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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Antimicrobial properties of a multi-component alloy

High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates metallic alloys comprised of several metals which individually possess antimicrobial properties, with the target of achieving broad-spectrum, rapid sanitation through synergistic activity. An entropy-motivated stabilization paradigm is proposed to prepare scalable alloys of copper, silver, nickel and cobalt. Using combinatorial sputtering, thin-film alloys were prepared on 100 mm wafers with ≈50% compositional grading of each element across the wafer. The films were then annealed and investigated for alloy stability. Antimicrobial activity testing was performed on both the as-grown alloys and the annealed films using four microorganisms—Phi6, MS2, Bacillus subtilis and Escherichia coli —as surrogates for human viral and bacterial pathogens. Testing showed that after 30 s of contact with some of the test alloys, Phi6, an enveloped, single-stranded RNA bacteriophage that serves as a SARS-CoV-2 surrogate, was reduced up to 6.9 orders of magnitude (> 99.9999%). Additionally, the non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E. coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7 log reduction in activity after 30, 20 and 10 min, respectively. Antimicrobial activity in the alloy samples showed a strong dependence on the composition, with the log reduction scaling directly with the Cu content. Concentration of Cu by phase separation after annealing improved activity in some of the samples. The results motivate a variety of themes which can be leveraged to design ideal antimicrobial surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Approximation of nearly-periodic symplectic maps via structure-preserving neural networks

A continuous-time dynamical system with parameter ε is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as ε approaches 0. Nearly-periodic maps are discrete-time analogues of nearly-periodic systems, defined as parameter-dependent diffeomorphisms that limit to rotations along a circle action, and they admit formal U(1) symmetries to all orders when the limiting rotation is non-resonant. For Hamiltonian nearly-periodic maps on exact presymplectic manifolds, the formal U(1) symmetry gives rise to a discrete-time adiabatic invariant. In this paper, we construct a novel structure-preserving neural network to approximate nearly-periodic symplectic maps. This neural network architecture, which we call symplectic gyroceptron, ensures that the resulting surrogate map is nearly-periodic and symplectic, and that it gives rise to a discrete-time adiabatic invariant and a long-time stability. This new structure-preserving neural network provides a promising architecture for surrogate modeling of non-dissipative dynamical systems that automatically steps over short timescales without introducing spurious instabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2023

Improving nuclear data evaluations with predictive reaction theory and indirect measurements

Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Actinide oxide dissolution in tributyl phosphate

An alternative to dissolving used nuclear fuel (UNF) in an acidic solution during reprocessing is direct dissolution in an organic solution, which would eliminate an aqueous dissolution step, decrease the amount of nitrate needed, and reduce the facility size. The flowsheet for this potentially less expensive alternative is first to voloxidize the UNF to remove fission product gases and form an oxide. After voloxidation, the UNF is then dissolved in an organic solution containing an extractant mixed with an aliphatic diluent and pre-equilibrated with nitric acid. The organic solution then goes through a solvent extraction process to recover the uranium and/or other desired radionuclides. This work qualitatively studied the dissolution of actinide oxides (UO2, NpO2, and PuO2) in tributyl phosphate using UV-Vis-NIR absorbance spectroscopy to ascertain dissolution behavior. Initial studies included material that is otherwise difficult to dissolve in only nitric acid, specifically CeO2, that is sometimes used as a dissolution surrogate for PuO2. This work confirmed that CeO2, NpO2, and PuO2 are difficult to dissolve in 30 vol% TBP-dodecane pre-equilibrated with 10 M HNO3 and will readily dissolve when co-precipitated with U (i.e., the mixed oxides U-Ce, U-Np, and U-Pu), surrogates for voloxidized nuclear fuel.

Gogolski, Jarrod [Savannah River National Laborato↗
DOE OSTI2026

Construction of generalized quasilinear diffusion coefficient using neural networks with physical restrictions

The quasilinear diffusion coefficient (D QL ) derived from our machine learning framework shows comparable trends with the ground truth D QL obtained from GENRAY-CQL3D simulations. Additionally, for the strong absorption cases, the radial current drive profiles generated using the D QL from our model exhibit consistent behavior with those obtained from the original simulation. These findings indicate the potential of our surrogate modeling approach with physical restrictions to replicate key wave–plasma interaction characteristics while reducing computational costs. Traditionally, calculating D QL for wave–particle interactions relies on computationally intensive wave simulations coupled with Fokker–Planck solvers. To address this challenge, we developed a machine learning-based surrogate model with physical restrictions derived from cold plasma theory and bounce-averaged damping effects. First, we establish the propagation domain of Lower Hybrid Waves in the (N∥, ρ) space by identifying the accessibility limit and determining the upper and lower bounds of N∥ using the Potential Power Deposition (PPD) method. Subsequently, leveraging a database constructed using Latin hypercube sampling alongside the underlying physical restrictions (e.g. PPD), machine learning methods including U-Net and Recurrent Neural Networks are employed to design a physics-restricted machine learning framework capable of reconstructing D QL .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Qualitative comparison of bremsstrahlung X-rays and 800 MeV protons for tomography of urania fuel pellets

