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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O 3 . Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

Over the past several decades, electron and scanning probe microscopes have become critical components of condensed matter physics, materials science and chemistry research. At the same time, the infrastructure for establishing a connection between microscopy observations and materials behaviour over a broader parameter space is lacking. In this work, we introduce AtomAI, an open-source software package bridging instrument-specific Python libraries, deep learning and simulation tools into a single ecosystem. AtomAI allows direct applications of deep neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders for disentangling structural factors of variation and im2spec type of encoder–decoder models for mapping structure–property relationships. Finally, our framework allows seamless connection to the first principles modelling with a Python interface on the inferred atomic positions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Informed unsupervised machine learning analysis of dislocation microstructure from high-resolution differential aperture X-ray structural microscopy data

This study leverages high-resolution differential-aperture X-ray structural microscopy (DAXM) to probe the local dislocation structure in deformed 304L-stainless steel at small strain, by measuring the lattice rotation and deviatoric elastic strain with a sub-micron resolution. For a single grain in a polycrystalline specimen, the measured lattice rotation field over the measured volume exhibited a multimodal distribution while the deviatoric elastic strain showed a single-mode distribution. An unsupervised Cauchy mixture machine learning model was developed to resolve the multimodal distribution of the lattice rotation. By mapping the lattice rotation data associated with each Cauchy peak in the model back onto the measured volume, we identify contiguous regions of the crystal rotated near the average values corresponding to the peaks of the overall rotation distribution. These regions represent the grain subdivision in the microstructure. Finally, the dislocation density tensor was also computed and its norm was laid over the rotation field to detect the subgrain boundaries. This step provided a validation of the Cauchy mixture model for the analysis of the lattice rotation distribution. The current study highlights the integration of advanced X-ray microscopy techniques with data-driven analysis methods to uncover detailed microstructure scales in deformed crystals.

Machine learning; Lattice rotation; High-energy X-↗
DOE OSTI2025

Bio‐Inspired In Situ Tuning of the Hydrophobic Environment Around Catalytically Active Organic Ligand‐Stabilized Ruthenium Nanoparticles

Abstract The organic ligand environment surrounding enzymatic and homogeneous catalytic active sites often determines catalytic activity. Ruthenium nanoparticles, ≤1 nm in diameter, are synthesized using monodentate thiol, monodentate phosphine, and bidentate bisphosphine ligands. Even though some of the ruthenium surface is blocked by the ligands, catalytic activity is still observed for CO oxidation and H 2 O 2 decomposition. All three ligand‐stabilized ruthenium nanoparticles have similar CO oxidation rates; however, the bisphosphine‐stabilized Ru nanoparticles are approximately 2.5 times less active than the monothiol‐stabilized and monophosphine‐stabilized ruthenium nanoparticles for H 2 O 2 decomposition. It is observed that the organic ligand environment is modulated in situ during nanoparticle synthesis via partial oxidation of the bisphosphine as confirmed by 31 P NMR measurements. We hypothesize that bisphosphine‐bound Ru nanoparticles consist of a Ru core with some of the ligands bound in a monodentate manner where the other P atom is oxidized and not bound to the Ru surface leading to a thicker hydrophobic layer around the Ru nanoparticles. The increase in hydrophobicity is confirmed via contact angle and zeta potential measurements. H 2 O 2 decomposition rates are known to decrease with increasing hydrophobicity, and this work illustrates a pathway for increasing hydrophobicity in situ using ligand‐bound metallic nanoparticles.

Sufyan, Sayed Abu [Department of Chemical Engineer↗
DOE OSTI2021

Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Significance Liquid cell transmission electron microscopy (LCTEM) is an emerging technique, which enables nanoscale visualization and tracking of single nanoparticles near interfaces with unprecedented spatial resolution. Here, we studied the diffusion of nanoparticles in LCTEM experiments using techniques powered by deep neural networks and statistical tests. We observed two underlying regimes of diffusive behavior which are governed by the interaction of the electron beam, the nanoparticle, the nearby substrate, and the liquid environment. This understanding forms the foundation to use LCTEM for single-nanoparticle tracking for a broad range of nanoparticles, interfaces, and liquids.

