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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗
DOE OSTI2024

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multiscale Interactions between Local Short- and Long-Term Spatio-Temporal Mechanisms and Their Impact on California Wildfire Dynamics

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ -0.7, p ≤ 0.05).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

In situ catalyst activation and regeneration enable energy-efficient high-current CO 2 reduction to ethanol-rich C 2+ mixtures

Electrochemical conversion of dissolved CO 2 in bicarbonate electrolytes, i.e., bicarbonate electrolysis, offers distinct advantages over gas diffusion electrode systems by enabling direct utilization of the CO 2 capture electrolyte while bypassing the energy-intensive CO 2 release step. However, bicarbonate electrolysis faces challenges such as CO 2 mass-transfer limitation, local pH-driven CO 2 depletion, and high cathodic potentials. The higher potential often causes catalyst surface reorganization, leading to a gradual loss of active sites and variations in selectivity during CO 2 reduction. Here, we report a directed, in situ activation and regeneration method that allows precatalysts to equilibrate under dynamic (pulsed) electrolysis conditions. We demonstrate in situ activation of a scalable Cu 2 O/Cu mesh that, under short-width (t = 4 s) pulsed electrolysis, provides stable mixed oxidation states of Cu, favoring the formation of an ethanol-rich crude mixture. The pulsed electrolysis waveform, consisting of six distinct segments, is tuned to form Cu + oxides, which are then reduced to generate local alkaline conditions favoring C–C coupling. This synergistic effect results in FEs of 73% for C2+ products and 39% for ethanol at an applied current density of −150 mA cm −2 and a cathodic potential of −1.45 V (vs. RHE). The overall half-cell energy efficiency is ∼30% for C 2+ products. The in situ Raman experiments confirm the role of pCO 2 R in dynamically regenerating Cu+-containing surface species during pulsed operation, thereby steering selectivity towards C 2+ products. A comprehensive multiscale, multiphysics model is developed to investigate the dynamic behavior of copper surface species (Cu, Cu + , and Cu 2+ ) and local microenvironmental conditions during the pCO 2 R. The results reveal that the coexistence of different copper oxidation states, especially the Cu+ intermediate, is critical in steering selectivity towards multicarbon (C 2+ ) products. The dynamic modulation of surface redox states via tailored pulsing strategies favors C–C coupling pathways by inducing localized alkaline conditions and stabilizing reactive intermediates. This work establishes a predictive modeling platform that links pulse waveform design with mechanistic insights into catalyst state evolution and product selectivity. Overall, this study provides valuable insights into the synergistic effect of in situ activation of pre-catalysts and pulsed electrolysis for higher selectivity towards C 2+ products.

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

Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions

The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.

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

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2025

Towards a multiscale approach for understanding irradiation induced swelling and creep in 316 stainless steels - A coupled cluster dynamics and crystal plasticity approach

Structural materials undergo mechanical degradation, in part due to irradiation-induced swelling and creep, under nuclear reactor conditions. While swelling results from the migration and clustering of irradiation-induced atomic-scale mobile defects, the interaction of mesoscale dislocations with these defects causes creep deformation. A coupled crystal plasticity (CP) and mean-field cluster dynamics (CD) approach is presented to investigate the effect of irradiation on the long-term mechanical behavior of 316 stainless steel, which are under consideration for use in nuclear reactors. The temporal evolution of Frenkel pairs and extended defect population, under a chosen irradiation flux and temperature, is predicted using the CD model. The impact of the irradiation defects on the stress state, and the resulting dislocation-mediated inelastic deformation, is modeled concurrently with the CP model. The inelastic deformation is irradiation flux dependent, and early-stage defect evolution determines the later-stage mechanical behavior in 316 stainless steel.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

MultiSector Dynamics: 2023 Inaugural Workshop Report

Preface The MultiSector Dynamics (MSD) Community of Practice (CoP) hosted an inaugural workshop on October 3-5, 2023 at the University of California, Davis, to bring together members of the MSD community of practice to advance understanding of the co-evolution of human and natural systems, and to build the next generation of tools that bridge sectors, scales, and systems to realize a more resilient and equitable future. The theme of the workshop was "Advancing Complex Adaptive Human-Earth Systems Science in a World of Interconnected Risks". This document outlines the motivation for the workshop, its goals and objectives, the application process, the agenda, overviews of the training sessions offered to the workshop participants and a summary of each breakout session. The MSD workshop report further discusses the feedback from workshop participants and presents some reflections and next steps. The MSD Workshop organizers thank the DOE Office of Science, Earth and Environmental System Modeling, MultiSector Dynamics program area for financial support of its activities through the Integrated Multisector Multiscale Modeling (IM3) project. For more information related to the broader DOE MultiSector Dynamics Program please see https://climatemodeling.science.energy.gov/program-area/multisector-dynamics. D.L.M. and C.M.B. acknowledge support from the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the US Department of Energy (DOE). Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither theUnited States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, complete- ness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial products, process, or service by trade name,trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Pacific Northwest National Laboratory operated by Battelle for the United States Department of Energy Available from:Office of Scientific and Technical Information http://www.OSTI.gov multisectordynamics.org This work is made available under the terms of the Creative Commons Attribution- NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4 Suggested citation: Monier, E., Reed, P.M., Vernon, C.R., Hadjimichael, A., Brelsford, C.M., Burleyson, C.B., Dyreson, A.R., Fletcher, S.M., Giang, A., Gupta, R.S., Jackson, N.D., Jones, A.D., Lamontagne, J.R., McCollum, D.L., Morris, J.F., Moss, R.H., Peng, W., Saari, R.K., Srikrishnan, V., Szinai, J.K., Yoon, J. (2024) MultiSector Dynamics: 2023 Inaugural Workshop Report. MSD-LIVE Data Repository. doi:10.57931/2371710.

