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DOE OSTI2024

Status of the Spallation Neutron Source beam test facility and progress of beam dynamics studies

The Spallation Neutron Source (SNS) Beam Test Facility (BTF) supports the study of beam dynamics in the front end of a high power LINAC. The BTF combines a replica of the SNS front end, including nearly-identical ion source, RFQ and MEBT, with extensive phase space diagnostics and a FODO transport line. Diagnostic capabilities include direct measurement of 6D phase space distribution and detection of halo distributions to a sensitivity of greater than one part-per-million. The goal of on-going BTF studies is to demonstrate accurate particle-in-cell modeling of halo growth and evolution by leveraging unprecedented accuracy in the description of the initial beam distribution. This work is motivated by operational experience at the SNS, which currently operates with beam loss that cannot be described by any model. This paper summarizes progress in the BTF beam study program as well as diagnostics development and recent upgrades to the beamline configuration.

Ruisard, Kiersten↗

DOE OSTI2023

Understanding the evolution of double perovskite band structure upon dimensional reduction

Recent investigations into the effects of dimensional reduction on halide double perovskites have revealed an intriguing change in band structure when the three-dimensional (3D) perovskite is reduced to a two-dimensional (2D) perovskite with inorganic sheets of monolayer thickness (n = 1). The indirect bandgap of 3D Cs 2 AgBiBr 6 becomes direct in the n = 1 perovskite whereas the direct bandgap of 3D Cs 2 AgTlBr 6 becomes indirect at the n = 1 limit. Here, we apply a linear combination of atomic orbitals approach to uncover the orbital basis for this bandgap symmetry transition with dimensional reduction. We adapt our previously established method for predicting band structures of 3D double perovskites for application to their 2D congeners, emphasizing new considerations required for the 2D lattice. In particular, we consider the inequivalence of the terminal and bridging halides and the consequences of applying translational symmetry only along two dimensions. The valence and conduction bands of the layered perovskites can be derived from symmetry adapted linear combinations of halide p orbitals propagated across the 2D lattice. The dispersion of each band is then determined by the bonding and antibonding interactions of the metal and halide orbitals, thus affording predictions of the essential features of the band structure. We demonstrate this analysis for 2D Ag–Bi and Ag–Tl perovskites with sheets of mono- and bilayer thickness, establishing a detailed understanding of their band structures, which enables us to identify the key factors that drive the bandgap symmetry transitions observed at the n = 1 limit. Importantly, these insights also allow us to make the general prediction that direct → indirect or indirect → direct bandgap transitions in the monolayer limit are most likely in double perovskite compositions that involve participation of metal d orbitals at the band edges or that have no metal-orbital contributions to the valence band, laying the groundwork for the targeted realization of this phenomenon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Disentangling multiple gas kinematic drivers in the Perseus galaxy cluster

Galaxy clusters, the Universe’s largest halo structures, are filled with an X-ray-emitting gas with a temperature between 10 million and 100 million degrees. Their evolution is shaped by energetic processes such as feedback from supermassive black holes (SMBHs) and mergers with other cosmic structures. The imprints of these processes on gas kinematics remain largely unknown, restricting our understanding of energy conversion within clusters4. High-resolution spectral mapping with the X-Ray Imaging and Spectroscopy Mission (XRISM) observatory offers a way forward. Here we present XRISM kinematic measurements of the Perseus cluster, radially covering the extent of its cool core. We find direct evidence for at least two dominant drivers of gas motions operating on distinct physical scales: a small-scale driver in the inner approximately 60 kpc, probably associated with the SMBH feedback; and a large-scale driver in the outer core, powered by mergers. This finding suggests that, during the active phase, SMBH feedback drives gas motions, which, if fully dissipated into heat, could have a substantial role in offsetting radiative cooling losses in the Perseus core. Furthermore, our study underscores the necessity of kinematic mapping observations of extended sources to robustly characterize the velocity fields and their role in the evolution of massive halos. It further offers a kinematic diagnostic for SMBH feedback models.

