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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ion Specificity Influences on the Structure of Zwitterionic Brushes

Zwitterionic brushes have a wide range of applications, including use as lubricating surfaces and antifouling membranes. In this study, densely end-tethered poly(cysteine methacrylate) (PCysMA) brushes were synthesized using surfaceinitiated activators regenerated by electron transfer atom transfer radical polymerization (SI-ARGET-ATRP). The structure of the PCysMA brushes was investigated using ellipsometry, X-ray reflectivity (XRR), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and atomic force microscopy (AFM). The results of these characterization techniques were used to study the effect of SO 4 2– , Cl – , NO 3 – , Br – , and SCN – anions, divalent Ca 2+ and Ba 2+ cations, and trivalent Y 3+ cations on the structure of the PCysMA brushes. The results showed that the PCysMA brushes in solution exhibit an “antipolyelectrolyte” effect to a certain degree, which inversely follows the Hofmeister series of anions. The introduction of divalent cations Ca 2+ and Ba 2+ had a modest impact on the dimensions of the PCysMA brushes, indicating that chelating interactions between the cations and zwitterion units work against the “antipolyelectrolyte” effect. The complexation was even stronger in the presence of trivalent Y 3+ cations, which caused the PCysMA brushes to shrink. These findings highlight the importance of ion specificity to the structure of zwitterionic brushes in aqueous solutions.

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

The Kinetic Consequences of Water on Catalytic Methane Pyrolysis

Hydrogen production from biomass and natural gas has emerged as a prominent research area in response to the growing demand for energy from alternative sources that minimize CO 2 emissions. In this study, we investigate the impact of water, which is present in and generated from biomass-derived streams, on carbon nanotube (CNT) growth and hydrogen production during methane decomposition using Ni–Mo/MgO as a catalyst. We reveal here that the role of water on CNT growth is highly complex; its effect depends on the stage of growth at which the water is incorporated. When water is introduced at the beginning of methane decomposition ( t = 0 h), methane conversion rates are negatively impacted. We hypothesize that water inhibits the significant phase changes the Ni–Mo/MgO catalyst undergoes during catalyst carburization. In contrast, the incorporation of a small percentage of water after a stabilization period ( t = 3 h) results in methane conversion rate enhancements that scale with the introduced water partial pressure as water selectively reacts with amorphous carbon deposits that lead to catalyst deactivation, thus prolonging the lifetime of some of the most active sites. Moreover, water incorporation after stabilization significantly reduces the apparent activation energy. Density Functional Theory (DFT) calculations reveal that water preferentially interacts with carbon fragments on the catalyst surface to remove carbon deposits with a barrier lower than that required for methane activation, further supporting its role in cleaning active sites on the catalyst surface. Characterization of the resulting carbon nanotubes reveals the formation of more graphitic materials produced in the presence of water, highlighting the impact of water on nanotube properties. These results provide clarity toward the many ways in which water, or cofeeding of biomass-derived materials, may impact catalytic methane pyrolysis rates.

carbon nanotubes↗
DOE OSTI2020

Demonstrating the Critical Role of Solvation in Supported Ti and Nb Epoxidation Catalysts via Vapor-Phase Kinetics

Catalytic oxidation of hydrocarbons with hydrogen peroxide (H 2 O 2 ) has been of the utmost importance for several decades. The vast majority of studies have been performed in the condensed phase, even though condensed phases introduce complex solvent effects and can promote the leaching of active sites. In response, we have built a custom reactor system to understand H 2 O 2 activation and selective oxidation in the vapor-phase. In this report, we study the epoxidation of cyclohexene with H 2 O 2 over four Lewis-acidic metal oxide catalysts: Ti and Nb grafted on SiO 2 and on the Zr based metal–organic framework, NU-1000. The M–SiO2 materials are highly selective to the formation of epoxides and diols, as they can be in the condensed phase, while the NU-1000 based materials are far more prone to overoxidation to CO 2 , which appears to be connected to their strong reactant adsorption. Apparent activation energies are calculated for all materials when operating in the same kinetic regime, and the heats of cyclohexene adsorption into their pores are then used to directly compare intrinsic enthalpies of activation in the vapor vs condensed phase for the M–SiO 2 catalysts. Nb–SiO 2 catalysts exhibit similar intrinsic enthalpies of activation in the vapor and condensed phases, whereas the condensed phase transition state in Ti–SiO 2 is 24 kJ/mol lower in energy than that of the same material in the vapor phase. These experiments establish another methodology for understanding the various roles of solvent in selective oxidation reactions and studying these reactions under conditions that differ significantly from the thousands of prior studies in the condensed phase.

