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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Surface Plasmon Dynamics in Periodic Nanostructures through Ultrafast Electron Microscopy

Surface plasmon polaritons (SPPs) can be manipulated to localize and guide light in subwavelength distances, enabling them to find applications in a wide range of areas, from sensing to quantum computing. Among several methods of SPP excitation, periodic arrays of nano- and microstructures are of particular interest, as they enable engineering SPP properties through structural parameters. Here, in this study, using the photon-induced near-field electron microscopy (PINEM) technique, we investigated the mode formation, coupling, interference, and decay of SPPs in square and hexagonal arrays of circular nanoholes under both visible and near-infrared excitation. Polarization-resolved analysis revealed the key factors governing SPP localization and interference patterns, showing that the periodicity and symmetry of the array primarily determine the SPP interference patterns and their orientation, while pump polarization mainly modulates their intensity. Time-resolved PINEM measurements demonstrated the spatial dependence of the SPP temporal characteristics. In addition, cathodoluminescence (CL) spectroscopy was employed to examine the intrinsic plasmonic characteristics of the structure. Finite difference time domain (FDTD) simulations showed strong agreement with both PINEM and CL measurements on the spatial and spectral behavior of SPPs. Understanding the spatiotemporal dynamics of SPPs on nanostructures beyond the diffraction limit is crucial for optimizing plasmonic structures for advanced photonic and quantum technologies.

Plasmonics↗
DOE OSTI2026

Microstructural Topology as a Prescriptor for Quantum Coherence: Towards A Unified Framework for Decoherence in Superconducting Qubits

In superconducting quantum circuits, decoherence improvements are frequently obtained through process interventions that simultaneously modify surface chemistry, microstructural topology, and device geometry, leaving mechanistic attribution structurally underdetermined. Predictive materials engineering requires measurable structural statistics to be separated from geometry-dependent coupling coefficients into independently testable factors. We introduce the concept of classical and quantum microstructure. In that context, we formulate a channel-wise separable framework for decoherence in superconducting transmon qubits in which each loss channel is described by a reduced prescriptor. Here, a channel-specific microstructural state variable is determined independently of device geometry, and a geometry-dependent coupling functional is computable from field solutions without reference to surface chemistry. We derive this product form from a spatially resolved kernel representation and establish a perturbative separability criterion that defines the regime where independent variation of the variables is valid. The framework specifies five prescriptor classes for dominant loss pathways in transmon-class devices. Falsifiability is operationalized through a pre-committed 2x2 experimental protocol in which the variables must satisfy independent ratio checks within propagated uncertainty. A Minimum-Dataset Specification standardizes reporting for cross-laboratory inference. Part I establishes the conceptual and mathematical architecture; coordinated experimental validation is reserved for Part II.

Dravid, Vinayak P. [Northwestern U.]↗
DOE OSTI2026

Microstructure‐Informed Analysis Framework for Lattice Structures: Guiding Topology‐Material Synergy in Titanium Alloys

In this work we discuss a microstructure‐informed analysis framework for lattice structures that maps the material's microstructural response to guide topology selection and mechanical performance optimization. By coupling geometrical topology with intrinsic material behavior, we demonstrate how anisotropic microstructural response can inform the design of optimized lattice structures. To illustrate this concept, we focus on two distinct classes of titanium alloys: Ti5553 (Ti‐5Al‐5Mo‐5V‐3Cr wt%), which exhibits a predominantly ‐phase microstructure, and Ti64 (Ti‐6Al‐4V wt%), which features a dual‐phase structure. These alloys exhibit markedly different mechanical responses under multiaxial loading in “fully dense” solid form. The strut‐level stress analysis of these alloys reveals how specific microstructural characteristics can guide the selection of appropriate lattice topologies. Two representative lattice configurations, one stretching‐dominated and one bending‐dominated, are evaluated under identical loading conditions to explore how microstructure‐driven design can lead to topology choices that are better suited to accommodate shear or other critical local stress states, thereby enhancing mechanical performance. A strut‐level mechanics‐based analysis is performed to evaluate shear stress distribution and highlight the role of topology‐microstructure synergy and compatibility in determining overall lattice behavior. The findings emphasize the importance of designing structures that are both load‐aware and microstructure‐responsive, enabling more effective material utilization in advanced engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coupled phase field damage and crystal plasticity analysis of intragranular fracture: The role of crystallographic orientation and voids