Astronomy and AstroPhysics↗

DOE OSTI2024

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Defect-free-induced Na + disordering in electrode materials

For reaching high-performance of electrode materials, it is generally believed that understanding the structure evolution and heterogeneous alignment effect is the key. Presently, a very simple and universally applicable self-healing method is investigated to prepare defect-free Prussian blue analogs (PBAs) that reach their theoretical capacity as cathode materials for sodium-ion batteries (SIBs). For direct imaging of the local structure and the dynamic process at the atomic scale, we deliver a fast ion-conductive nickel-based PBA that enables rapid Na + extraction/insertion within 3 minutes and a capacity retention of nearly 100% over 4000 cycles. This guest-ion disordered and quasi-zero-strain nonequilibrium solid–solution reaction mechanism provides an effective guarantee for realizing long-cycle life and high-rate capability electrode materials that operate via reversible two-phase transition reaction. Unconventional materials and mechanisms that enable reversible insertion/extraction of ions in low-cost metal–organic frameworks (MOFs) within minutes have implications for fast-charging devices, grid-scale energy storage applications, material discovery, and tailored modification.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs), especially with arbitrary initial data. We address this problem by taking advantage of recent advances in scientific machine learning and the spectral dynamically orthogonal (DO) and borthogonal (BO) methods for representing stochastic processes. The recently introduced DO/BO methods reduce the SPDE to solving a system of deterministic PDEs and a system of stochastic ordinary differential equations. Specifically, we propose two new physics-informed neural networks (PINNs) for solving time-dependent SPDEs, namely the neural network (NN)-DO/BO methods. The proposed methods incorporate the DO/BO constraints into the loss function (along with the modal decomposition of the SPDE) with an implicit form instead of generating explicit expressions for the temporal derivatives of the DO/BO modes. Hence, the NN-DO/BO methods can overcome some of the drawbacks of the original DO/BO methods. For example, we do not need the assumption that the covariance matrix of the random coefficients is invertible as in the original DO method, and we can remove the assumption of no eigenvalue crossing as in the original BO method. Moreover, the NN-DO/BO methods can be used to solve time-dependent stochastic inverse problems with the same formulation and same computational complexity as for forward problems. Furthermore, we demonstrate the capability of the proposed methods via several numerical examples, namely: (1) A linear stochastic advection equation with deterministic initial condition: we obtain good results with the proposed methods, while the original DO/BO methods cannot be applied directly in this case. (2) Long-time integration of the stochastic Burgers' equation: we show the good performance of NN-DO/BO methods, especially the effectiveness of the NN-BO approach for such problems with many eigenvalue crossings during the whole time evolution, while the original BO method fails. (3) Nonlinear reaction diffusion equation: we consider both the forward problem and the inverse problems, including very noisy initial point values, to investigate the flexibility of the NN-DO/BO methods in handling inverse and mixed type problems. Taken together, these simulation results demonstrate that the NN-DO/BO methods can be employed to effectively quantify uncertainty propagation in a wide range of physical problems, but future work should address the efficiency issue of PINNs for forward problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Analytically Derived Piezoelectric Ejecta Data Transformations, with Implications for Claims of Experimentally Observed Self-Similarity

Building upon a previously documented analytic formalism, we derive a set of data transformations that relate piezoelectrically inferred time-dependent ejecta masses, dynamical ejecta cloud densities, and "radiographic" ejecta cloud density distributions. These transformation rules are derived rigorously, with careful attention to the assumption of instant ejecta production which is inherent in the piezoelectric mass inference procedure. Some of these relationships have not appeared previously in the literature, including solutions for the unique ejecta velocity distribution and radiographic ejecta cloud density distribution consistent with the instant-production assumption for a given piezoelectrically inferred mass dataset. In deriving the latter, we prove that—again subject to the ubiquitous instant-creation assumption—measurements at a single sensor are sufficient to determine properties of the ejecta cloud everywhere at all times. Other data transformation relationships have appeared in the literature without a rigorous derivation, or have been stated in forms that obscure the key mathematical relationships. We apply these results to arguments for self-similar ejecta cloud evolution, thereby deriving mathematical expressions by which claims of self-similarity may be examined. We prove that one analysis used in the literature to claim experimental evidence of a short ejecta production timescale—and consequently of self-similar cloud evolution—in fact places no constraint the ejecta production interval and therefore does not support the associated conclusion. We also provide a potentially useful uniqueness proof.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

Challenge Problem 1: Preliminary Results of the Direct Numerical Simulation of Transient Flows

This report presents the first direct numerical simulations (DNS) of transient mixed convection in an idealized downcomer-like channel (Challenge Problem 1, Phase II). Using the GPU-accelerated NekRS solver, we modeled a sudden decay in driving pressure, mimicking loss-of-flow events, and tracked the resulting evolution of Reynolds number, boundary-layer structure, turbulence statistics, and heat-transfer metrics. Key findings include the systematic thickening and eventual asymmetry of velocity and thermal boundary layers under buoyant deceleration; minimal “memory” lag in Reynolds shear stress and TKE profiles when sampled at matching Re, yet clear shifts of peak locations toward the cooled wall; overshoots in transient eddy-viscosity and eddy-diffusivity (and corresponding sub-unity turbulent Prandtl numbers) on the cooled side; and a pronounced transient Nusselt-number enhancement driven by wall-temperature inertia and residual eddy mixing. These effects combined to offer a temporary cooling margin above steady-state predictions during reactor LOF transients. Future work will extend this work to a more complex “Case II” geometry (90° turn + lower plenum) and generate multi-Re/Pr datasets for data-driven turbulence closures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Nucleation and growth of voids in shock loaded copper bicrystals

