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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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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Majorana modes with side features in magnet-superconductor hybrid systems

Magnet-superconductor hybrid (MSH) systems represent promising platforms to host Majorana zero modes (MZMs), the elemental building blocks for fault-tolerant quantum computers. Theoretical description of such MSH structures is mostly based on simplified models, not accounting for the complexity of real materials. Here, based on density functional theory, we derive a superconducting 80-band model to study an MSH system consisting of a magnetic manganese chain on the s wave superconductor niobium. For a wide range of values of the superconducting order parameter, the system is a topological superconductor, with MZMs exhibiting non-universal spatial patterns and a drastic accumulation of spectral weight on both sides along the magnetic chain. These side feature states can be explained by an effective model which is guided by the ab initio results. Performing scanning tunneling spectroscopy experiments on the same system, we observe a spatial structure in the low-energy local density of states that is consistent with the theoretical findings. Our results open a first-principle approach to the discovery of topological superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Laser-based ultrasound interrogation of surface and sub-surface features in advanced manufacturing materials

Abstract Structures formed by advanced manufacturing methods increasingly require nondestructive characterization to enable efficient fabrication and to ensure performance targets are met. This is especially important for aerospace, military, and high precision applications. Surface acoustic waves (SAW) generated by laser-based ultrasound can detect surface and sub-surface defects relevant for a broad range of advanced manufacturing processes, including laser powder bed fusion (LPBF). In particular, an all-optical SAW generation and detection configuration can effectively interrogate laser melt lines. Here we report on scattered acoustic energy from melt lines, voids, and surface features. Sub-surface voids are also characterized using X-ray Computed Tomography (CT). High resolution CT results are presented and compared with SAW measurements. Finite difference simulations inform experimental measurements and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Ligand-triplet migration in iridium( iii ) cyclometalates featuring π-conjugated isocyanide ligands

The manipulation of the triplet excited state manifold leads to large differences in the photophysical properties within a given class of metal–organic chromophores. By the appropriate choice of ancillary ligand, large changes can be made both to the order and nature of the lowest excited states and therefore to the resulting photophysical properties. Herein, a series of four bis-2-phenylpyridine (ppy) cyclometalated Ir(III) compounds bearing two arylisocyanide ligands were synthesized and photophysically characterized to understand the effects of using ancillary ligands featuring systematic changes in π-conjugation. By varying the arylisocyanide ligands, the photoluminescence quantum yield ranged from 5% to 49% and the excited state lifetime ranged between 24 μs and 2 ms. These variations in photophysical response are consistent with lowering the triplet ligand-centered ( 3 LC) state of the arylisocyanide ligand as the π system was extended, confirmed by 77 K photoluminescence emission spectra and ultrafast transient absorption experiments. Here, the latter analysis gleaned detailed insight into the importance of the interplay of the 3 LC state of the phenylpyridine and arylisocyanide ligands in these polychromophic Ir(III) molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Sequentially regular polymer acceptors featuring flexible spacers for high-performance and mechanically robust all-polymer solar cells

Developing high-performance and mechanically robust polymer solar cells (PSCs) is crucial for realizing wearable power sources. While efficient all-polymer solar cells (all-PSCs) can be fabricated from polymerized small-molecule acceptors (PSMAs) with high optical absorption and electron mobilities, they still show limited mechanical robustness. Here, we achieve highly efficient and mechanically robust all-PSCs by designing a PSMA (PYFS-Reg) containing sequence-regular flexible spacers (FSs). The regular incorporation of the FS units into PSMAs is essential in simultaneously improving the electrical and mechanical properties of blend films. As a result, all-PSCs featuring PYFS-Reg achieve a high power conversion efficiency (PCE = 16.1%) and stretchability (crack onset strain (COS) = 22.4%), outperforming PSMAs without FSs (i.e., PYBDT, PCE = 12.6% and COS = 11.7%) or with randomly distributed FSs (i.e., PYFS-Ran, PCE = 12.2% and COS = 18.1%). Importantly, these all-PSCs are fabricated by an environmentally benign, non-halogenated solvent process. To further demonstrate their feasible applications in wearable devices, we construct intrinsically stretchable (IS) all-PSCs by using PYFS-Reg-based active layers, which exhibit a high PCE (10.6%) and excellent device stretchability (strain at PCE 80% = 36.7%).

