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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Probing Dynamic Phase Changes in Electrochemical Systems with Cryogenic and in Situ Analytical Electron Microscopy (Final Technical Report)

In this project, we focus on the advancement of our newly developed, unique in situ and cryogenic analytical electron microscopy (AEM) techniques and the development of additional high-end capabilities to quantitatively elucidate the mechanisms of resistive state changes in solid-state electrochemical materials for non-volatile, neuromorphic memory. The objectives of the proposed research are (1) to characterize and elucidate the mechanisms by which electronic state changes occur in various solid-state devices to build guiding principles for electrochemically activated smart materials; (2) to advance our understanding of such mechanisms by coupling cryogenic and in situ techniques to control kinetics of the observed mechanisms while mitigating probe-induced damage; and (3) to utilize additional advanced techniques including electron-beam induced current and quantum mechanical simulations in order to identify the underlying fundamental science of such processes, including roles of defect formation and its effect on phase nucleation, as well as dynamics of these processes in the electrochemical systems at the nanometer scale.

25 ENERGY STORAGE↗
DOE OSTI2026

Development of high throughput light-sheet fluorescence lifetime imaging microscopy for 3D functional imaging of metabolic pathways in plant and microorganisms (Final Technical Report)

This research program will enable new biochemical contrast in the nanosecond lifetime domain through use of the recently demonstrated electro-optic fluorescence lifetime imaging technique (EO-FLIM) for wide-field lifetime imaging. The Stanford/Stanford Linear Accelerator Center multidisciplinary collaboration -- physics, applied physics, and structural biology -- will develop a light-sheet fluorescence lifetime imaging microscope for functional studies of microbial and plant metabolic pathways and dynamic interactions between plants and microorganisms in the rhizosphere. The proposed approach overcomes the imaging time bottleneck associated with existing fluorescence lifetime imaging methods. Initial demonstrations have shown a factor of 100,000 improvement in photon throughput compared to existing methods. High photon efficiency allowed the first wide-field fluorescence lifetime imaging of single molecules. Recent work has improved the technique’s repetition rate to enable compatibility with mode-locked lasers and demonstrated the combination of wide-field fluorescence lifetime imaging with super-resolution localization microscopy, observations of single molecule dynamics, and observation of donor lifetime quenching in single-molecule imaging. These results were achieved on standard camera sensors and would not have been possible with other wide-field approaches. The throughput and photon economy of the EO-FLIM method enables new BER-relevant imaging opportunities. In particular, scanned single- and two-photon light-sheet excitation will be used to achieve volumetric imaging with time-domain contrast.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Investigation of the thermal decomposition of Pu(IV) oxalate: a transmission electron microscopy study

The degradation of the internal structure of plutonium (IV) oxalate during calcination was investigated with Transmission Electron Microscopy (TEM), electron diffraction, Electron Energy-Loss Spectroscopy (EELS), and 4D Scanning TEM (STEM). TEM lift-outs were prepared from samples that had been calcined at 300°C, 450°C, 650°C and 950°C. The resulting phase at all calcination temperatures was identified as PuO 2 with electron diffraction. The grain size range was obtained with high-resolution TEM. In addition, 4D STEM images were analyzed to provide grain size distributions. In the 300°C calcined sample, the grains were <10 nm in diameter, at 650°C, the grains ranged from 10 to 20 nm, and by 950°C, the grains were 95–175 nm across. Using the Kolmogorov-Smirnov (K-S) two sample test, it was shown that morphological measurements obtained from 4D-STEM provided statistically significant distributions to distinguish samples at the different calcination conditions. Using STEM-EELS, carbon was shown to be present in the low temperature calcined samples associated with oxalate but had formed carbon (possibly graphite) deposits in the 950°C calcined sample. This work highlights the new methods of STEM-EELS and 4D-STEM for studying the internal structure of special nuclear materials (SNM).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Use of X-Ray Fluorescence Microscopy for Studies on Research Models of Hepatocellular Carcinoma