Damage evolution in engineering metal alloys at the grain scale exhibits significant microstructural heterogeneity and anisotropy. These heterogeneities create local hotspots for stress and strain localization, leading to void nucleation. Crystal orientation influences the active slip systems around voids, affecting lattice rotation and potentially forming discontinuities. At low triaxiality, voids may change shape due to lower stress, rotation, elongation, and coalescence. At high triaxiality, the correlation between crystal orientation and void growth rate becomes stronger, resembling the behavior observed in isolated single crystals. Therefore, understanding the effects of crystal orientation, heterogeneous strain, and defect evolution is crucial for single crystal fracture characterization. Here, in this work, a coupled phase-field damage (PFD) and crystal plasticity (CP) model is implemented within a finite element framework to analyze crystal deformation and failure. The CP method employs a dislocation density-based constitutive model, while intragranular failure is modeled using an anisotropic PFD method. The PFD model considers both the stored energy due to elastic stretching and the energy release due to defect formation and crack formation. A single crystal Al2219 with an intracrystalline spherical void is chosen to analyze fracture. The study finds that fracture propagation is strongly correlated with crystal orientations. This coupled CP-PFD model provides accurate failure prediction in crystalline materials by incorporating the effects of crystal orientations and existing voids. This study demonstrates how the local microstructure and defects influence plastic deformation and failure mechanisms in metal alloys.

Aluminum alloy↗
DOE OSTI2024

Highly Ordered Eutectic Mesostructures via Template‐Directed Solidification within Thermally Engineered Templates

Template-directed self-assembly of solidifying eutectics results in emergence of unique microstructures due to diffusion constraints and thermal gradients imposed by the template. Here, the importance of selecting the template material based on its conductivity to control heat transfer between the template and the solidifying eutectic, and thus the thermal gradients near the solidification front, is demonstrated. Simulations elucidate the relationship between the thermal properties of the eutectic and template and the resultant microstructure. The overarching finding is that templates with low thermal conductivities are generally advantageous for forming highly organized microstructures. When electrochemically porosified silicon pillars (thermal conductivity < 0.3 Wm −1 K −1 ) are used as the template into which an AgCl-KCl eutectic is solidified, 99% of the unit cells in the solidified structure exhibit the same pattern. In contrast, when higher thermal conductivity crystalline silicon pillars (≈100 Wm −1 K −1 ) are utilized, the expected pattern is only present in 50% of the unit cells. The thermally engineered template results in mesostructures with tunable optical properties and reflectances nearly identical to the simulated reflectances of perfect structures, indicating highly ordered patterns are formed over large areas. This work highlights the importance of controlling heat flows in template-directed self-assembly of eutectics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Redox-Induced Microstructure and Phase Dynamics in Nickel: Insights from In Situ Synchrotron X-ray Diffraction

Using in situ synchrotron X-ray diffraction, we interrogate the microstructural and phase evolution of polycrystalline nickel (Ni) during redox cycling in O 2 , H 2 , and H 2 O environments. Oxidation in O 2 promotes strong (111) texturing in both the NiO overlayer and the underlying Ni substrate. However, this crystallographic alignment is lost following reduction in H 2 and subsequent reoxidation, demonstrating irreversible microstructural changes. H 2 exposure leads to proton dissolution into the Ni lattice, triggering a localized phase transition from face-centered cubic (FCC) to hexagonal close-packed (HCP) Ni in hydrogen-saturated regions. In H 2 O-containing atmospheres, dissociative H 2 O adsorption produces protons that permeate the NiO layer, forming γ-NiOOH within the NiO lattice and HCP Ni beneath the NiO overlayer as protons accumulate. Kinetic analysis via the Johnson-Mehl-Avrami–Kolmogorov model uncovers distinct growth mechanisms: preoxidized Ni surfaces follow one-dimensional (1D) kinetics for NiO, γ-NiOOH, and HCP growth, whereas pristine Ni exhibits three-dimensional (3D) kinetics due to island-like nucleation and growth of NiO. Furthermore, these results highlight the critical interplay between H 2 O dissociation, hydrogen permeation, and redox-driven phase transformations, with practical implications in engineering nickel-based catalysts and hydrogen storage systems through controlled microstructural and phase evolution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure and mechanical properties of grain boundaries in molybdenum disulfide (MoS 2 )