Monier, Erwan↗
DOE OSTI2025

Potential quantum advantage for simulation of fluid dynamics

Numerical simulation of turbulent fluid dynamics needs to either parametrize turbulence—which introduces large uncertainties—or explicitly resolve the smallest scales—which is prohibitively expensive. Here, we provide evidence through analytic bounds and numerical studies that a potential quantum speedup can be achieved to simulate fluid dynamics using quantum computing. Specifically, we provide a lattice Boltzmann formulation of fluid dynamics for which we give evidence that low-order Carleman linearization is much more accurate than previously believed for these systems. This is achieved via a combination of reformulating the Navier-Stokes nonlinearity (u·$\triangledown$u) to lattice-Boltzmann nonlinearity (u 2 ) and accurately linearizing the dynamical equations, which effectively trades nonlinearity for additional degrees of freedom that add negligible expense in the quantum solver. Based on this, we apply a quantum algorithm for simulating the Carleman-linearized lattice Boltzmann equation and provide evidence that its cost scales logarithmically with system size compared with polynomial scaling in the best known classical algorithms. In this paper, we suggest that a quantum advantage may exist for simulating fluid dynamics, paving the way for simulating nonlinear multiscale transport phenomena in a wide range of disciplines using quantum computing.

42 ENGINEERING↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2025

Ion Size Effects on the Thermodynamic, Kinetic, and Mechanical Properties during Ion Exchange in Solid-State Electrolytes

Ion exchange offers a pathway to impose residual compressive stresses to mitigate the electro-chemo-mechanical cracking of solid-state electrolytes such as lithium lanthanum zirconium oxide. This study uses a coupled multiscale framework (integrating density functional theory (DFT), molecular dynamics (MD), and continuum modeling) to examine how exchange ion size influences stress, diffusion, fracture toughness, and electronic properties. Larger isovalent ions (Na + , Ag + , K + ) were exchanged with Li + , with DFT confirming their preference for octahedral 96h sites and a linear relationship between ion size and chemical free expansion coefficient. MD simulations reveal stress and concentration effects on exchange ion diffusivity at elevated temperatures, with Na + and Ag + maintaining favorable mobility while K + showing concentration-dependent clustering. Continuum modeling predicts the range of fracture strength improvements and the required ion exchange concentration profile. It was shown that a 5% surface exchange concentration can induce ∼0.6 GPa of surface compressive stress using Na + and ∼1.0 GPa of surface compressive stress using Ag + . On the other hand, larger ion exchange species may penalize Li + diffusivity by increasing the activation volume and activation energy. Interestingly, Na + has a negligible penalty on Li-ion diffusivity. The room temperature Li + ion diffusivity is reduced by ∼40% with Ag + ion exchange. Electronic band structure analysis shows no size-dependent change in the bandgap, though Ag + introduces localized defect states near the valence band maximum. This study highlights ion size as a key factor in optimizing LLZO properties, offering a framework to improve the solid-state battery performance.

Jagad, Harsh D. [Brown Univ., Providence, RI (Unit↗
DOE OSTI2025

Theory of x-ray photon correlation spectroscopy for multiscale flows

Complex multiscale flows associated with instabilities and turbulence are commonly induced under high-energy density (HED) conditions, but accurate measurement of their transport properties has been challenging. X-ray photon correlation spectroscopy (XPCS) with coherent x-ray sources can, in principle, probe material dynamics to infer transport properties using time autocorrelation of density fluctuations. Here we develop a theoretical framework for utilizing XPCS to study material diffusivity in multiscale flows. We extend single-scale shear flow theories to broadband flows using a multiscale analysis that captures shear and diffusion dynamics. Our theory is validated with simulated XPCS for Brownian particles advected in multiscale flows. We demonstrate the versatility of the method over several orders of magnitude in timescale using sequential-pulse XPCS, single-pulse x-ray speckle visibility spectroscopy (XSVS), and double-pulse XSVS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Hydrological Controls on Riverbed Methane Emissions: A Numerical Investigation of Hydrodynamic and Ebullitive Mechanisms from Site to Basin Scales