Understanding the evolution of damage and deformation due to spall at grain boundaries can provide a basis for connecting micro- to macroscale failure behavior in metals under extreme conditions. Copper bicrystal samples were shock loaded using flyer-plate impacts in a light gas gun with shock stresses ranging from 3 to 6 GPa. Pulse duration as well as crystal orientation along the shock direction were varied for a fixed boundary misorientation to determine their effects on void nucleation and coalescence. Samples were soft recovered and cross-sectioned to characterize damage using electron backscattering diffraction and scanning electron microscopy to gather information on damage characteristics at and around the GB, with emphasis on growth of boundary and bulk voids. Chemistry and composition analysis were also performed on samples to determine if trace elements present in a sample affected the threshold for void nucleation. Results show that the kinetics of damage growth at the boundary are strongly affected by stress level and impurities. It was found that the boundary selected had a similar or even lower tendency to show damage than the bulk at low pulse durations and amplitudes. As pulse duration and amplitude increased damage localized at the boundary, which was found to consist of many small voids, indicating that the boundary experienced rapid void nucleation and coalescence. Furthermore, the presence of impurities correlated strongly with scatter on damage evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Discharge Rate‐Driven Li 2 O 2 Growth Exhibits Unconventional Morphology Trends in Solid‐State Li‐O 2 Batteries

Solid-state lithium oxygen batteries (LOBs) are known for their enhanced safety, higher electrochemical stability, and improved energy density compared to liquid-state LOBs. However, the investigation of solid-state LOBs is limited with little understanding of their discharge and charge processes. In this work, a polymer-based solid-state LOB is used to investigate the effect of discharge rate on lithium peroxide (Li 2 O 2 ) formation, the oxygen evolution reaction (OER), and cycle performance. Notably, we observe a counterintuitive trend: Li 2 O 2 particle size increases with increasing discharge current density, in contrast to liquid systems. This behavior arises from inherent space charge layers that restrict Li⁺ transport under high current, and spatially heterogeneous active sites at the solid electrolyte–cathode interface, directly evidenced by small angle X-ray scattering (SAXS), which govern nucleation accessibility and promote site-selective Li 2 O 2 growth. Furthermore, higher current densities improve ORR and OER efficiency but accelerate anode degradation, while lower currents promote side reactions. These opposing effects result in a trade-off that defines an optimal discharge rate (0.1 mA cm -2 ) for maximizing cycle life. This study provides a new mechanistic perspective on discharge-driven processes in solid-state LOBs and offers practical guidelines for performance optimization in future high-energy battery systems.

Discharge current density↗

DOE OSTI2023

Opportunities to advance the synthesis of ecology and evolution

Despite decades of research on the interactions between ecology and evolution, opportunities still remain to further integrate the two disciplines, especially when considering multispecies systems. Here, we discuss two such opportunities. First, the traditional emphasis on the distinction between evolutionary and ecological processes should be further relaxed as it is particularly unhelpful in the study of microbial communities, where the very notion of species is hard to define. Second, key processes of evolutionary theory such as adaptation should be exported to hierarchical levels higher than populations to make sense of biodiversity dynamics. Together, we argue that broadening our perspective of eco-evolutionary dynamics to be more inclusive of all biodiversity, both phylogenetically and hierarchically, will open up fertile new research directions and help us to address one of the major scientific challenges of our time, that is, to understand and predict changes in biodiversity in the face of rapid environmental change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

AMMT 2025 Milestone

Idaho National Laboratory initiated the examination of nickel-based alloys manufactured laser powder directed energy deposition additive manufacturing for potential applications in nuclear, high temperature structural components. With the rapid push towards additive manufacturing, codes do not exist that definitively define what is or is not tolerable for each process and application, such as with conventional, wrought products. This report contains the initial work to understand possible manufacturing methods for high temperature alloys, and specifically, void formation, microstructure evolution, and mechanical properties. To generate mechanical test data, specimens were tested irrespective of voids and microstructures were analyzed to better understand how to negate/improve these issues. The preliminary results showed major decreases in mechanical performance for material tested. Test specimens will continue to be produced to further improve additive manufacturing processes, quantify void acceptance, and better understand the most suitable high temperature alloys receptive to additive manufacturing and high temperature nuclear applications.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Modulating Cu electrode microenvironments with MOF coatings: insights from molecular dynamics and electrochemical experiments of CO reduction