Jamali, Vida↗
NASA NTRS1989

Electron beam analysis of particulate cometary material

Electron microscopy will be useful for characterization of inorganic dust grains in returned comet nucleus samples. The choice of instrument(s) will depend primarily on the nature of the samples, but ultimately a variety of electron-beam methods could be employed. Scanning and analytical (transmission) electron microscopy are the logical choise for morphological, mineralogical, and bulk chemical analyses of dust grains removed from ices. It may also be possible to examine unmelted ice/dust mixtures using an environmental scanning electron microscope equipped with a cryo-transfer unit and a cold stage. Electron microscopic observations of comet nuclei might include: (1) porosities of dust grains; (2) morphologies and microstructures of individual mineral grains; (3) relative abundances of olivine, pyroxene, and glass; and (4) the presence of phases that might have resulted from aqueous alteration (layer silicates, carbonates, sulfates).

Bradley, John↗
DOE OSTI2025

Designing Antifouling and Antimicrobial Interfaces: Structural Characterization using CryoEM, Automated Microscopy, and AI Image Segmentation

The design of functionalized surfaces for interactions with biological systems is critical across sectors such as healthcare, energy, and agriculture. Tailoring materials for specific applications, such as antifouling and antimicrobial surfaces, demands a comprehensive understanding of topology and chemistry across multiple length and time scales on both biological and materials systems. This work presents the development and characterization of nanostructured surfaces with controlled topographies and chemistries that enhance bacterial membrane disruption, reduce biofilm formation, and improve antimicrobial and antifouling capabilities. Two specific use cases will be presented - the use of cellulose nanocrystals (CNCs) for bacterial growth inhibition and the development of antifouling surfaces to prevent protein and bacterial adsorption [1-4]. By leveraging large language models (LLMs) for image segmentation and training [5], we enable automated analysis of terabyte-scale cryogenic electron microscopy (cryoEM) datasets. This analysis provides statistical insights into the biotic/abiotic interface and facilitates automated electron microscopy experiments to mitigate time and dose. The integration of cryogenic electron tomography (cryoET) and cryogenic focused ion beam (cryoFIB) milling enables high-resolution, near-native-state imaging and 3D reconstructions of bio/material interfaces [6]. Orthogonal characterization techniques and computational modeling further enhances our understanding, offering a robust platform for the design and optimization of next-generation functional surfaces [7].

Williams, Alexis [ORNL] (ORCID:0000000252835822)↗
NASA NTRSDate not supplied

Contamination, Transportation or Transmutation in LENR Material Analyses

LENR experiments using various loading and triggering mechanisms rely on a variety of elemental and isotopic assays to determine LENR effects. These assays include optical microscopy, Scanning Electron Microscopy with Energy Dispersive X-Ray Analysis (SEM/EDX), X-Ray Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM). All of these methods observe the surface of a material. By dissolving or vaporizing the sample, Inductively Coupled Plasma Optical Element Spectroscopy (ICP-OES) can determine elemental composition (ppb) or with mass spectroscopy, isotopes (ICP/MS). One can use a focused ion beam (FIB) to cut open a sample and observe the “cut” with either SEM/EDX, or Time-of-Flight Secondary Ion Mass Spectroscopy (TOF-SIMS). High Purity Gamma Ray Spectroscopy (HPGe) and alpha/ beta Liquid Scintillator spectroscopy can be used. We’ve employed these assay methods in our NASA GRC research under both the Advanced Energy Conversion and Lattice Confinement Fusion Projects. However, there are limitations associated with each of these methods ranging from handling contamination, instrumental limitations including sensitivity and field of view, and sample preparation.

Low Energy Nuclear Reactions↗
NASA NTRS2009

Stardust Interstellar Preliminary Examination (ISPE)

In January 2006 the Stardust sample return capsule returned to Earth bearing the first solid samples from a primitive solar system body, C omet 81P/Wild2, and a collector dedicated to the capture and return o f contemporary interstellar dust. Both collectors were approximately 0.1m(exp 2) in area and were composed of aerogel tiles (85% of the co llecting area) and aluminum foils. The Stardust Interstellar Dust Col lector (SIDC) was exposed to the interstellar dust stream for a total exposure factor of 20 m(exp 2-) day during two periods before the co metary encounter. The Stardust Interstellar Preliminary Examination ( ISPE) is a three-year effort to characterize the collection using no ndestructive techniques. The ISPE consists of six interdependent proj ects: (1) Candidate identification through automated digital microsco py and a massively distributed, calibrated search (2) Candidate extr action and photodocumentation (3) Characterization of candidates thro ugh synchrotronbased FourierTranform Infrared Spectroscopy (FTIR), S canning XRay Fluoresence Microscopy (SXRF), and Scanning Transmission Xray Microscopy (STXM) (4) Search for and analysis of craters in f oils through FESEM scanning, Auger Spectroscopy and synchrotronbased Photoemission Electron Microscopy (PEEM) (5) Modeling of interstell ar dust transport in the solar system (6) Laboratory simulations of h ypervelocity dust impacts into the collecting media

