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DOE OSTI2025

Simulating Muon Capture with MARLEY

Core-collapse supernovae are intense sources of tens-of-MeV neutrinos. Each explosion releases about 1058 neutrinos in a burst lasting tens of seconds. As neutrinos are weakly interacting, they escape more easily compared to photons, allowing them to be observed before the light signal. Understanding low-energy neutrino interactions with argon will be critical for future supernova measurements with the Deep Underground Neutrino Experiment (DUNE). Interpreting future DUNE supernova neutrino data will require a high-quality model of O(10 MeV) neutrino interactions, but the dominant charged-current cross section has never been measured at these low energies for an argon target. We exploit the theoretical similarities between muon capture, which has been measured, and antineutrino scattering to test MARLEY (Model of Argon Reaction Low Energy Yields), the simulation code used by DUNE to model supernova neutrino reactions. I implemented C++ code to add a muon capture mode to MARLEY and compared the predictions of the new simulation to measured isotope branching ratios of the residual nuclei. The preliminary branching ratios predicted by MARLEY running in muon capture mode demonstrate promising baseline performance when compared to experimental data and are expected to improve with further model adjustments.

Wong, Baker Louie↗

DOE OSTI2020

Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Development of Lightweight Structural Materials with Improved Properties for Fission Batteries

The notion of a “fission battery” conveys a vision focused on realizing very simple “plug-and-play” nuclear systems that can be integrated into a variety of applications requiring affordable, reliable energy in the form of electricity and/or heat and function without operations and maintenance staff. Fission batteries require lightweight structural materials to increase their mobility, and the lightweight materials must demonstrate structural resilience under various conditions. The objective of this work is to develop lattice structured lightweight structural material featuring a good combination of mechanical properties using advanced modeling and simulation together with an advanced additive manufacturing technique such as laser powder bed fusion. The preliminary results show that different lattice structures and types can be successfully meshed using nTopology software, and the lattice structure data can be successfully transformed to Multiphysics Object-Oriented Simulation (MOOSE) Environment input. Finite Element Analysis (FEA) displays that, at macro/engineering scale simulation, the weight saving design has an obvious effect on tensile behavior such as effective elastic modulus and yield stress. The novel approaches of this work are (1) development of lattice structures for improved mechanical properties using advanced simulation and modeling techniques; and (2) model predictions of the mechanical properties (e.g., strength and stress distribution) of macroscopic materials in order to preliminarily select a lattice structure for additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2024

COMPUTER-AIDED LATTICE DESIGN AND ADVANCED MODELING FOR THE DEVELOPMENT OF LIGHTWEIGHT STRUCTURAL MATERIALS

36 MATERIALS SCIENCE↗

DOE OSTI2023

Measuring and simulating ice–ablator mix in inertial confinement fusion

Fuel–ablator mix has been established as a major performance degrading effect in the burning plasma regime of recent inertial confinement fusion (ICF) experiments. As such, the study of fuel–ablator mix with experiments and simulations can provide valuable insight for our understanding of these experiments and establish a path for even higher yields and increased robustness. Here we present a novel high-yield experimental ICF design that is motivated by recent experiments measuring ice–ablator mix with a CH ablator instead of a high-density carbon (HDC) ablator. Here we review these experiments in more detail and describe the modeling assumptions and parameters used to obtain agreement with the data from implosion and burn simulations with mix. Using this mix model calibrated a posteriori to the experimental data, we design an implosion that uses a CH ablator that is predicted to achieve better performance than a recent experiment that achieved net target gain of 1.5 in HDC. Because hydrodynamic instabilities are greatly reduced with this new design, we also expect a high reproducibility at the same implosion adiabat as current record yield experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Circumventing thermodynamics to synthesize highly metastable perovskites: nano eggshells of SnHfO 3

Sn(II)-based perovskite oxides, being the subject of longstanding theoretical interest for the past two decades, have been synthesized for the first time in the form of nano eggshell particle morphologies. All past reported synthetic attempts have been unsuccessful owing to their metastable nature, i.e., by their thermodynamic instability towards decomposition to their constituent oxides. A new approach was discovered that finally provides an effective solution to surmounting this intractable synthetic barrier and which can be the key to unlocking the door to many other predicted metastable oxides. A low-melting KSn 2 Cl 5 salt was utilized to achieve a soft topotactic exchange of Sn(II) cations into a Ba-containing perovskite, i.e., BaHfO 3 with particle sizes of ~350 nm, at a low reaction temperature of 200 °C. The resulting particles exhibit nanoshell-over-nanoshell morphologies, i.e., with SnHfO 3 forming as ~20 nm thick shells over the surfaces of the BaHfO 3 eggshell particles. Formation of the metastable SnHfO 3 is found to be thermodynamically driven by the co-production of the highly stable BaCl 2 and KCl side products. Despite this, total energy calculations show that Sn(II) distorts from the A-site asymmetrically and randomly and the interdiffusion has a negligible impact on the energy of the system (i.e., layered vs. solid solution). Additionally, nano eggshell particle morphologies of BaHfO 3 were found to yield highly pure SnHfO 3 for the first time, thus circumventing the intrinsic ion-diffusion limits occurring at this low reaction temperature. In summary, these results demonstrate that the metastability of many theoretically predicted Sn(II)-perovskites can be overcome by leveraging the high cohesive energies of the reactants, the exothermic formation of a stable salt side product, and a shortened diffusion pathway for the Sn(II) cations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Lipid production from non-sugar compounds in pretreated lignocellulose hydrolysates by Rhodococcus jostii RHA1

