the error in magnetic coupling parameters from the Perdew–Burke–Ernzerhof (PBE) GGA as seen for manganese complexes. The performance of local hybrid density functionals shows no improvement in terms of bias and is scattered without a clear trend, suggesting that more efforts are needed for the extension from global to local hybrid density functionals for this particular property. The SCAN and r 2 SCAN meta-GGAs are found to perform as well as benchmark global hybrids on most tested complexes. We further analyze the charge density redistribution of meta-GGAs as well as global and local hybrid density functionals with respect to that of PBE, in connection to the self-interaction error or delocalization error.

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

A Semi-supervised Hybrid Machine Learning Framework for the Qualification of Resistance Spot Welds

• Industries requiring high structural integrity, including automotive, aerospace, and construction, place considerable significance on weld quality classification. • The inspection normally involves human expertise through predefined quality metrics that are subjective, error-prone, and time-intensive • The challenge to classification model development is the scarcity of labeled data and imbalanced distributions in the data that are labeled. • This work develops a new hybrid methodology that achieves clustering using KMeans++ together with supervised classification to overcome these challenges. • The ensemble-based classifiers were identified as optimal, with accuracy enhancements of up to 8% using the pseudo-labeled dataset. • The work provides practical insight into feature engineering and machine learning integration in industrial quality assurance applications.

Rogers, Jeremy K. [Savannah River National Laborat↗
DOE OSTI2022

Role of nanocellulose in tailoring electroanalytical performance of hybrid nanocellulose/multiwalled carbon nanotube electrodes

Abstract Nanocellulose has emerged as a promising green dispersant for carbon nanotubes (CNTs), and there is an increasing trend in developing nanocellulose/CNT hybrid materials for electrochemical detection of various small molecules. However, there have been very few comprehensive studies investigating the role of nanocellulosic material properties upon the electroanalytical performance of the resultant hybrid electrodes. In this work, we demonstrate the influence of both nanocellulose functionalization and geometry, utilizing sulfated cellulose nanocrystals, sulfated cellulose nanofibers, and TEMPO-oxidized cellulose nanofibers. Transmission electron microscopy tomography enables direct visualization of the effect of nanocellulosic materials on the hybrid architectures. High resolution X-ray absorption spectroscopy verifies that the chemical nature of CNTs in the different hybrids is unmodified. Electroanalytical performances of the different nanocellulose/CNT hybrid electrodes are critically evaluated using physiologically relevant biomolecules with different charge such as, dopamine (cationic), paracetamol (neutral), and uric acid (anionic). The hybrid electrode containing fibrillar nanocellulose geometry with a high degree of sulfate group functionalization provides the highest electroanalytical sensitivity and strongest enrichment towards all studied analytes. These results clearly demonstrate for the first time, the extent of tailorability upon the electroanalytical response of nanocellulose/CNT hybrid electrodes towards different biomolecules, offered simply by the choice of nanocellulosic materials.

Durairaj, Vasuki↗
DOE OSTI2023

Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Ultra-narrow-linewidth hybrid-integrated self-injection locked laser at 780 nm

Narrow-linewidth lasers are essential across a wide range of applications, including classical and quantum sensing, trapped ion systems, position/navigation/timing systems, optical clocks, and microwave frequency synthesizers. In the visible and near-visible spectrum, low-noise lasers are particularly important for laser trapping and cooling techniques, which are vital for trapped ion quantum computing, sensing, and atomic clocks. In this context, our work showcases a hybrid-integrated narrow-linewidth laser that operates at 780 nm, achieving a self-heterodyne linewidth of 105 Hz. To validate the experimental results, we performed a numerical analysis that combines insights from a many-body theory applied to the gain region with a travelling-wave model to capture the laser dynamics. Our investigation further delves into how the linewidth of the self-injection locked lasers is influenced by the parameters of micro-ring resonators, aiming to assess the potential for achieving Hz-level integrated laser linewidths at 780 nm. This work not only demonstrates the technical feasibility of Hz-level narrow-linewidth lasers but also lays the groundwork for future explorations in the field.

Prokoshin, Artem (ORCID:0009000699760238)↗
DOE OSTI2023

A hybrid deep neural operator/finite element method for ice-sheet modeling

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work we develop a hybrid approach to approximate existing ice sheet models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while we retain a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model, compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier and can therefore be used for generating samples for uncertainty quantification. Further, we study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A hybrid calorimetry-simulation model of mixing enthalpy for molten salt

Calorimetric determination of enthalpies of mixing (ΔH mix ) in multicomponent molten salts is often interpreted using empirical models that lack physically meaningful parameters. However, for improving pyrochemical separation of spent nuclear fuel, where lanthanides are major fission products and critical elements, a deeper thermodynamic understanding of the link between excess thermodynamic properties and solvation structure is critically needed. In this work, we implement a hybrid and physics-informed framework, MIVM+Calorimetry+AIMD, which integrates experimentally measured ΔH mix (via high temperature drop calorimetry) with solvation structures from ab initio molecular dynamics (AIMD). This approach is demonstrated using LaCl 3 mixed with eutectic LiCl-KCl (58 mol% – 42 mol%) at 873 K and 1133 K. MIVM-derived parameters enable extrapolation of excess Gibbs energy and La 3+ activity across compositions. In contrast, direct ΔH mix predictions from AIMD and polarizable ion model simulations deviate significantly. By incorporating experimentally benchmarked solvation structures into an interpretable thermodynamic model, the MIVM+Calorimetry+AIMD formalism achieves higher accuracy and generalizable method for studying molten salts, offering a robust path for understanding and optimizing molten salt chemistry relevant to nuclear fuel cycles and separation science.

Goncharov, Vitaliy G. [Washington State Univ., Pul↗
DOE OSTI2021

Development of High Energy Density Diaminocyclopropenium-Phenothiazine Hybrid Catholytes for Non-Aqueous Redox Flow Batteries

Our work describes the design of diaminocyclopropenium-phenothiazine hybrid catholytes for non-aqueous redox flow batteries. The molecules are synthesized in a rapid and modular fashion by appending a diaminocyclopropenium (DAC) substituent to the nitrogen of the phenothiazine. Combining a versatile C-N coupling protocol (which provides access to diverse derivatives) with computation and structure-property analysis enabled the identification of a catholyte that displays stable two-electron cycling at potentials of 0.64 and 1.00 V vs. Fc/Fc + as well as high solubility in all oxidation states (≥0.45 M in TBAPF 6 /MeCN). This catholyte was deployed in a high energy density two-electron RFB, exhibiting >90 % capacity retention over 266 hours of flow cell cycling at >0.5 M electron concentration.

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

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Adsorption↗