Westphal, A. J.↗
DOE OSTI2023

Spectro-Microscopy Studies of Atmospheric Particles

Our project investigated composition and physical properties of individual atmospheric particles collected in field campaigns organized by the DOE Atmospheric Systems Research and Atmospheric Radiation Measurement (ASR/ARM) programs, such as the Green Ocean Amazon (GoAmazon, 2014/5), the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE, 2016/7), the Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA, 2017/8), the Aerosol–Ice Formation Closure Pilot Study (AEROICESTUDY, 2019) and in additional small-scale supporting field experiments focused on the investigation of the light-absorbing (aka brown carbon) atmospheric particles. In these studies, we investigated the contribution of natural and anthropogenic sources to populations of atmospheric particles during representative atmospheric conditions; characterized particle composition and evaluated their hygroscopic properties and propensity to act as cloud condensation nuclei (CCN) and ice nuclei (IN); correlated typical particle-type and mixing state characteristics with real-time measurements in the areas of studies. We used multi-modal spectro-microscopy techniques to examine elemental and molecular composition, mixing state, size, and higher order morphology for individual particles. Specifically, we used computer-controlled scanning electron microscopy and X-ray microanalysis for quantitative analysis of particle elemental composition, and we used transmission electron microscopy to provide additional information on the distribution of different components within individual particles (particle heterogeneity). We used synchrotron-based scanning transmission X-ray microscopy for quantitative description of the mixing state and molecular bonding of carbon in particles. The particle composition, mixing state, and morphology from analyzed periods was then combined with the real-time ARM measurements of aerosol size distribution, CCN concentration, hygroscopicity, and aerosol bulk composition. Combined together, results of our studies allowed us to assess the major particle sources (biogenic vs. anthropogenic, primary vs secondary) and gain insight into the atmospheric processing of aerosol particles resulting from condensation, coagulation, oxidative aging, and cloud processing. In collaboration with other scientists involved in field campaigns, we carried out statistical analysis of particle properties and their source-specific contributions to regional aerosol loading under representative air mass, meteorological, and cloud conditions.

scanning transmission X-ray microscopy↗
DOE OSTI2021

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of x-ray diffraction, photoluminescence, Raman spectra, and other data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Helium Ion Microscopy

Nanotechnology research led to the development of various instruments to be used for imaging or fabrication. The helium ion microscope (HIM) became available in 2006 but quickly gained popularity. Its unique gas field ion source enables high-resolution imaging, nanofabrication, direct write, and the recent combination with a secondary ion mass spectrometer offers chemical imaging with ~15 nm resolution. Particularly attractive are the small interaction volume of He and Ne (the two gases offered), small beam spot size, and a moderate sputtering rate. Additionally, the HIM is an excellent imaging tool for insulating samples in soft, polymeric, and biological materials. It can be expected that helium ion microscopy will continue to have a strong influence on broad range of scientific disciplines, including chemistry, materials science, and biology.This chapter aims at providing an overview of the current status of HIM for imaging, analysis, and nanofabrication. It introduces the reader to key components of the instrument, theoretical background, and latest results in the areas of imaging, material fabrication, and chemical analysis capabilities of helium ion microscopy.

Belianinov, Alex↗
DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Analysis of laser-induced modification of collagen structure using nonlinear optical microscopy

Nonlinear optical microscopy is used to monitor changes in the collagen structure of patellar ligament after IR laser heating up to a temperature of 60 °C. Pre-denaturation modification of the tissue matrix exhibits specific properties and consists in splitting of initially parallel fibre bundles into array of divergent fibres. Image processing employing Fast Fourier transform analysis is used for characterising images of the fibre structure and determining the collagen orientation index. An increase in manifestation of laser-induced disordering with an increase in IR laser radiation power from 0.3 to 2 W is accompanied by a decrease in the collagen orientation index. The specific disorganisation of the collagen tissue structure during laser heating is supposed to be due to fast speed and local action of the laser heating, which leads to mechanical distortions of the structural elements of tissue matrix. (paper)

36 MATERIALS SCIENCE↗
DOE OSTI2021

Helium Ion Microscopy with Secondary Ion Mass Spectrometry for Nanoscale Chemical Imaging and Analysis of Polyolefins

To date, chemical imaging of polymers and polymer blends has been primarily accomplished using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to directly visualize the distribution of components in a complex material with spatial resolution ranging from 100 nm to 5 μm. However, in many cases, this resolution falls far short of visualizing interfaces directly. To overcome these limitations, recent work has focused on developing a SIMS detection system based on a helium ion microscope (HIM) enabling chemical imaging with a demonstrated ~14 nm spatial resolution. Here, we utilize HIM-SIMS for differentiation between the olefin-based polymers of polyethylene (PE) and polypropylene (PP). We illustrate both analyses for separating PE and PP using specific mass fragment ratios as well as demonstrate spatially resolved imaging of phase-separated domains within PE. Altogether, we demonstrate the abilities of HIM-SIMS as a multimodal chemical technique for imaging and quantification of polyolefin interfaces, which could be more broadly applied to the analysis of more complex polymeric systems.