Valorization of all carbon sources from biorefinery wastes to biofuels and bioproducts is an attractive but challenging strategy to improve the carbon efficiency of the entire biorefinery process for economic competitiveness. Rhodococcus jostii RHA1 is considered as a promising candidate for conversion of non-sugar compounds in pretreated lignocellulose hydrolysate, which contain aromatic monomers, furans and acids derived from biomass. However, the insufficient understanding of the strain’s complex metabolism network limits its application. In this study, furans (furfural, 5-hydroxymethylfurfural), phenols (vanillin, vanillate), and organic acids (acetic acid), which mimic the complex components of the non-sugar compounds in pretreated biomass hydrolysate, were mixed with benzoate or used individually as carbon sources to investigate the growth and lipid accumulation of R. jostii RHA1. Fermentation kinetics, specific growth rates (h-1) and lipid yield (g(g) -1, g(mmol)-1), for each selected biomass-derived compound were compared for lipid production. The order of the specific growth rates (h-1) was benzoate > acetic acid > vanillate > vanillin. Higher consumption rate of benzoate than that of vanillate, as well as different lipid yield from them, suggested the preference of strain employing two branches of ß-ketoadipate pathway and plausibly distinct carbon routing to lipid biosynthesis. And acetic acid showed the lowest lipid yield (g(mmol)-1) indicating less carbon contributed to lipid production. The highest lipid content (0.46 g lipid/g CDW) was achieved using benzoate as a main carbon source in the presence of 5-hydroxymethylfurfural (5-HMF). The generated lipids predominantly contained C16:0 and C17:0 fatty acids. The concentration of furfural and 5-HMF in fermentation broth decreased individually or simultaneously with benzoate catabolism. NMR results revealed the oxidation of furfural and 5-HMF by R. jostii RHA1. This study provides new insights into the catabolism and metabolism of non-sugar compounds derived from biomass by Rhodococcus in terms of growth and lipid production, facilitating its application to 2nd generation biofuel production.

Li, Xiaolu↗

DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The effect of rare-earth substitution on the Debye temperature of inorganic phosphors

In the quest to predict photoluminescent efficiency in rare-earth substituted inorganic phosphors, research has shown that materials consisting of ordered, rigid crystal structures tend to possess the highest photoluminescent quantum yields. A compound's Debye temperature (Θ D ), which can be calculated using ab initio calculations, is an ideal proxy for quantitatively comparing structural rigidity among different inorganic compounds, allowing potentially efficient phosphors to be selected from large crystal structure databases. However, the high computational cost of these calculations limits estimating Θ D for unsubstituted host crystal structures only. It is assumed that the low substitution concentration of the rare-earth luminescent center does not significantly influence a material's Debye temperature. This work evaluates the validity of this approximation by examining the effect of luminescent center substitution on a host structure's Θ D . Two well-known phosphors, (Y 1– x Ce x ) 3 Al 5 O 12 ( x = 0 – 0.05) and Ba 1-δ Eu δ MgAl 10 O 17 (δ = 0 – 0.15), were synthesized with varying rare-earth concentrations, while Θ D was computationally estimated and then determined by ultrasonic pulse-echo speed-of-sound and low-temperature heat capacity measurements. Overall, the ensuing results provide key implications for using Θ D as a proxy for structural rigidity in substituted inorganic compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

High-volume and -adiabat capsule (“HVAC”) ignition: Lowered fuel compression requirements using advanced Hohlraums