Metal-organic frameworks (MOFs) present a compelling strategy for tuning electrochemical interfaces by reshaping interfacial solvent structure. In this study, we examine how MOF coatings influence the microenvironment at copper electrodes during the CO electroreduction reaction (CORR) using a combined approach of molecular dynamics (MD) simulations and electrochemical experiments. Two MOFs, NU-901 and ZIF-8, are selected to explore the impact of pore size and channel hydrophobicity on electrochemical activity and interfacial concentration in acetonitrile (ACN) and dimethyl sulfoxide (DMSO) electrolytes. Electrochemical measurements reveal that MOF@Cu electrodes exhibit lower Faradaic efficiencies for CO hydrogenation products (ethylene and methane) compared to bare copper but have dramatic impacts on the interfacial microenvironment. NU-901, with its larger pores and strong interactions with DMSO, traps DMSO molecules and enhances CO coordination in DMSO but suppresses CORR selectivity in favor of the hydrogen evolution reaction (HER). ZIF-8, with smaller pores and hydrophobic channels, limits the interfacial water concentration, and, in ACN, promotes CO coordination. The simulations provide insights into how MOFs can act as physical modulators of reactant delivery and interfacial structure to control electrochemical microenvironments. This work highlights the value of molecular dynamics in uncovering how structural features of MOFs influence interfacial phenomena, even when catalytic performance is not directly improved.

Copper electrode microenvironments↗

DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗

DOE OSTI2024

Standardization Gaps in Powder Feedstock Characterization and Establishing Acceptability for Reuse in Additive Manufacturing

Characterization techniques for powder feedstocks used in additive manufacturing (AM) have long been relied upon to describe the inputs to an AM workflow. However, functional gaps remain between tests to measure intrinsic and extrinsic properties with the direct performance within AM equipment. Furthermore, the common practice of reusing powder through multiple build cycles introduces effects and changes to feedstock performance that are otherwise difficult to measure quantitatively. Here, standardization and the development of new test methods have not kept pace with the rapid evolution of the AM industry and its reliance on highly coupled process-structure–property-performance relationships.

Characterization and Analytical Technique↗

DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗

DOE OSTI2020

Self-grown NiCuO x hybrids on a porous NiCuC substrate as an HER cathode in alkaline solution

Electrocatalysts converted directly from the substrates hold the key to achieve high catalytic activity and durability due to their high bonding strength, intimate electronic contact, and tunable phase composition. Herein, an electrically conductive porous NiCuC substrate was adopted to develop a multi-porous NiCuO x /NiCuC hydrogen evolution reaction (HER) catalyst using a one-step oxidation method in this research. The NiCuO x hybrids were characterized as three typical layers: an outer layer with NiCuO 2 solid solution phase, a partially oxidized intermediate layer, and an inner layer with limited stable oxygen content. Here, the NiCuO x /NiCuC possesses abundant Ni(II)–Cu(II) sites to accelerate the Volmer step via the electrostatic effect with OH – , numerous metallic NiCu sites to facilitate the H adsorption, and highly accessible channels to promote the Heyrovsky step during HER catalysis. As a result, high electrocatalytic performance was obtained with an overpotential of 116 mV at a current density of 10 mA cm –2 in 1.0 M potassium hydroxide, and a stable catalytic performance during the HER process for more than 24 h.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Oblique and rippled heliosphere structures from the Interstellar Boundary Explorer

Abstract Past analysis has shown that the heliosphere structure can be deduced from correlations between long-scale solar wind pressure evolution and energetic neutral atom emissions. However, this required spatial and temporal averaging that smoothed out small or dynamic features of the heliosphere. In late 2014, the solar wind dynamic pressure increased by roughly 50% over a period of 6 months, causing a time and directional-dependent rise in around 2–6 keV energetic neutral atom fluxes from the heliosphere observed by the Interstellar Boundary Explorer. Here, we use the 2014 pressure enhancement to provide a simultaneous derivation of the three-dimensional heliospheric termination shock (HTS) and heliopause (HP) distances at high resolution from Interstellar Boundary Explorer measurements. The analysis reveals rippled HTS and HP surfaces that are oblique with respect to the local interstellar medium upwind direction, with significant asymmetries in the heliosphere structure compared to steady-state heliosphere models. We estimate that the heliosphere boundaries contain roughly ten astronomical unit-sized spatial variations, with slightly larger variations on the HTS surface than the HP and a large-scale, southwards-directed obliquity of the surfaces in the meridional plane. Comparisons of the derived HTS and HP distances with Voyager observations indicate substantial differences in the heliosphere boundaries in the northern versus southern hemispheres and their motion over time.

79 ASTRONOMY AND ASTROPHYSICS↗