Molybdenum disulfide (MoS 2 ) is a two-dimensional material widely used as a lubricant in many applications involving mechanical loading under a wide range of operating temperatures. Since many synthesis and processing techniques yield MoS 2 in its polycrystalline form, establishing grain boundary (GB) structure–property relations is key to designing MoS 2 microstructures with tailored properties. Here, we employ classical, reactive atomistic simulations to investigate the structure and mechanical behavior of a wide range of GBs in MoS 2 as a function of temperature. Using a bicrystal MoS 2 geometry, we characterize the atomic structure and calculate the energy of several low-angle GBs. Then, we simulate the tensile deformation behavior of MoS 2 bicrystals at several temperatures. Our results reveal that at temperatures above 100 K, the deformation of MoS 2 bicrystals is characterized by the nucleation of shear bands from GBs that grow, with subsequent loading, into the MoS 2 crystals. At low temperatures, the tensile deformation is characterized by the nucleation and propagation of deformation fronts, resulting in altered bond angles and bond lengths. Quantitative analysis reveals a decrease in the ultimate tensile stress and ultimate failure strain of MoS 2 bicrystals with the increase in temperature. Furthermore, our simulations of the mechanical behavior of metastable GBs reveal that the strength and ductility decrease with the increase in energy of these boundary structures. In broad terms, our work provides future avenues to employ GB engineering as a strategy to tailor the properties of MoS 2 microstructures.

Moore, Robert D. [Lehigh Univ., Bethlehem, PA (Uni↗
DOE OSTI2025

SEM Milestone Report for MARMOT Validation Project

MARMOT is a meso-scale fuel code under development by NEAMS designed to predict the microstructure evolution of fuel materials to determine updated thermal and mechanical property models. These updated models can be used by engineering scale codes such as BISON. To support its development and validation the MARMOT Validation Project (MVP) was initiated and consists of extensive microstructure and thermal characterization of U-10Zr fuel material before and at discreet levels of irradiation. This report summarizes the SEM and EDS characterization of the lowest burnup samples (1.5% FIMA) and how they compare to pre-irradiation SEM and EDS.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗
DOE OSTI2025

Chemo-Mechanical Behavior and Stability of High-Loading Cathodes in Solid-State Batteries

Solid-state batteries can offer higher energy density and improved safety compared to lithium ion batteries, which use flammable liquid electrolytes. Increasing the ratio of cathode active materials in composite cathodes enhances the energy density and reduces manufacturing costs. Changes in the ratio of cathode active materials alter the microstructure and chemo-mechanical response of a cathode during operation. Understanding the relationship between composition, microstructure, and chemo-mechanical interactions is critical for optimizing solid-state cathodes. Here, in this study, we engineered composite cathodes with varying ratios of LiNi 0.8 Co 0.1 Mn 0.1 O 2 and Li 6 PS 5 Cl to systematically investigate the role of microstructural evolution in long-term chemo-mechanical transformations. Chemo-mechanical stresses resulting from the volume changes of the cathode active materials led to degradation mechanisms, such as fracture and interfacial delamination. Active material fracture and delamination led to underutilization of active material and significant capacity decay during cycling. Coatings that suppress active material-active material interactions during cycling may aid in suppressing the generation of local stress hotspots.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructural features and deuterium diffusion in lithium penta-aluminate pellets under He + and D + ion irradiation