14 SOLAR ENERGY↗
DOE OSTI2024

Accelerating nano-XANES imaging via feature selection

We utilize feature selection to reduce experimental time by ∼80% of a nanoscale X-ray Absorption Fine Structure (XANES) spectroscopy imaging study of a sample with Fe-bearing mineral phases.

36 MATERIALS SCIENCE↗
DOE OSTI2020

SUGAR: An improved empirical model of Type Ia supernovae based on spectral features

Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Mapping the Perseus galaxy cluster with XRISM: Gas kinematic features and their implications for turbulence

We present extended gas kinematic maps of the Perseus cluster based on a combination of five new XRISM/Resolve pointings observed in 2025 with four performance verification datasets from 2024, totaling a net exposure of 745 ks. To date, Perseus remains the only cluster that has been extensively mapped out to ≃0.7 r 2500 by XRISM/Resolve, while simultaneously offering sufficient spatial resolution to resolve gaseous substructures driven by mergers and active galactic nucleus (AGN) feedback. Our observations cover multiple radial directions and a broad range of dynamical scales, enabling us to characterize the kinematic properties of the intracluster medium up to a scale of ∼500 kpc. In the measurements, we detected high-velocity dispersions (≃300km s −1 ) in the eastern region of the cluster that are spatially coincident with the extended X-ray surface brightness excess and correspond to a nonthermal pressure fraction of ≃7 − 13%. The velocity field outside the AGN-dominant region can be effectively described by a single, large-scale kinematic driver based on the velocity structure function, which statistically favors an energy injection scale of at least a few hundred kpc. The estimated turbulent dissipation energy is comparable to the gravitational potential energy released by a recent merger, implying a significant role of turbulent cascade in the merger energy conversion. In the bulk velocity field, we observed a dipole-like pattern along the east-west direction with an amplitude of ≃ ± 200 − 300 km s −1 , indicating rotational motions induced by the recent merger event. This feature constrains the viewing direction to ≃30° −50° relative to the normal of the merger plane. Our hydrodynamic simulations suggest that Perseus has experienced at least two energetic mergers since redshift z ∼ 1, the most recent of which is associated with the radio galaxy IC310, in agreement with recent SRG/eROSITA findings. This study showcases exciting scientific opportunities for future missions with high-resolution spectroscopic capabilities (e.g., HUBS, LEM, and NewAthena).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Features of explosion of thin aluminum foils on an 8 kA, 350 ns pulse generator

This paper presents the results of a study of the explosion of Al foils with a thickness of 4 μm on an 8 kA, 350 ns pulsed-power generator. The features of the energy deposition process in the explosion of foils in comparison with wires of similar mass and cross section, as well as with thin wires, are studied. In the experiments presented here, the dependence of the energy deposition in the foil on its initial structure was found. In conclusion, two types of exploded foil structure were recorded using point-projection radiography

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Detrimental effects and mitigation of the joint feature in double shell implosion simulations