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

Tunable band gaps and optical absorption properties of bent MoS2 nanoribbons

Abstract The large tunability of band gaps and optical absorptions of armchair MoS 2 nanoribbons of different widths under bending is studied using density functional theory and many-body perturbation GW and Bethe–Salpeter equation approaches. We find that there are three critical bending curvatures, and the non-edge and edge band gaps generally show a non-monotonic trend with bending. The non-degenerate edge gap splits show an oscillating feature with ribbon width n , with a period $$\Delta n=3$$ Δ n = 3 , due to quantum confinement effects. The complex strain patterns on the bent nanoribbons control the varying features of band structures and band gaps that result in varying exciton formations and optical properties. The binding energy and the spin singlet–triplet split of the exciton forming the lowest absorption peak generally decrease with bending curvatures. The large tunability of optical properties of bent MoS 2 nanoribbons is promising and will find applications in tunable optoelectronic nanodevices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Conditional guided generative diffusion for particle accelerator beam diagnostics

Abstract Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam’s 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam’s longitudinal phase space at megapixel resolution (1024 × 1024) based on a generative conditional diffusion model. We demonstrate the model’s generative ability on experimental data from the European X-ray FEL.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Facile design of renewable lignin copolymers by photoinitiated RAFT polymerization as Pickering emulsion stabilizers

As the richest aromatic renewable resource, lignin has attracted significant attention for fabricating various materials. However, due to lignin's structural complexity, an effective and facile structural modification strategy remains a substantial challenge. Herein, we have successfully applied photoinitiated reversible addition–fragmentation chain transfer (RAFT) as a versatile tool to synthesize amphiphilic lignin copolymers with lignin derivatives as the macroinitiator. The experimental and density functional theory analysis systematically investigated the photopolymerization kinetics, proving excellent control over molecular weights. Additionally, 1 H NMR and FT-IR analyses confirmed the well-designed structures. Moreover, the structures of copolymers were tunable by simple variations of monomer structures. Finally, the self-assembly of the synthesized copolymers into nanoparticles was explored, together with the potential applications as Pickering emulsion stabilizers. Finally, we believe that this work will offer a facile and green approach to synthesize lignin-based novel copolymers and further expand its applications.

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

Facile electrocatalytic proton reduction by a [Fe–Fe]-hydrogenase bio-inspired synthetic model bearing a terminal CN — ligand

An azadithiolate bridged CN — bound pentacarbonyl bis-iron complex, mimicking the active site of [Fe–Fe] H 2 ase is synthesized. The geometric and electronic structure of this complex is elucidated using a combination of EXAFS analysis, infrared and Mössbauer spectroscopy and DFT calculations. The electrochemical investigations show that complex 1 effectively reduces H + to H 2 between pH 0–3 at diffusion-controlled rates (10 11 M —1 s —1 ) i.e. 10 8 s —1 at pH 3 with an overpotential of 140 mV. Electrochemical analysis and DFT calculations suggests that a CN— ligand increases the p K a of the cluster enabling hydrogen production from its Fe(I)–Fe(0) state at pHs much higher and overpotential much lower than its precursor bis-iron hexacarbonyl model which is active in its Fe(0)–Fe(0) state. The formation of a terminal Fe–H species, evidenced by spectroelectrochemistry in organic solvent, via a rate determining proton coupled electron transfer step and protonation of the adjacent azadithiolate, lowers the kinetic barrier leading to diffusion controlled rates of H 2 evolution. The stereo-electronic factors enhance its catalytic rate by 3 order of magnitude relative to a bis-iron hexacarbonyl precursor at the same pH and potential.