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

Structural and Mechanical Analysis of Individual Mineralized Collagen Fibrils Using In Situ Transmission Electron Microscopy

Bone serves as an example of nature’s architectured material with its characteristic blend of strength and toughness, all at a lightweight design. Given the hierarchical nature of these materials, it is essential to understand the governing mechanisms and organization of their constituents across length scales for bioinspired structural design. Despite recent advances in transmission electron microscopy (TEM) that have allowed us to witness the hierarchical arrangement of bone at micro-down to the nanoscale, we are still missing the details about the structural organization and mechanical properties of the main building blocks of bone─mineralized collagen fibrils (MCFs). Here, we present a method to extract individual MCFs from nature’s model material, mineralized turkey leg tendon, using a dropcasting procedure. By isolating the MCFs onto TEM supporting grids, we visualized the arrangement of organic and mineral phases within individual MCFs at the nanoscale. Using a four-dimensional scanning transmission electron microscopy (4D-STEM) approach, the orientation of individual mineral crystals within the MCFs was examined. Furthermore, we conducted in situ tensile experiments, revealing exceptional tensile strains of at least 8%, demonstrating the intricate relationship between structural organization and the mechanical behavior of MCFs. These insights into the ultrastructure of mineralized tissue building blocks, as well as the proposed sample-extraction method compatible with in situ mechanical testing, provide a strong basis for research into nature-inspired material design.

4D-STEM↗
DOE OSTI2025

Machine Learning Enabled Advanced Electron Tomography for Resolving Chemical Inhomogeneity and Materials Dynamics in Lithium-Ion Battery Electrodes

The objective of this project is to develop machine learning-assisted electron microscopy, together with three-dimensional, cryogenic, and in-situ imaging techniques, for resolving chemical inhomogeneity and materials dynamics in lithium battery electrodes and interfaces. The project aims to expand the spatial, temporal, and dimensional resolution of transmission electron microscopy and to enable quantitative analysis of beam-sensitive battery materials.

25 ENERGY STORAGE↗
DOE OSTI2022

DED Additively Manufactured HEAs Optimized via Parametric Study of Functionally Graded Materials

Complex concentrated alloys (CCAs) are a system of alloys composition of nearly equiatomic elements, creating a state of high configurational entropy in the material. These alloys are of interest for various industrial applications due to their high strength and hardness, wide operational temperature range, creep and diffusion resistance, and radiation resistance. In the current work CoCrFeNi-base CCAs are synthesize by combining three commonly industrial alloys: IN718, SS316L, and 70Co30Cr (commercially known as Stellite 21), in-situ utilizing directed energy deposition (DED) additive manufacturing (AM). One bulk specimen is fabricated with a 1:1:1 ratio of IN718, SS316L, and 70Co30Cr where IN718 is a source of nickel and chrome, SS316 is a source of iron and chrome, and 70Co30Cr is a source of cobalt and supplemental chrome. Additionally, a functionally graded material (FGM) of the identified CCA system from SS316 was fabricated with 10 layers, where the initial five layers are the base material of SS316L and graded by steps of 20% to transition to the CCA system. Overall build quality, microstructure, compositional distribution, phases analysis, and microhardness were studied by laser optical microscopy, scanning election microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction analysis (XRD), and Vickers microhardness techniques. Overall build quality showed a successful bulk build with acceptable density, however unmelted particles are observed in the as-built alloy. Compositional results show that the system meets the Boltzmann’s hypothesis definition of an HEA, and that an FCC CCA was produced with increased hardness, small grain size, and superior hardness properties to the SS316L substrate. While initial XRD results show predominately FCC structure, EDS analysis of interdendritic regions show the existence of Laves phases in the material. The fabrication of an FGM CCA allowed for potential screening of additional alloy mixes which may be of interest to explore in future research, and the resulting composition is compared to predicted values. This novel method of CCA fabrication results in substantial cost savings of these CCA systems over traditional methods.

36 MATERIALS SCIENCE↗