Double shell capsules provide an attractive option in inertial confinement fusion experiments due to their potential for achieving a low-convergence, robust burn. However, these designs suffer from symmetry degradation and accompanying reduced fuel confinement due to the currently necessary joint between the two hemispheres of the outer shell. Here, the gap widens as a result of the excess ablation pressure produced by x rays that penetrate the joint during the drive phase, and this perturbation grows and imprints onto the inner shell during the collision. xRAGE Eulerian radiation-hydrodynamic simulations predict significant reductions in deuterium–tritium fusion yields compared to joint-less simulations when the depth of the outer joint is increased, whereas the performance is less sensitive to the depth of the inner gap. Here we examine the technique of plating the insides of the outer gap with a high-Z material to mitigate the impact of this feature. Gold-plating in quantities comparable to or exceeding the “missing” outer shell mass shows promise toward restoring both implosion symmetry and yield closer to the joint-less levels, and synthetic diagnostics suggest that high-energy x-ray radiographs can capture this shape retention of the inner and outer shells in experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Temperature effects of ion irradiation on the nanostructural features in ductile-phase-toughened tungsten composites

Ductile-phase toughened tungsten (DPT W) composites have emerged as promising candidates for load-bearing components behind the plasma-facing tungsten armor in fusion reactors due to their enhanced thermomechanical properties. This study focuses on a composite consisting of W particles embedded in a ductile NiFeW solution matrix, hot-rolled to a thickness reduction of 87% (87R DPT W). Sequential irradiations with Ni2+ and He+ ions were performed to identical doses and helium concentrations at room temperature (RT) and 1273 K. Irradiation at RT produced no discernible nanostructural features due to the immobility of mono-vacancies, whereas cavity formation was observed at 973 K. At 1273 K, the W phase exhibited larger cavities, reduced cavity number density, and lower volumetric swelling compared to 973 K. Notably, nanosized NiFeW precipitates formed within the W phase at 1273 K, a phenomenon absent at 973 K. A new phase of cubic (NiFe)6W6C was also observed at the interphase boundary. In contrast, the NiFeW matrix showed no nanostructural changes at 1273 K, likely due to cavity dissociation. Separate irradiations at 1273 K indicated that Ni2+ ions induced precipitate formation in the W phase, while He+ ions exclusively caused cavity formation. The microstructure of 87R DPT W irradiated at RT and subsequently annealed at 1273 K closely resembled that of material irradiated directly at 1273 K. Like oxide-dispersion-strengthened steels, the observed nanoparticle-embedded W can inhibit dislocation propagation, potentially delaying the ductile-to-brittle transition temperature. These findings highlight the potential of NiFeW nanoparticle-reinforced W composites as irradiation-resistant materials for fusion reactors.

Jiang, Weilin (ORCID:0000000183028313)↗
DOE OSTI2025

Surrogate models to optimize plasma-assisted atomic layer deposition in high aspect ratio features

In this work, we explore surrogate models to optimize plasma enhanced atomic layer deposition (PEALD) in high aspect ratio features. In plasma-based processes such as PEALD and atomic layer etching (ALE), surface recombination can dominate the reactivity of plasma species with the surface, which can lead to unfeasibly long exposure times to achieve full conformality inside nanostructures like high aspect ratio vias. Using a synthetic dataset based on simulations of PEALD, we train artificial neural networks to predict saturation times based on cross section thickness data obtained for partially coated conditions. The results obtained show that just two experiments in undersaturated conditions contain enough information to predict saturation times within 10% of the ground truth. A surrogate model trained to determine whether surface recombination dominates the plasma–surface interactions in a PEALD process achieves 99% accuracy. This demonstrates that machine learning can provide a new pathway to accelerate the optimization of PEALD processes in areas such as microelectronics. Our approach can be easily extended to ALE and more complex structures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Spectral features of colloidal solutions of elongated gold nanoparticles produced by laser ablation in aqueous solutions

The extinction spectra of colloidal solutions of gold nanoparticles produced by laser ablation in water and aqueous solutions of salts using two near-IR lasers with pulse durations of 200 ns and 1 ps are experimentally investigated. The extinction spectrum of the particles formed by ablation in aqueous solutions is characterised by enhanced optical density in the red and IR regions. This feature is due to the formation of elongated gold nanoparticles, as confirmed by transmission electron microscopy. The surface images of a gold target subjected to multipulse laser ablation exhibit micron and submicron structures. (nanophotonics)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