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

Sample glue layer investigation and mitigation for laser induced prompt impulse experiments

Understanding longer timescale material reactions under dynamic stress loading is critical for applications in materials engineering, shock physics, and planetary science. Prompt impulse experiments generate lower pressures since the ablator—the material first removed by the laser—is thicker and farther from the diagnostic plane, capturing aggregate material responses from the initial shock wave, rarefaction waves, and later time effects. This complexity demands thorough material characterization and simulation support. Since traditional sample construction is specific to supported shock experiments, designing prompt impulse experiments requires reconsideration around target design and sample engineering. Here, we present sample preparation techniques, experimental investigations, and theoretical simulations to investigate glue layer impacts, aiming to standardize samples for consistent data at lower laser fluences. We find that glue layers <30 μm have a minimal impact on peak velocity and pulse shape. The peak velocity scales linearly with glue layer thickness until a glue layer of 75 μm. For glue layers >75 μm, the peak velocity no longer scales with thickness; however, the pulse shape continues to degrade as described by simulations.

Lasers↗
DOE OSTI2026

Analysis of a Heat Balance Experiment in the TREAT Reactor with SPARTA and OpenMC

This article presents the interpretation of a heat balance experiment in TREAT using the SPARTA methodology and an OpenMC model. The study examines the effects of control rods and temperature variations on the effective multiplication factor ($k_{\rm eff}$) and correction factors. Results indicate that both core temperature and rod positions have a linear effect on $k_{\rm eff}$ and correction factors in the upper-west channel. A critical hypothesis of SPARTA was validated: reactivities and correction factors can be obtained by linearly combining individual effects. The previously considered negligible effect of temperature on correction factors was found to be significant, necessitating updates to the SPARTA methodology. By fitting an exponential curve to mean temperature data, SPARTA's accuracy and convergence have been improved. Comparisons between SPARTA and OpenMC showed consistent reactivity removal due to temperature increase. While SPARTA effectively interprets complex transients, simpler methods may suffice when correction factors have minimal influence on reactivity calculations.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Measurement of muon neutrino charged-current quasielasticlike cross section using off-axis NuMI beam at ICARUS

This paper presents the first neutrino cross-section measurement from the ICARUS detector at Fermilab, using Neutrinos at the Main Injector (NuMI) beam data collected from two beam operation periods corresponding to 2.5 × 10 20 protons-on-target in neutrino beam mode. The signal is defined by events with no pions produced in the final state, a topology dominated by charged-current quasielasticlike signatures. The measurement is reported as flux-averaged differential cross sections as functions of kinematic variables that provide sensitivity to the complex nuclear effects that often dominate the systematic uncertainty budgets of neutrino oscillation measurements. Specifically, this work reports cross sections in two angular variables—the angle of the outgoing lepton and the opening angle between the lepton and leading proton—and two variables characterizing the kinematic imbalance between the muon and proton in the plane transverse to the incoming neutrino. These results are compared against predictions from a variety of neutrino event generators, with p values calculated between the extracted cross sections and each prediction. Overall, the predictions agree with the data; however, the current budget of uncertainties does not yet provide sufficient discriminating power to favor a specific model.

