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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 OSTI2020

Residual Stress Modeling with Phase Transformation for Wire Arc Additive Manufacturing of B91 Steel

Wire arc additive manufacturing (WAAM) is an energy-efficient manufacturing technique used for near-net-shape production of functional industrial components. However, heat accumulation during deposition and the associated mechanical and metallurgical changes result in complex residual stress profiles across the cross section of the fabricated components. These residual stresses are detrimental to the service life of the components. In this study, sequentially coupled thermomechanical analysis of WAAM B91 steel is conducted to quantify the residual stress variation across the component. The thermomechanical analysis includes a transient heat transfer model and a static stress model that incorporates the transformation-induced plasticity due to martensitic phase transformation. Here, the experimentally calibrated heat transfer model mirrors the temperature variation of the system during the deposition. The results from the stress model are validated via x-ray diffraction measurements, and the numerical results are in good agreement with the experimental data.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2022

Automatic Calibration of a Geomechanical Model from Sparse Data for Estimating Stress in Deep Geological Formations

Summary In this study, we demonstrate geomechanical modeling with fully automatic parameter calibration to estimate the full geomechanical stress fields of a prospective US carbon dioxide (CO2) storage site, based on sparse measurement data. The goal is to compute full stress tensor field estimates (principal stresses and orientations) that are maximally compatible with observations within the constraints of the model assumptions, thereby extending pointwise, incomplete partial stress measurement to a simulated full formation stress field, as well as a rough assessment of the associated error. We use the Perch site, located in Otsego County, Michigan, USA, as our case study. The input data consist of partial stress tensor information inferred from in-situ borehole tests, geophysical well logs, and processing of seismic data. A static earth model (SEM) of the site was developed, and geomechanical simulation functionality of the open-source MATLAB Reservoir Simulation Toolbox (MRST) was used to model the stress field. Adjoint-based nonlinear optimization was used to adjust boundary conditions and material properties to calibrate simulated results of observations. Results were interpreted through a Bayesian framework. The focus of this paper is to demonstrate how the fully automatic calibration procedure works and discuss the results obtained; it does not attempt a detailed analysis of the stress field in the context of the proposed CO2 storage initiatives. Our work is part of a larger effort to noninvasively determine in-situ stresses in deep formations considered for CO2 storage. Guided by previously published research on geomechanical model calibration, our work presents a novel calibration approach supporting a potentially large number of linear or nonlinear calibration parameters to produce results optimally agreeing with available measurements and thus extend partial pointwise estimates to full tensor fields compatible with the physics of the site.

Engineering↗

DOE OSTI2021

Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought

In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Rooting for survival: how plants tackle a challenging environment through a diversity of root forms and functions

Abstract The current climate crisis has global impacts and will affect the physiology of plants across every continent. Ensuring resilience of our agricultural and natural ecosystems to the environmental stresses imposed by climate change will require molecular insight into the adaptations employed by a diverse array of plants. However, most current studies continue to focus on a limited set of model species or crops. Root systems are particularly understudied even though their functions in water and nutrient uptake are likely pivotal for plant stress resilience and sustainable agriculture. In this review, we highlight anatomical adaptations in roots that enable plant survival in different ecological niches. We then present the current state of knowledge for the molecular underpinnings of these adaptations. Finally, we identify areas where future research using a biodiversity approach can fill knowledge gaps necessary for the development of climate-resilient crops of the future.

Plant Sciences↗

DOE OSTI2020

A temporal hierarchy underpins the transcription factor–DNA interactome of the maize UPR

Adverse environmental conditions reduce crop productivity and often increase the load of unfolded or misfolded proteins in the endoplasmic reticulum (ER). This potentially lethal condition, known as ER stress, is buffered by the unfolded protein response (UPR), a set of signaling pathways designed to either recover ER functionality or ignite programmed cell death. Despite the biological significance of the UPR to the life of the organism, the regulatory transcriptional landscape underpinning ER stress management is largely unmapped, especially in crops. To fill this significant knowledge gap, we performed a large–scale systems–level analysis of the protein–DNA interaction (PDI) network in maize (Zea mays). Using 23 promoter fragments of six UPR marker genes in a high–throughput enhanced yeast one–hybrid assay, we identified a highly interconnected network of 262 transcription factors (TFs) associated with significant biological traits and 831 PDIs underlying the UPR. We established a temporal hierarchy of TF binding to gene promoters within the same family as well as across different families of TFs. Cistrome analysis revealed the dynamic activities of a variety of cis–regulatory elements (CREs) in ER stress–responsive gene promoters. By integrating the cistrome results into a TF network analysis, we mapped a subnetwork of TFs associated with a CRE that may contribute to UPR management. Lastly, we validated the role of a predicted network hub gene using the Arabidopsis system. The PDIs, TF networks, and CREs identified in our work are foundational resources for understanding transcription–regulatory mechanisms in the stress responses and crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗

DOE OSTI2020

Shear Softening in a Metallic Glass: First-Principles Local-Stress Analysis

Metallic glasses deform elastically under stress. However, the atomic-level origin of elastic properties of metallic glasses remain unclear. In this Letter using ab initio molecular dynamics simulations of the Cu 5 0Zr 50 metallic glass under shear strain, we show that the heterogeneous stress relaxation results in the increased charge transfer from Zr to Cu atoms, enhancing the softening of the shear modulus. Changes in compositional short-range order and atomic position shifts due to the nonaffine deformation are discussed. It is shown that the Zr subsystem exhibits a stiff behavior, whereas the displacements of Cu atoms from their initial positions, induced by the strain, provide the stress drop and softening.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Local stress within a granular molecular solvent matrix, a mechanism for individual ion hydration

The activity of water is important in many natural systems and directed processes. To better understand water activity, the hydration of individual ions is approximated for a recently developed speciation-based solution model. Here a matrix of values for hydration of complete salts (anion and cation) was used as source data to calculate individual ion hydration values. Some electrolytes were excluded from calculations of individual ion hydration due to the prevalence of ion pairing and resulting liberation of water upon ion pairing. Evaluation of the periodic trends emerging from individual ion hydration values suggests that ionic volume, relative to the volume of molecular water, dictates both modeled hydration requirements (related to vapor-liquid-equilibrium-derived activity) as well as properties historically described by hydrodynamic radius (ion diffusion and solution viscosity). When water is conceptualized as a granular three-dimensional matrix, solutes may function as substitutional defects (dilation or constriction) inducing local compressive or tensile stress. These results have implications on the mechanism of the Hofmeister series and series reversals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Characterization of Properties of Laser Powder Bed Fusion 3D-Printed Inconel 718 for Centrifugal Turbomachinery Applications

Here, this paper presents the results of a comprehensive effort to characterize the properties of Inconel 718 produced by a form of laser powder bed fusion (LPBF) additive manufacturing (AM) or 3D-printing, subsequently subjected to hot isostatic pressing (HIP) and heat treatment according to standards F3055-14a and AMS 5663, respectively. Material property data, while broadly available for traditional Inconel 718 presentations (e.g. forgings or castings), is currently lacking for the 3D-printed material. It is expected that while limited in size, the experimental data sets presented provide sufficient information to glean the capability of LPBF Inconel 718. These include: 1) Chemical composition, electron backscatter diffraction (EBSD), and x-ray energy dispersive spectroscopy (XEDS) characterization of 3D-printed material structure; 2) Tensile properties — 0.2% yield stress, ultimate stress, modulus of elasticity, and elongation to failure — based on 108 samples, as functions of temperature and sample print orientation; 3) Creep rupture data including the Larson-Miller parameter, based on 21 samples; and 4) High cycle fatigue data based on 21 samples as a function of temperature. Results are compared to available standards and/or data for forged, cast, and other AM Inconel 718. A key observation of this study, based on the EBSD results, is that while the material appears to approach full recrystallization following heat treatment, there is a detectable fraction of the material that does not fully recrystallize, resulting in a material with mechanical properties (e.g. yield stress, creep rupture) measurably lower than those of forgings, but higher than those of castings.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Pulse Protein Isolates as Competitive Food Ingredients: Origin, Composition, Functionalities, and the State-of-the-Art Manufacturing

The ever-increasing world population and environmental stress are leading to surging demand for nutrient-rich food products with cleaner labeling and improved sustainability. Plant proteins, accordingly, are gaining enormous popularity compared with counterpart animal proteins in the food industry. While conventional plant protein sources, such as wheat and soy, cause concerns about their allergenicity, peas, beans, chickpeas, lentils, and other pulses are becoming important staples owing to their agronomic and nutritional benefits. However, the utilization of pulse proteins is still limited due to unclear pulse protein characteristics and the challenges of characterizing them from extensively diverse varieties within pulse crops. To address these challenges, the origins and compositions of pulse crops were first introduced, while an overarching description of pulse protein physiochemical properties, e.g., interfacial properties, aggregation behavior, solubility, etc., are presented. For further enhanced functionalities, appropriate modifications (including chemical, physical, and enzymatic treatment) are necessary. Among them, non-covalent complexation and enzymatic strategies are especially preferable during the value-added processing of clean-label pulse proteins for specific focus. This comprehensive review aims to provide an in-depth understanding of the interrelationships between the composition, structure, functional characteristics, and advanced modification strategies of pulse proteins, which is a pillar of high-performance pulse protein in future food manufacturing.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Quarterly Research Performance Progress Report (Q8)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Probing the elastic modulus and hardness of superionic boron cluster solid electrolytes