Lithium (Li) penta-aluminate (LiAl 5 O 8 ) is investigated as a potential tritium (T) breeding material, with a focus on microstructural response to ion irradiation and deuterium (D) diffusion behavior. Under high-fluence ion irradiation (2 x 10 17 (He + +D + )/cm 2 ) at 773 K, LiAl 5 O 8 exhibits significant disorder on the Li sublattice, as revealed by atomic-resolution scanning transmission electron microscopy, while the Al and O sublattices remain stable, demonstrating strong resistance to structural amorphization. Irradiation induces the formation of platelet-shaped antiphase boundaries (APBs), which may serve as effective D trapping sites. Atom probe tomography suggests the presence of 6 LiD clusters in the mass spectra, though definite conclusions regarding APB composition are hindered by signal overlap and limited data statistics. Time-of-flight secondary ion mass spectrometry reveals that D retention approaches to saturation at 3 x 10 17 (He + +D + )/cm 2 . Isothermal and isochronal annealing studies determine an average diffusivity of 1.6 x 10 -13 at 773 K and an effective activation energy of 0.8 ± 0.1 eV for D migration. Compared to γ-LiAlO 2 , LiAl 5 O 8 demonstrates superior irradiation resistance, minimal Li loss, and enhanced D retention, underscoring its potential as a durable breeder material for T production. In conclusion, these findings provide key insights into the microstructural evolution, defect dynamics, and D retention mechanisms in LiAl 5 O 8 under reactor-relevant conditions.

42 ENGINEERING↗
DOE OSTI2026

A User-Friendly GUI Tool for Automated Microstructural Analysis of Fiber-Reinforced Composites and Porous Structures

Understanding and quantifying microstructural features such as fiber orientation and porosity is critical for predicting the mechanical behavior and performance of fiber-reinforced polymer composites. Traditional manual analysis is time-consuming, subjective, and unsuitable for high-throughput datasets. We present a graphical user interface (GUI) application that automates the analysis of microscopy images to extract key microstructural metrics, including fiber orientation tensors, fiber orientation distribution, porosity and pore size distribution. The app integrates multiple image segmentation techniques including global and local thresholding, clustering, and region-based approaches, offering flexibility for different types of image qualities and features. Users can load microstructural images, select regions of interest and segmentation techniques tailored to their image dataset. It also addresses a critical challenge in fiber orientation analysis: the ambiguities caused by touching, overlapping, or partially cut fibers. It supports autorun examples for standardized workflows, enabling reproducible analysis and facilitating training and benchmarking. This tool significantly reduces manual intervention, enhances consistency, and accelerates data generation for structure–property modeling, process optimization, and digital materials research. The tool is intended for use by materials scientists, engineers, and researchers engaged in composite characterization, quality control, and machine learning-based microstructural studies.

Chawla, Komal [ORNL] (ORCID:0000000190327565)↗
DOE OSTI2025

Toward Standardized Microscale Tensile Testing for Two‐Photon Polymerization‐Fabricated Materials in Liquid

Two-photon polymerization (TPP) enables the fabrication of intricate 3D microstructures with submicron precision, offering significant potential in biomedical applications like tissue engineering. In such applications, to print materials and structures with defined mechanics, it is crucial to understand how TPP printing parameters impact the material properties in a physiologically relevant liquid environment. Herein, an experimental approach utilizing microscale tensile testing (μTT) for the systematic measurement of TPP-fabricated microfibers submerged in liquid as a function of printing parameters is introduced. Using a diurethane dimethacrylate-based resin, the influence of printing parameters on microfiber geometry is first explored, demonstrating cross-sectional areas ranging from 1 to 36 μm 2 . Tensile testing reveals Young's moduli between 0.5 and 1.5 GPa and yield strengths from 10 to 60 MPa. The experimental data show an excellent fit with the Ogden hyperelastic polymer model, which enables a detailed analysis of how variations in writing speed, laser power, and printing path influence the mechanical properties of TPP microfibers. The μTT method is also showcased for evaluating multiple commercial resins and for performing cyclic loading experiments. Collectively, this study builds a foundation toward a standardized microscale tensile testing framework to characterize the mechanical properties of TPP printed structures.

mechanical characterization↗
DOE OSTI2026

Equilibrium and non-equilibrium effects in high pressure phase transformations of carbon

The behavior of carbon in the range 1–100 GPa and 1–10 kK is central to problems in planetary interiors, inertial confinement fusion targets, and high-pressure synthesis of carbon-based materials, but experiments in this regime are difficult and often provide only indirect constraints on phase behavior. As a result, phase boundary loci, structure, and limits of metastability at high pressure remain uncertain. In this work, machine-learning enhanced atomistic simulations are used to address this knowledge gap. We determine the melt line up to 100 GPa, the graphite-diamond phase boundary up to the melt line, and analyze structure of the coexisting phases. We show that the coexisting liquid evolves smoothly with pressure without evidence for a first-order liquid–liquid transition. Orientation-resolved graphite melting simulations indicate that basal-plane interfaces develop a dewetting layer and undergo layer-by-layer melting, producing kinetic hysteresis and an apparent orientation dependence of the melt line. Non-equilibrium quenches from the melt are used to construct a kinetically limiting graphite–diamond phase boundary for rapid quenches from above the melt line, and show that graphite is metastable at pressures of up to ≈ 25 GPa. These results provide bounds on equilibrium and metastable behavior in carbon relevant for interpreting high-pressure experiments and for designing synthesis pathways to specific carbon microstructures.