Abd Alrahman, F. [Houston U.]↗
DOE OSTI2025

Magnetic order and physical properties of the kagome metal UNb 6 ⁢Sn 6

The 𝑅⁢𝑀 6 ⁢𝑋 6 family of materials (𝑅 = rare-earth, 𝑀 = transition metal, 𝑋 = Ga, Si, Ge, Sn) produces an array of emergent phenomena, such as charge density waves, intrinsic Hall effects, and complex magnetic order, due to its kagome net of transition metal atoms, its local-moment magnetic anisotropies, and its extensive chemical tunability. Here, in this study, we report UNb 6⁢ Sn 6 , a new “166” material containing both an actinide and a 4⁢𝑑 transition metal, along with its nonmagnetic analog ThNb6⁢Sn6, to investigate the properties of a 5⁢𝑓−4⁢𝑑 electron 166 system. UNb 6 ⁢Sn 6 crystallizes in the hexagonal 𝑃⁢6/𝑚⁢𝑚⁢𝑚 space group with a small degree of disorder due to shifts in the size of the CoSn-like cages along the 𝑐 axis. Upon cooling at zero magnetic field, the material undergoes two magnetic phase transitions at 𝑇 2 = 46K and 𝑇 N = 43K. The low-temperature, zero-field phase is an antiferromagnet with ordered uranium moments and a k = (0,0,1/2) propagation vector determined by neutron diffraction. Remarkably, with a magnetic field applied along the 𝑐 axis, five additional magnetic transitions occur, evidenced by magnetization and resistivity data, before the moment saturates at 2.62 𝜇 B /U at 2 K and ≥ 13.6T. In two magnetic phase regions, the Hall resistivity of UNb 6⁢ Sn 6 significantly deviates from the magnetization, suggesting that the phases have a large Berry curvature or a change in the Fermi surface. The unknown magnetic ordering of the field-dependent phases of UNb 6⁢ Sn 6 demonstrates the complexity of the 5⁢𝑓−4⁢𝑑 166 system and encourages further study of its properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improved precision in As speciation analysis with HERFD-XANES at the As K -edge: the case of As speciation in mine waste

High-energy-resolution fluorescence-detected (HERFD) X-ray absorption near-edge spectroscopy (XANES) is a spectroscopic method that allows for increased spectral feature resolution, and greater selectivity to decrease complex matrix effects compared with conventional XANES. XANES is an ideal tool for speciation of elements in solid-phase environmental samples. Accurate speciation of As in mine waste materials is important for understanding the mobility and toxicity of As in near-surface environments. In this study, linear combination fitting (LCF) was performed on synthetic spectra generated from mixtures of eight measured reference compounds for both HERFD-XANES and transmission-detected XANES to evaluate the improvement in quantitative speciation with HERFD-XANES spectra. The reference compounds arsenolite (As 2 O 3 ), orpiment (As 2 S 3 ), getchellite (AsSbS 3 ), arsenopyrite (FeAsS), kaňkite (FeAsO 4 ·3.5H 2 O), scorodite (FeAsO 4 ·2H 2 O), sodium arsenate (Na 3 AsO 4 ), and realgar (As 4 S 4 ) were selected for their importance in mine waste systems. Statistical methods of principal component analysis and target transformation were employed to determine whether HERFD improves identification of the components in a dataset of mixtures of reference compounds. LCF was performed on HERFD- and total fluorescence yield (TFY)-XANES spectra collected from mine waste samples. Arsenopyrite, arsenolite, orpiment, and sodium arsenate were more accurately identified in the synthetic HERFD-XANES spectra compared with the transmission-XANES spectra. In mine waste samples containing arsenopyrite and either scorodite or kaňkite, LCF with HERFD-XANES measurements resulted in fits with smaller R -factors than concurrently collected TFY measurements. The improved accuracy of HERFD-XANES analysis may provide enhanced delineation of As phases controlling biogeochemical reactions in mine wastes, contaminated soils, and remediation systems.

58 GEOSCIENCES↗
DOE OSTI2020

Quantifying Dynamic On-State Resistance of GaN HEMTs for Power Converter Design via a Survey of Low and High Voltage Devices

Designing and optimizing high switching frequency, ultra-efficient converters requires detailed knowledge of the behavior and parasitic parameters for both active and passive components. Recently, wide bandgap transistors have enabled simultaneous increases in both switching frequency and efficiency due to higher maximum operating junction temperature limits, lower dc on-state resistances, and reduced parasitic inductances and capacitances. Yet, the early acceptance of gallium-nitride (GaN) switches was plagued by detrimental dynamic on-state resistance effects. This complex phenomenon for GaN devices is characterized by deviations in on-state resistance from dc operating characteristics based on design choices such as the magnitude and duration of both voltage and current stress, switching mode, and junction temperature. While device manufacturers have made improvements compared to early generation devices, experimental evidence from a survey of commercial GaN transistors highlight measurable change in on-state resistance still exists due to variations in voltage stress during hard-switching operation. After sharing insights for obtaining low noise measurements, an analysis method along with two metrics are proposed to characterize dynamic on-state resistance measurements for power electronics designers. Furthermore, quantifying the performance of GaN devices with standardized metrics facilitates a fair comparison between different GaN device technologies during converter development, enables manufacturing qualification for GaN switches, and provides a benchmark to catalyze improvement for the next generation of GaN device development.