We present an assessment of x-rays and proton tomography as tools for studying the time dependence of the development of damage in fuel rods. We also show data taken with existing facilities at Los Alamos National Laboratory that support this assessment. Data on surrogate fuel rods have been taken using the 800 MeV proton radiography (pRad) facility at the Los Alamos Neutron Science Center (LANSCE), and with a 450 keV bremsstrahlung X-ray tomography facility. The proton radiography pRad facility at LANSCE can provide good position resolution (<70 {mu}m has been demonstrate, 20 {mu}m seems feasible with minor changes) for tomography on activated fuel rods. Bremsstrahlung x-rays may be able to provide better than 100 {mu}m resolution but further development of sources, collimation, and detectors is necessary for x-rays to deal with the background radiation for tomography of activated fuel rods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Shock timing measurements and analysis in deuterium-tritium-ice layered capsule implosions on NIF

Recent advances in shock timing experiments and analysis techniques now enable shock measurements to be performed in cryogenic deuterium-tritium (DT) ice layered capsule implosions on the National Ignition Facility (NIF). Previous measurements of shock timing in inertial confinement fusion implosions [Boehly et al., Phys. Rev. Lett. 106, 195005 (2011); Robey et al., Phys. Rev. Lett. 108, 215004 (2012)] were performed in surrogate targets, where the solid DT ice shell and central DT gas were replaced with a continuous liquid deuterium (D2) fill. These previous experiments pose two surrogacy issues: a material surrogacy due to the difference of species (D2 vs. DT) and densities of the materials used and a geometric surrogacy due to presence of an additional interface (ice/gas) previously absent in the liquid-filled targets. This report presents experimental data and a new analysis method for validating the assumptions underlying this surrogate technique. Comparison of the data with simulation shows good agreement for the timing of the first three shocks, but reveals a considerable discrepancy in the timing of the 4th shock in DT ice layered implosions. Electron preheat is examined as a potential cause of the observed discrepancy in the 4th shock timing.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

A magnetic particle time-of-flight (MagPTOF) diagnostic for measurements of shock- and compression-bang time at the NIF (invited)

A magnetic particle time-of-flight (MagPTOF) diagnostic has been designed to measure shock- and compression-bang time using D{sup 3}He-fusion protons and DD-fusion neutrons, respectively, at the National Ignition Facility (NIF). This capability, in combination with shock-burn weighted areal density measurements, will significantly constrain the modeling of the implosion dynamics. This design is an upgrade to the existing particle time-of-flight (pTOF) diagnostic, which records bang times using DD or DT neutrons with an accuracy better than ±70 ps [H. G. Rinderknecht et al., Rev. Sci. Instrum. 83, 10D902 (2012)]. The inclusion of a deflecting magnet will increase D{sup 3}He-proton signal-to-background by a factor of 1000, allowing for the first time simultaneous measurements of shock- and compression-bang times in D{sup 3}He-filled surrogate implosions at the NIF.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Development of the CD Symcap platform to study gas-shell mix in implosions at the National Ignition Facility

Surrogate implosions play an important role at the National Ignition Facility (NIF) for isolating aspects of the complex physical processes associated with fully integrated ignition experiments. The newly developed CD Symcap platform has been designed to study gas-shell mix in indirectly driven, pure T{sub 2}-gas filled CH-shell implosions equipped with 4 μm thick CD layers. This configuration provides a direct nuclear signature of mix as the DT yield (above a characterized D contamination background) is produced by D from the CD layer in the shell, mixing into the T-gas core. The CD layer can be placed at different locations within the CH shell to probe the depth and extent of mix. CD layers placed flush with the gas-shell interface and recessed up to 8 μm have shown that most of the mix occurs at the inner-shell surface. In addition, time-gated x-ray images of the hotspot show large brightly radiating objects traversing through the hotspot around bang-time, which are likely chunks of CH/CD plastic. This platform is a powerful new capability at the NIF for understanding mix, one of the key performance issues for ignition experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

A geophysical shock and air blast simulator at the National Ignition Facility

The energy partitioning energy coupling experiments at the National Ignition Facility (NIF) have been designed to measure simultaneously the coupling of energy from a laser-driven target into both ground shock and air blast overpressure to nearby media. The source target for the experiment is positioned at a known height above the ground-surface simulant and is heated by four beams from the NIF. The resulting target energy density and specific energy are equal to those of a low-yield nuclear device. The ground-shock stress waves and atmospheric overpressure waveforms that result in our test system are hydrodynamically scaled analogs of full-scale seismic and air blast phenomena. This report summarizes the development of the platform, the simulations, and calculations that underpin the physics measurements that are being made, and finally the data that were measured. Agreement between the data and simulation of the order of a factor of two to three is seen for air blast quantities such as peak overpressure. Historical underground test data for seismic phenomena measured sensor displacements; we measure the stresses generated in our ground-surrogate medium. We find factors-of-a-few agreement between our measured peak stresses and predictions with modern geophysical computer codes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

The effect of shock dynamics on compressibility of ignition-scale National Ignition Facility implosions

