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Results for “heavy-ion collisions, hadronic transport, nuclear matter, equation of state, symmetry energy”

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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1,763 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Uncertainty quantification for competing failure mechanisms in unidirectionally reinforced carbon–carbon composites

Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Classical-Quantum Algorithm for Solving Stochastic Programs

Stochastic programming provides a rigorous mathematical framework for making decisions under uncertainty in a risk-aware manner. Two-stage stochastic programming is, perhaps, the simplest form of this framework. Here the first-stage variables represent decisions that must be made "here and now" in the face of uncertainty, while the second-stage variables are decisions made after uncertain events. However, the broad adoption of stochastic programming has been hindered by computational challenges caused by the two-stage stochastic programming formulation which requires solving an ensemble of optimization problems. Using quantum amplitude estimation (QAE), quantum computers have shown the theoretic ability to compute expectations with Monte-Carlo methods with quadratically fewer samples than classical methods. In this work, we present a quantum algorithm for computing the expectation term using QAE for given first-stage decisions. Further, we detail methods of computing gradient information from the quantum calculation enabling the application of classical gradient-based optimization techniques. The result is a classical-quantum hybrid method of solving two-stage stochastic programs. These techniques are demonstrated with computational experiments based an engineering optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hierarchical Epoxy Structures via Tunable Polymerization-Induced Phase Separation Combined with Additive Manufacturing

Polymerization-induced phase separation (PIPS) allows for the control of thermoset morphologies and properties, enabling the tuning of domain sizes and thermomechanical response. However, its use in generating substructural features in additively manufactured materials has been limited. In this work, we combine epoxy PIPS with UV curable acrylate and rheological modifiers to print nano- to macro-phase separating materials via a two-step, dual-cure approach. This method enables direct ink write printing of hierarchical structures with both controlled morphologies through phase separation and macroscale architecture through print design. We find that formulations for phase-separating materials require judicious incorporation of additives to enable printability and to provide sufficient green strength. Atomic force microscopy-nano infrared mapping reveals tunable, reticulated nano- to micron-scale domains of the resultant multiphase materials and their morphology changes due to additives, resulting in alterations to thermomechanical and tensile properties. Shape memory behavior is also demonstrated through multimaterial additive manufacturing of epoxies with functionally graded internal morphology using active mixing techniques, highlighting this method’s ability to fabricate complex architectures with controlled morphologies and thermomechanical response.

Van Meter, Kylie E [Organic Materials Science, San↗
DOE OSTI2026

A Permanently Porous Chalcogen-Bonded Organic Framework

The nature of connectivity between constituent atomic or molecular building blocks is fundamental in shaping the properties and functionality of materials. The extrapolation of emergent interatomic interactions to enable functional materials has driven transformative technological advancements. However, the bonding interactions used in material design have been largely static since the emergence of dynamic covalent chemistry ~30 years ago. Here we demonstrate that non-covalent chalcogen bonding (Ch-bonding) is a distinct mode of interatomic connectivity for constructing functional materials by design. This is established by leveraging self-complementary assembly of 1,2,5-telluradiazole moieties to construct a honeycomb-type permanently porous Ch-bonded organic framework, assembled and stabilized solely through non-covalent Te...N contacts. Empirical and computational studies of electronic structure, structural healing and lattice dynamics highlight the pi-type electronic communication, controlled assembly and modulated lattice dynamics in Trip3Tez-I arising directly from the unique nature of the Te...N Ch-bonding that holds substantial implications for next generation crystalline semiconductors. In addition to introducing a distinct class of permanently porous frameworks, this work establishes Ch-bonding as a programmable molecular tool for constructing functional materials with distinct properties.

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

Flow synthesis of iron oxide nanoparticles: using multiple precursor additions to improve size control

Controlling the size of iron oxide nanoparticles while maintaining uniformity in flow-based synthesis systems has been a great challenge in nanoparticle synthesis. Using an extended LaMer mechanism, we improve both the size and shape uniformity as compared to a conventional flow synthesis. The key to this approach is the injection of additional Fe precursor during the flow reaction, providing extra precursor during the growth process leading to a size focusing step. This approach has also allowed for the systematic variation of the nanoparticle size produced in the flow reaction. This work represent a new synthetic concept for improving the size uniformity of iron oxide nanoparticles in flow-based synthesis and has potential for application across a wide range of nanoparticle systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Topographic effects on reflected acoustic waves from the OSIRIS-REx reentry observed from stratospheric balloons

During long-distance sound propagation in planetary atmospheres, acoustic waves may reflect off the air/surface interface one or more times. For low sound frequencies and flat interfaces, the incident and reflected wave tend to be nearly identical. However, this may not be the case when the downgoing acoustic wave encounters topography. Here, we describe a set of direct and reflected acoustic signals recorded on free-flying balloons during the hypersonic entry of the OSIRIS-REx sample return capsule (SRC). In two of the three cases presented here, an impulsive reflected arrival similar in form to the direct sonic boom of the SRC is observed, followed by a diffuse coda. In contrast, one of the floating stations lacked an impulsive reflection entirely, with only the coda present. We use full waveform modeling to show how reflection in the presence of complex topography can explain coda seen in all three examples as well as the lack of impulsive arrival on the third. Our results indicate that the complex signals often observed in long range acoustic propagation could be due, in part, to interactions with topography during transmission.