Lower-than-expected deuterium–tritium fuel areal densities have been experimentally inferred across a variety of high-convergence, nominally low-adiabat implosion campaigns at the National Ignition Facility (NIF) using cylinder-shaped Hohlraums [Hurricane et al., Phys. Plasmas 26, 052704 (2019)]. A leading candidate explanation is the presence of atomic mix between the fuel and ablator from hydrodynamic instability growth [Clark et al., Phys. Plasmas 26, 050601 (2019)], leading to reduced fuel compressibility and an effectively higher (in-flight) fuel adiabat α. Tolerating a high-α implosion can be obtained with significantly higher capsule absorbed energy Ecap according to the one-dimensional (1-D) ignition-threshold-factor analytic scaling [S. Atzeni and J. Meyer-ter-Vehn, Nucl. Fusion 41, 465 (2001)], ITF∼Ecap·α−1.8. Recent experiments with large Al shells in rugby-shaped Hohlraums have established high laser-capsule coupling efficiencies of ≽ 30% [Ping et al., Nat. Phys. 15, 138 (2019)], enabling a path to Ecap≽ 0.5 MJ at the NIF and increased performance margin M ≡ ITF − 1. The ability to operate at high adiabat with large capsules using nonstandard Hohlraums leads to the predicted onset of a volume-ignition mode, defined as when both the entire fuel is the “hot spot” and inertial confinement is principally provided by the ablator compared with the compressed fuel. Such an ignition mode, normally reserved for high-Z targets, e.g., double shells [Amendt et al., Phys. Plasmas 14, 056312 (2007)], is predicted to lead to lower fuel convergence and less exposure to mix due to the intended high adiabat—but at the expense of ∼3–4 × reduced (1-D) yield compared with conventional central hot-spot ignition designs.

Amendt, Peter (ORCID:0000000174021777)↗

DOE OSTI2024

A reactive molecular dynamics model for uranium/hydrogen containing systems

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. Here, we show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Theoretical description of fission yields: Toward a fast and efficient global model

A recently proposed approach to global modeling of fission fragment distributions is extended to account for odd-even staggering in charge yields and for neutron evaporation. We show that the proposed hybrid model can reproduce experimental mass and charge fragment yields, including the odd-even staggering for a wide range of fissioning nuclei. Experimental isotopic yields can be described within a simple neutron evaporation scheme. Here, we also explore fission fragment distributions of exotic neutron-rich and superheavy systems and compare our predictions with other state-of-the art global calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Quantitative measurements of reactive oxygen species partitioning in electron transfer flavoenzyme magnetic field sensing

Biological magnetic field sensing that gives rise to physiological responses is of considerable importance in quantum biology. The radical pair mechanism (RPM) is a fundamental quantum process that can explain some of the observed biological magnetic effects. In magnetically sensitive radical pair (RP) reactions, coherent spin dynamics between singlet and triplet pairs are modulated by weak magnetic fields. The resulting singlet and triplet reaction products lead to distinct biological signaling channels and cellular outcomes. A prevalent RP in biology is between flavin semiquinone and superoxide (O 2 •–) in the biological activation of molecular oxygen. This RP can result in a partitioning of reactive oxygen species (ROS) products to form either O 2 •– or hydrogen peroxide (H 2 O 2 ). Here, we examine magnetic sensing of recombinant human electron transfer flavoenzyme (ETF) reoxidation by selectively measuring O 2 •– and H 2 O 2 product distributions. ROS partitioning was observed between two static magnetic fields at 20 nT and 50 μT, with a 13% decrease in H 2 O 2 singlet products and a 10% increase in O 2 •– triplet products relative to 50 µT. RPM product yields were calculated for a realistic flavin/superoxide RP across the range of static magnetic fields, in agreement with experimental results. For a triplet born RP, the RPM also predicts about three times more O 2 •– than H 2 O 2 , with experimental results exhibiting about four time more O 2 •– produced by ETF. The method presented here illustrates the potential of a novel magnetic flavoprotein biological sensor that is directly linked to mitochondria bioenergetics and can be used as a target to study cell physiology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Reaction Temperature Manipulation as a Process Intensification Approach for CO 2 Absorption

Reactor temperature manipulation to increase product yields of chemical reactions is a known technique used in many industrial processes. In the case of exothermic chemical reactions, the well-known Le Chatelier’s principle predicts that a decrease in temperature will displace the chemical reaction toward the formation of products by increasing the value of the equilibrium constant. The reverse is true for endothermic reactions. Reactor temperature manipulation in an industrial system, however, affects the values of many variables, including physical properties, transport parameters, reaction kinetic parameters, etc. In the case of reactive absorption, some variables change with increasing temperatures due to solute absorption, while others change in such a way that the solute absorption rate decreases. For example, temperature drop increases product formation for exothermic reactions but reduces the value of transport parameters, leading to decreasing interfacial concentrations and absorption rates. Therefore, temperature manipulation strategies must be designed carefully to achieve the process goals. In this work, we theoretically study the use of temperature as a tool to increase CO 2 absorption by solvents in a semi-batch reactor. A computer code has been developed and validated using reported experimental data. Calculated results demonstrate an increase in absorbed CO 2 of more than 28% with respect to the highest temperature used. Despite high agitation and high gas flow rate, the system is mass transfer controlled at short times, becoming kinetically controlled as time increases. An operating strategy to decrease cooling energy costs is also proposed. This study reveals that reactor temperature manipulation can be an effective process to improve CO 2 absorption by solvents in two-phase semi-batch reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Impact of Cosmic Filaments on Galaxy Morphological Evolution and Predictions of Early Cosmic Web Structure for Roman