Lyu, Yanjun [Department of Materials Science and E↗
DOE OSTI2025

An Initial Microstructurally Informed Model of High Burnup Structure Formation in UO 2 Fuel

The microstructure of a UO 2 fuel pellet changes as burnup increases, impacting fuel performance. Predicting and characterizing high burnup structure (HBS) and dark zone formation is a key part of supporting burnup limit extensions for light water reactors. This paper describes a model developed through fitting radially resolved pellet data obtained from recently published microstructural characterization data. The model predicts grain size and grain character, in addition to pore density and size, with fitting dependencies on power history variables. Separately fitting power history variables to microstructural parameters allows for insight into the underlying physical phenomena for future model development. Additionally, experimental data have been correlated to an HBS fraction to facilitate the development of a model capable of predicting a total fuel restructured fraction at the engineering scale. In conclusion, this two-step approach provides a coupling from reactor power history to microstructural data to fractional HBS and creates a basis to model HBS-dependent parameters in a fuel performance code.

High burnup structure↗
DOE OSTI2024

Real-space visualization of a defect-mediated charge density wave transition

Here, we study the coupled charge density wave (CDW) and insulator-to-metal transitions in the 2D quantum material 1T-TaS 2 . By applying in situ cryogenic 4D scanning transmission electron microscopy with in situ electrical resistance measurements, we directly visualize the CDW transition and establish that the transition is mediated by basal dislocations (stacking solitons). We find that dislocations can both nucleate and pin the transition and locally alter the transition temperature T c by nearly ~75 K. This finding was enabled by the application of unsupervised machine learning to cluster five-dimensional, terabyte scale datasets, which demonstrate a one-to-one correlation between resistance—a global property—and local CDW domain-dislocation dynamics, thereby linking the material microstructure to device properties. This work represents a major step toward defect-engineering of quantum materials, which will become increasingly important as we aim to utilize such materials in real devices.

4D-STEM↗
DOE OSTI2025

Performance Evaluation of LPBF Manufactured 316H Components

This work represents the continuation of a benchmark study that includes modeling, fabrication and characterization as demonstration to support industry’s adoption of advanced manufacturing processes in a variety of structures. This comprehensive study investigated the feasibility of using additive manufacturing (AM) technologies, specifically Laser Powder Direct Energy Deposition (LP-DED) and Laser Powder Bed Fusion (LPBF), to produce complex nuclear microreactor components using 316H stainless steel. The research focused on manufacturing an expanded elbow pipe component with transitioning sections, which are traditionally difficult and costly to produce through conventional manufacturing methods. The overall study’s primary objectives are therefore demonstrating AM viability for nuclear applications, optimizing process parameters, developing comprehensive material characterization protocols, validating computational modeling approaches, and establishing manufacturing guidelines for complex geometries. Although the initial work included the phased approach of cubical, upscaled cylindrical components, it is to enable to obtain more knowledge for the printing of the expanded elbow structure. The project achieved significant progress in process development by successfully optimizing LP-DED parameters to achieve 99.16-99.97% relative density in 316H stainless steel components. Through systematic evaluation of sixteen cube samples with varied laser powers (400-700W) and scan speeds (600-900 mm/min), optimal processing windows were identified at 500-550W with 600-700 mm/min or 650-700W with 650-900 mm/min scan speeds. The DED manufactured 316H demonstrated mechanical properties comparable or superior to wrought materials, with Young's modulus ranging from 153-208 GPa and controlled microstructural characteristics including greater than 95% face-centered cubic (FCC) phases and engineered cellular structures with sizes between 3.23-6.17 µm.

36 MATERIALS SCIENCE↗