Lees, Jonathan M. [University of North Carolina, C↗
DOE OSTI2026

Molecular origin of anisotropic shear elastoplasticity in chitin

Chitin nano- and mesoscale structures present in the exoskeleton of crustaceans exhibit exceptional longitudinal stiffness and toughness, rivaling or even exceeding that of many synthetic polymer architectures. Here, we reveal the origin of the asymmetric shear response in chitin multiscale architectures, marked by pronounced anisotropy in deformation. Under shear aligned with the molecular axis, chitin accommodates strain through coherent atomic rearrangements that enable elastic recovery. In contrast, shear applied perpendicular to this axis induces liquid-like plasticity via localized sliding. These results demonstrate the intrinsic mechanical anisotropy of chitin, underpinning a dual function in resisting repetitive loading while dissipating internal stress during high-strain events. Our findings establish the molecular basis of shear elastoplasticity in multiscale chitin structures, wherein axial elasticity supports energy storage in load-bearing regions, whereas transverse plasticity enables controlled energy dissipation. These atomic-scale insights lay a foundation for the predictive design of chitin-based materials with tunable strength-toughness profiles.

Wan, Zhangmin [University of British Columbia, Van↗
DOE OSTI2026

Quantifying the dislocation content of atomically resolved grain boundary line defects using the Nye tensor

The Nye tensor, which quantifies the density of Burgers vector for a given dislocation line direction, can be effectively used to characterize dislocation content in bulk crystals from atomic-resolution transmission electron microscopy images. The Nye tensor can be calculated from these images, in part because the reference state is simply defined by the lattice of the perfect crystal. The application of the Nye tensor to interfacial line defects, for which the natural reference state is the dichromatic pattern of the two grains in their reference orientation, poses additional challenges. In this work, we present a method that employs the Nye tensor to characterize the edge dislocation content of line defects at grain boundaries from atomic-resolution images. This approach enables us to rapidly characterize all edge dislocation content along a grain boundary. Additionally, the Nye tensor provides information about line defect core structure. Finally, we demonstrate this method on two exemplar defects: a twin boundary disconnection and a facet junction in face-centered cubic Au.

Crystallographic defects↗
DOE OSTI2023

Flame retardant biogenic building insulation materials from hemp fiber

Biogenic thermal insulation materials are in high demand because of its carbon-sequestration nature. However, high flammability, moisture condensation, and relatively high thermal conductivity of biogenic material are major concerns for sustainable building applications. In this study, we report the fire-retardant cellulose xerogel insulation nanocomposites derived from hemp fiber recycling and silica xerogel, in which the boric acid treatment improves its fire retardancy. The as-prepared materials show a low thermal conductivity of 31.3 mW/m K, high flexural modulus of 665 MPa, hydrophobicity with the water contact angle of 115°, and fire retardancy with 30% weight loss over a period of burning time 10 min. Overall, this work provides an effective method for the synthesis of fire-retardant biogenic thermal insulation materials and shows a promising way for next-generation bio-based insulation materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toyota Highlander FCHV (CRADA CRD-12-00469 Final Report)

This project relates to the loan of four Toyota Mirai FCEV-adv vehicles to NLR to provide a load (vehicles to fill with hydrogen) to our fueling station research facility to study hydrogen fueling infrastructure performance using 700 bar precooled hydrogen at ESIF’s Hydrogen Infrastructure Testing and Research Facility (HITRF) facility.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chiral edge state control of thermoelectric effects

Thermoelectric responses in two-dimensional electron gases subjected to magnetic fields have the potential to provide unique information about quasiparticle statistics. In this study, we show that chiral edge states play a key role in thermoelectric Hall bar measurements by completely controlling the direction of the internal thermal gradient. To this end, we perform measurements of the magnetothermoelectric responses of cadmium arsenide quantum wells. The magnetothermoelectric responses in the quantum Hall regime agree with theoretical predictions if one considers the role of chiral edge states, which flow in opposite directions on either side of the Hall bar and establish an internal temperature gradient that is perpendicular to the externally applied thermal gradient. We show that the results are self-consistent within this picture under different measurement conditions. We discuss potential applications of the findings, such as in nanoscale control of local temperature gradients and thermoelectric effects along with the characterization of other topological systems with chiral edges states.

Science & Technology - Other Topics↗
DOE OSTI2026

Powering Large Loads: Solutions Across Transmission, Utility, and Facility Scales

This report synthesizes current strategies for accommodating large load growth through a review of publicly available technical literature and media insights around the United States (U.S.). Solutions are organized across three implementation scales (transmission, utility, and facility) and categorized according to common implementation pathways (including structural expansion, operations and efficiency, upgrades, and planning and policy).

Valdez, Raquel Lynn [Sandia National Laboratories ↗