We leverage the IllustrisTNG cosmological simulations to test how the large-scale cosmic web shapes galaxy morphology and to forecast the early cosmic web structure that the Nancy Grace Roman Space Telescope will reveal. In the hydrodynamic TNG50 and N-body TNG50-Dark runs, we reconstruct the cosmic web at redshifts z = 0, 0.5, 1, 2, 3, and 4 with the Monte Carlo Physarum Machine density estimator and the DisPerSE structure identification framework. We confirm that dark matter halos start out predominantly prolate (elongated), and their shapes are aligned with their nearest filaments; prolate galaxies retain strong shape alignment with their outer halos to later times. At z ≥ 1, the fraction of prolate (spheroidal) halos increases (decreases) toward lower stellar mass, higher redshift, and lower filament density. At z < 1, more spheroidal (oblate) stellar structures preferentially reside in higher-density (lower-density) filaments. We also find that higher-density filaments favor extended rotationally supported disks, whereas lower-density filaments more often host smaller dispersion-supported systems. Then, generating mock galaxy samples from TNG100 and TNG50, we predict the early cosmic web accessible to Roman. We find that the spectroscopic emission-line depth planned for the High-Latitude Wide-Area Survey (HLWAS) yields a highly incomplete galaxy sample that does not accurately trace the z = 1 cosmic web. A survey ≥2.5× deeper over a few square degrees would enable a proper reconstruction and reveal qualitatively correct filament–galaxy morphology relationships. Nevertheless, the planned HLWAS Deep field should still identify most galaxy overdensities; targeted deeper spectroscopy of these regions would efficiently and adequately map the early filamentary structure.

Hasan, Farhanul [Space Telescope Science Institute↗

DOE OSTI2024

Development of Lightweight Structural Materials with Improved Properties for Fission Batteries

The notion of a “fission battery” conveys a vision focused on realizing very simple “plug-and-play” nuclear systems that can be integrated into a variety of applications requiring affordable, reliable energy in the form of electricity and/or heat and function without operations and maintenance staff. Fission batteries require lightweight structural materials to increase their mobility, and the lightweight materials must demonstrate structural resilience under various conditions. The objective of this work is to develop lightweight structural material featuring a good combination of mechanical properties using advanced modeling and simulation together with an advanced additive manufacturing technique such as laser powder bed fusion. The preliminary results show that different lattice structures and types can be successfully meshed using nTopology software, and the lattice structure data can be successfully transformed to Multiphysics Object-Oriented Simulation Environment (MOOSE) input. Finite Element Analysis (FEA) displays that, at macro/engineering scale simulation, the weight saving design has an obvious effect on tensile behavior such as effective elastic modulus and yield stress. The novel approaches of this work are (1) development of optimized lattice structures for improved mechanical properties using advanced simulation and modeling techniques; and (2) model predictions of the mechanical properties (e.g., strength and stress distribution) of macroscopic materials in order to preliminarily select a lattice structure for additive manufacturing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Cyanobacterial photosystem II reaction center design in tobacco chloroplasts increases biomass in low light

Abstract The D1 polypeptide of the photosystem II (PSII) reaction center complex contains domains that regulate primary photochemical yield and charge recombination rate. Many prokaryotic oxygenic phototrophs express two or more D1 isoforms differentially in response to environmental light needs, a capability absent in flowering plants and algae. We report that tobacco (Nicotiana tabacum) plants carrying the Synechococcus (Synechococcus elongatus PCC 7942) low-light mutation (LL-E130Q) in the D1 polypeptide (NtLL) acquire the cyanobacterial photochemical phenotype: faster photodamage in high light and significantly more charge separations in productive linear electron flow in low light. This flux increase produces 16.5% more (dry) biomass under continuous low-light illumination (100 μE m−2 s−1, 24 h). This gain is offset by the predicted lower photoprotection at high light. By contrast, the introduction of the Synechococcus high-light mutation (HL-A152S) into tobacco D1 (NtHL) has slightly increased photoprotection, achieved by photochemical quenching, but no apparent impact on biomass yield compared to wild type under the tested conditions. The universal design principle of all PSII reaction centers trades off energy conversion for photoprotection in different proportions across all phototrophs and provides a useful guidance for testing in crop plants. The observed biomass advantage under continuous low light can be transferred between evolutionarily isolated lineages to benefit growth under artificial lighting conditions. However, removal of the selective marker gene was essential to observe the growth phenotype, indicating growth penalty imposed by use of the particular spectinomycin-resistance gene.

Zhang, Yuan (ORCID:0000000291785207)↗