Polyhedral borane salts are highly tunable compounds with the potential to be used as solid electrolytes due to their high Li and Na superionic conductivity and relatively wide electrochemical stability window. In considering their application to all solid-state batteries, their mechanical properties play a critical role in their operation due to stresses the solid electrolyte is subjected to during battery operation. Density functional theory (DFT) calculations have provided an initial assessment of bulk and elastic moduli for some selected boron cluster solid electrolytes, but direct measurements are still scarce. Here, in this paper, we report the elastic moduli and hardnesses of LiCB 11 H 12 , LiCB 9 H 10 , NaCB 11 H 12 , and NaCB 9 H 10 for the first time using nanoindentation continuous stiffness method (CSM) measurements under inert atmosphere. Experimental modulus values ranging 8.8–12.3 GPa and hardness values ranging 0.15–0.38 GPa are lower than that of other inorganic solid-state electrolytes such as oxide- and sulfide-based solid electrolytes. DFT calculations on the expected moduli of these compounds are also presented and discussed. Analysis of the indentation plasticity index reveals that these compounds have higher plasticity index compared to other common oxide and sulfide solid electrolytes. According to the calculated Pugh's ratio, all compounds in this study except LiCB 11 H 12 are considered ductile.

25 ENERGY STORAGE↗

DOE OSTI2021

Effects of Zr addition on lattice strains and electronic structures of NbTaTiV high-entropy alloy

We report the room-temperature (RT) deformation behavior for two single-phase body-centered-cubic (BCC) refractory high-entropy alloys (RHEAs), NbTaTiV and NbTaTiVZr, has been comprehensively investigated via in-situ neutron-diffraction experiments. Our work shows that the addition of Zr leads to the transition of mechanical response from ductile to brittle behavior. The results of lattice-strain evolutions obtained from in-situ neutron diffraction for the ductile NbTaTiV RHEA exhibit atypical plastic-deformation behavior, i.e., the reduced plasticanisotropic deformation, leading to an even distribution of the applied stress amongst the grains with different orientations rather than forming stress concentrations in {200}-oriented grains during plastic-deformation. Density functional theory (DFT) analysis shows that NbTaTiVZr has a lower electron density at the Fermi level, larger lattice distortion, and stronger charge transfer, as compared to NbTaTiV, suggesting higher strength and lower ductility in NbTaTiVZr, which are consistent with the current experimental results.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗

DOE OSTI2025

Cholesterol modulates membrane elasticity via unified biophysical laws

Cholesterol and lipid unsaturation underlie a balance of opposing forces that features prominently in adaptive cell responses to diet and environmental cues. These competing factors have resulted in contradictory observations of membrane elasticity across different measurement scales, requiring chemical specificity to explain incompatible structural and elastic effects. Here, we demonstrate that – unlike macroscopic observations – lipid membranes exhibit a unified elastic behavior in the mesoscopic regime between molecular and macroscopic dimensions. Using nuclear spin techniques and computational analysis, we find that mesoscopic bending moduli follow a universal dependence on the lipid packing density regardless of cholesterol content, lipid unsaturation, or temperature. Our observations reveal that compositional complexity can be explained by simple biophysical laws that directly map membrane elasticity to molecular packing associated with biological function, curvature transformations, and protein interactions. The obtained scaling laws closely align with theoretical predictions based on conformational chain entropy and elastic stress fields. These findings provide unique insights into the membrane design rules optimized by nature and unlock predictive capabilities for guiding the functional performance of lipid-based materials in synthetic biology and real-world applications.

Kumarage, Teshani [Virginia Polytechnic Inst. and ↗

DOE OSTI2021

Estimation of the orientation of stress in the Earth’s crust without earthquake or borehole data

Abstract Mechanical stress acting in the Earth’s crust is a fundamental property that is important for a wide range of scientific and engineering applications. The orientation of maximum horizontal compressive stress can be estimated by inverting earthquake source mechanisms and measured directly from borehole-based measurements, but large regions of the continents have few or no observations. Here we present an approach to determine the orientation of maximum horizontal compressive stress by measuring stress-induced anisotropy of nonlinear susceptibility, which is the derivative of elastic modulus with respect to strain. Laboratory and Earth experiments show that nonlinear susceptibility is azimuthally dependent in an anisotropic stress field and is maximum in the orientation of maximum horizontal compressive stress. We observe this behavior in the Earth—in Oklahoma and New Mexico, U.S.A, where maximum nonlinear susceptibility coincides with the orientation of maximum horizontal compressive stress measured using traditional methods. Our measurements use empirical Green’s functions and solid-earth tides and can be applied at different temporal and spatial scales.

58 GEOSCIENCES↗

DOE OSTI2023

TRISO Burnup-Dependent Failure Analysis of a HTGR Design-Basis Accident Using BISON

Here, this work assesses the failure behavior of the silicon carbide (SiC) layer in TRIstructural ISOtropic (TRISO) fuel particles with BISON during both steady-state and transient conditions for the MHTGR-350 design by General Atomics. A one-dimensional BISON model for a uranium oxycarbide–bearing TRISO is developed to simulate a power level of 50 mW/particles up to 12.4% fissions per initial metal atom (FIMA). Stress predictions for the materials of interest are presented as a function of burnup, along with the SiC failure probability computed using Weibull statistics under the assumption of realistic SiC quality.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