Methane production and emission from riverbed sediments constitute a significant yet underexplored component of the river methane budget. Despite their importance, a mechanistic and quantitative understanding of these processes under dynamic flow conditions remains elusive, particularly at the basin scale. In this study, we investigate the multiscale (site-to-basin) mechanisms governing methane emissions from riverbeds, focusing on the interplay between biogeochemical processes and hydrological dynamics. We developed a reactive transport model that integrates methane production with hydrodynamic and ebullitive pathways and coupled it with a basin-scale groundwater flow model to simulate methane emissions across scales. By incorporating key factors such as vertical hydrological exchange flow (VHEF), particulate organic carbon availability, sediment properties, and temperature sensitivity, our model captures the spatiotemporal variability of methane ebullition at both the site and basin scales. Our results reveal that methane ebullition is strongly modulated by VHEF, with lower flow rates promoting methane accumulation and subsequent ebullition. Sensitivity analyses underscore nonlinear responses of methane fluxes to environmental drivers. Furthermore, these findings emphasize the critical roles of sediment composition, hydrological exchange, and environmental conditions in shaping methane dynamics. Overall, this work advances our understanding of riverine methane emissions and offers insights for guiding climate models and mitigation strategies.

Fluxes↗
DOE OSTI2026

Substitution-Mediated Calcination of Nickel-Based Cathodes: Decoupling Lithiation and Crystallization

Nickel-based layered cathodes such as LiNiO 2 offer high energy density for lithium-ion batteries, yet improvements in cycling performance and safety are required for practical use–often achieved through manganese and cobalt substitution as in LiNi 0.80 Mn 0.10 Co 0.10 O 2 (NMC811). However, how such substitution impacts calcination, the key process that governs lithiation, structural ordering, crystallization, and ultimately the resulting material properties, remains unclear. Here, in this study, we investigate substitution-mediated calcination dynamics in NMC811 compared to LiNiO 2 using multiscale-correlated in situ spectroscopy and atomistic-to-mesoscale modeling. While both systems progress through the same sequence of intermediates toward the thermodynamically favored layered phase, NMC811 exhibits an earlier onset of layering, concurrent with hydroxide decomposition followed by sluggish crystallization. Modeling reveals that Mn and Co lower the energy barrier for lithium incorporation and ordering but increase the penalty for interlayer gliding, thereby slowing crystal growth at elevated temperatures. This substitution-mediated decoupling of lithiation and crystallization explains the fine-grained microstructure observed in NMC811 versus coarsened particles in LiNiO 2 and establishes a mechanistic framework for predictive microstructure engineering of Ni-based cathodes.

Calcination↗
DOE OSTI2025

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗
DOE OSTI2024

A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity

Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Multiscale Cryo Electron Microscopy Reveals Interfacial Degradation and Stabilization in Battery Electrodes

Electrochemical interfaces are dynamic systems, evolving based on their local environment and reactant surface structures. The electrode-electrolyte interface in Li-ion batteries can be protective, limiting parasitic reactions with the electrolyte to passivate the surface [1]. Additionally, this interphase has an impact on the Li-ion transport through that layer based on its composition, bonding environment, and thickness. These parameters are challenging to collect and may vary depending on the electrode surface site investigated relative to its spatial position in a coin cell. This study will detail a multiscale cryogenic electron microscopy approach where millimeter-scale cross-sections through the coin cell batteries were made using a cryogenic stage within a fs-laser plasma focused ion beam (laser PFIB) with complementary energy dispersive X-ray spectroscopy able to detect variations in the composition at electrode interfaces [2]. Microscale cross-sectioning and lamella sample preparation of battery electrodes was conducted at the Center for Integrated Nanotechnologies using a Ga-ion focused ion beam (FIB) with air-free and cryo-transfer [3], followed by nanoscale mapping of composition and bonding within the CEI through cryo-scanning transmission electron microscopy (cryo-STEM) electron energy loss spectroscopy [4]. This multiscale approach enabled identification of millimeter-scale features of a battery stack with visualization of degradation in electrodes such as cracks in cathode particles, gas evolution, and SEI evolution; microscale interfacial characteristics, such as heterogeneity in the SEI or barrier layer and identification of electrolyte networks to the electrode surfaces; and nanoscale measurement of the CEI thickness, mapping of transition metal bonding within the cathode particles to identify loss of active materials, and identification of beneficial electrolyte additives incorporated into the CEI structure. This multiscale approach allows for a statistical understanding of the primary mechanisms and parasitic degradation pathways that impact performance by limiting the ion transport pathways within Li+ batteries.

36 MATERIALS SCIENCE↗