The effects of shock dynamics on compressibility of indirect-drive ignition-scale surrogate implosions, CH shells filled with D{sup 3}He gas, have been studied using charged-particle spectroscopy. Spectral measurements of D{sup 3}He protons produced at the shock-bang time probe the shock dynamics and in-flight characteristics of an implosion. The proton shock yield is found to vary by over an order of magnitude. A simple model relates the observed yield to incipient hot-spot adiabat, suggesting that implosions with rapid radiation-power increase during the main drive pulse may have a 2× higher hot-spot adiabat, potentially reducing compressibility. A self-consistent 1-D implosion model was used to infer the areal density (ρR) and the shell center-of-mass radius (R{sub cm}) from the downshift of the shock-produced D{sup 3}He protons. The observed ρR at shock-bang time is substantially higher for implosions, where the laser drive is on until near the compression bang time (“short-coast”), while longer-coasting implosions have lower ρR. This corresponds to a much larger temporal difference between the shock- and compression-bang time in the long-coast implosions (∼800 ps) than in the short-coast (∼400 ps); this will be verified with a future direct bang-time diagnostic. This model-inferred differential bang time contradicts radiation-hydrodynamic simulations, which predict constant 700–800 ps differential independent of coasting time; this result is potentially explained by uncertainties in modeling late-time ablation drive on the capsule. In an ignition experiment, an earlier shock-bang time resulting in an earlier onset of shell deceleration, potentially reducing compression and, thus, fuel ρR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Adiabat-shaping in indirect drive inertial confinement fusion

Adiabat-shaping techniques were investigated in indirect drive inertial confinement fusion experiments on the National Ignition Facility as a means to improve implosion stability, while still maintaining a low adiabat in the fuel. Adiabat-shaping was accomplished in these indirect drive experiments by altering the ratio of the picket and trough energies in the laser pulse shape, thus driving a decaying first shock in the ablator. This decaying first shock is designed to place the ablation front on a high adiabat while keeping the fuel on a low adiabat. These experiments were conducted using the keyhole experimental platform for both three and four shock laser pulses. This platform enabled direct measurement of the shock velocities driven in the glow-discharge polymer capsule and in the liquid deuterium, the surrogate fuel for a DT ignition target. The measured shock velocities and radiation drive histories are compared to previous three and four shock laser pulses. This comparison indicates that in the case of adiabat shaping the ablation front initially drives a high shock velocity, and therefore, a high shock pressure and adiabat. The shock then decays as it travels through the ablator to pressures similar to the original low-adiabat pulses when it reaches the fuel. This approach takes advantage of initial high ablation velocity, which favors stability, and high-compression, which favors high stagnation pressures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Intermittency, coherent structures and dissipation in plasma turbulence

Collisionless dissipation in turbulent plasmas such as the solar wind and the solar corona has been an intensively studied subject recently, with new insights often emerging from numerical simulation. Here we report results from high resolution, fully kinetic simulations of plasma turbulence in both two (2D) and three (3D) dimensions, studying the relationship between intermittency and dissipation. The simulations show development of turbulent coherent structures, characterized by sheet-like current density structures spanning a range of scales. An approximate dissipation measure is employed, based on work done by the electromagnetic field in the local electron fluid frame. This surrogate dissipation measure is highly concentrated in small subvolumes in both 2D and 3D simulations. Fully kinetic simulations are also compared with magnetohydrodynamics (MHD) simulations in terms of coherent structures and dissipation. The interesting result emerges that the conditional averages of dissipation measure scale very similarly with normalized current density J in 2D and 3D particle-in-cell and in MHD. To the extent that the surrogate dissipation measure is accurate, this result implies that on average dissipation scales as ∼J{sup 2} in turbulent kinetic plasma. Multifractal intermittency is seen in the inertial range in both 2D and 3D, but at scales ∼ion inertial length, the scaling is closer to monofractal.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Asymptotic behavior of the mixed mass in Rayleigh–Taylor and Richtmyer–Meshkov instability induced flows

Rayleigh–Taylor instability (RTI) and Richtmyer–Meshkov instability (RMI) are serious practical issues in inertial confinement fusion research, and also have relevance to many cases of astrophysical fluid dynamics. So far, much of the attention has been paid to the late-time scaling of the mixed width, which is used as a surrogate to how well the fluids have been mixed. Yet, the actual amount of mixed mass could be viewed as a more direct indicator on the evolution of the mixing layers due to hydrodynamic instabilities. Despite its importance, there is no systematic study as yet on the scaling of the mixed mass for either the RTI or the RMI induced flow. In this article, the normalized mixed mass (Ψ) is introduced for measuring the efficiency of the mixed mass. Six large numerical simulation databases have been employed: the RTI cases with heavy-to-light fluid density ratios of 1.5, 3, and 9; the single shock RMI cases with density ratios of 3 and 20; and a reshock RMI case with density ratio of 3. Using simulated flow fields, the normalized mixed mass Ψ is shown to be more sensitive in discriminating the variation with Atwood number for the RTI flows. Moreover, Ψ is demonstrated to provide more consistent results for both the RTI and RMI flows when compared with the traditional mixedness parameters, Ξ and Θ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