14 SOLAR ENERGY↗
DOE OSTI2024

Labeling sequential data from noisy annotations

Crowdsourcing algorithms often work under the assumption that the data samples are independent. Recent work has shown that data dependence, such as temporal correlations in sequential data, can be leveraged to improve the label quality. Existing methods that exploit this special structure rely on third-order statistics of the annotator outputs to ensure the identifiability of key latent parameters, which are costly to acquire. This work proposes an approach for integrating crowdsourced annotations under the Dawid-Skene/Hidden Markov Model (DS-HMM) for sequential data based on second-order statistics, which naturally enjoys a lower sample complexity. An effective algorithm is proposed to tackle the challenging optimization problem associated with the proposed estimator. Numerical experiments showcase the effectiveness of the data labeling paradigm.

Marrinan, Timothy P.↗
DOE OSTI2023

ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs

Over the last decade merge trees have been proven to support a plethora of visualization and analysis tasks since they effectively abstract complex datasets. Here, this paper describes the ExTreeM-Algorithm: A scalable algorithm for the computation of merge trees via extremum graphs. The core idea of ExTreeM is to first derive the extremum graph G of an input scalar field f defined on a cell complex K, and subsequently compute the unaugmented merge tree of f on G instead of K; which are equivalent. Any merge tree algorithm can be carried out significantly faster on G, since K in general contains substantially more cells than G. To further speed up computation, ExTreeM includes a tailored procedure to derive merge trees of extremum graphs. The computation of the fully augmented merge tree, i.e., a merge tree domain segmentation of K, can then be performed in an optional post-processing step. All steps of ExTreeM consist of procedures with high parallel efficiency, and we provide a formal proof of its correctness. Our experiments, performed on publicly available datasets, report a speedup of up to one order of magnitude over the state-of-the-art algorithms included in the TTK and VTK-m software libraries, while also requiring significantly less memory and exhibiting excellent scaling behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VoroClust

SAND2025-11465O VoroClust, also known as Voronoi Clustering, is a fast, density-based unsupervised clustering algorithm applicable to high-resolution and high-dimensional data. It operates as quickly as distance-based clustering methods while effectively capturing complex regional geometries, matching the performance of current density-based methods. VoroClust employs a data-centered sphere cover to reduce computational demands while preserving data topology. It propagates clusters outward from local density peaks. Although supervised machine learning is powerful for applications like image classification and segmentation, it requires comprehensive, consistent datasets, which many applications lack. Unsupervised clustering algorithms analyze the structure of each dataset rather than relying on similarities with other examples, making them well-suited for practical applications with insufficient or inappropriate data for supervised learning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Ebeida, Mohamed [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2026

CTGAN-TVAE

SAND2026-18914O CTGAN-TVAE (Conditional Tabular Generative Adversarial Networks-Tabular Variational Autoencoders) generates extensive sets of variable generation data through a hybrid framework. It enhances latent space representation by combining TVAE's robust feature-embedding with CTGAN's ability to condition categorical variables such as time. CTGAN-TVAE employs a fully connected neural network within a conditional generative adversarial network framework to manage continuous and categorical data effectively, capturing complex feature interactions without needing sequential modeling. This was developed as part of NNSA-MSIPP: Minority Serving Institution Partnership Program, Grant Number DE-NA0004016. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Newlun, Cody [Sandia National Lab. (SNL-CA), Liver↗
DOE OSTI2025

Equivariant Graph Attention Network - 3D Conformers & Feature Fusion

EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.

Shim, Heesung [Lawrence Livermore National Laborat↗