Introduction: TheraSphere ® microspheres containing yttrium 90 Y are among many radioembolization agents used clinically to reduce liver tumor burden, and their effects on cancer volume reduction are well-established. At the same time, concerns about off target tissue injury often limit their use. Deeper investigation into tissue distribution and long-term impact of these microspheres could inform us about additional ways to use them in practice. Methods: Healthy rat liver and rabbit liver tumor samples from animals treated with TheraSpheres were sectioned and their elemental maps were generated by X-ray fluorescence microscopy (XFM) at the Advanced Photon Source (APS) synchrotron at Argonne National Laboratory (ANL). Results: Elemental imaging allowed us to identify the presence and distribution of TheraSpheres in animal tissues without the need for additional sample manipulation or staining. Ionizing radiation produced by 90 Y radioactive contaminants present in these microspheres makes processing TheraSphere treated samples complex. Accumulation of microspheres in macrophages was observed. Conclusions: This is the first study that used XFM to evaluate the location of microspheres and radionuclides in animal liver and tumor samples introduced through radioembolization. XFM has shown promise in expanding our understanding of radioembolization and could be used for investigation of human patient samples in the future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

X-ray Assisted Scanning Tunneling Microscopy and Its Applications for Materials Science: The First Results on Cu Doped ZrTe 3

Synchrotron X-ray Scanning Tunneling Microscopy (SX-STM) is a novel imaging technique capable of providing real space chemically specific mapping with a potential of reaching atomic resolution. Determination of chemical composition along with ultra-high resolution imaging by SX-STM can be realized through excitation of core electrons by incident X-rays when their energy is tuned to an absorption edge of a particular atom during raster scanning, as is done in the conventional STM experiments. In this work, we provide a brief summary and the current status of SX-STM and discuss its applications for material science. In particular, we discuss instrumentation challenges associated with the SX-STM technique and present early experiments on Cu doped ZrTe 3 single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Influence of Standard Image Processing of 3D X-ray Microscopy on Morphology, Topology and Effective Properties

Estimating porous media properties is a vital component of geosciences and the physics of porous media. Until now, imaging techniques have focused on methodologies to match image-derived flows or geomechanical parameters with experimentally identified values. Less emphasis has been placed on the compromise between image processing techniques and the consequences on topological and morphological characteristics and on computed properties such as permeability. The effects of some of the most popular image processing techniques (filtering and segmentation) available in open source on 3D X-ray Microscopy (micro-XRM) images are qualitatively and quantitatively discussed. We observe the impacts of various filters such as erosion-dilation and compare the efficiency of Otsu’s method of thresholding and the machine-learning-based software Ilastik for segmentation.

58 GEOSCIENCES↗
DOE OSTI2022

Molecular Dynamics Simulation and Cryo-Electron Microscopy Investigation of AOT Surfactant Structure at the Hydrated Mica Surface

Structural properties of the anionic surfactant dioctyl sodium sulfosuccinate (AOT or Aerosol-OT) adsorbed on the mica surface were investigated by molecular dynamics simulation, including the effect of surface loading in the presence of monovalent and divalent cations. The simulations confirmed recent neutron reflectivity experiments that revealed the binding of anionic surfactant to the negatively charged surface via adsorbed cations. At low loading, cylindrical micelles formed on the surface, with sulfate head groups bound to the surface by water molecules or adsorbed cations. Cation bridging was observed in the presence of weakly hydrating monovalent cations, while sulfate groups interacted with strongly hydrating divalent cations through water bridges. The adsorbed micelle structure was confirmed experimentally with cryogenic electronic microscopy, which revealed micelles approximately 2 nm in diameter at the basal surface. At higher AOT loading, the simulations reveal adsorbed bilayers with similar surface binding mechanisms. Adsorbed micelles were slightly thicker (2.2–3.0 nm) than the corresponding bilayers (2.0–2.4 nm). Upon heating the low loading systems from 300 K to 350 K, the adsorbed micelles transformed to a more planar configuration resembling bilayers. The driving force for this transition is an increase in the number of sulfate head groups interacting directly with adsorbed cations.

58 GEOSCIENCES↗
DOE OSTI2023

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals

One of the key challenges in laser powder bed fusion (LPBF) additive manufacturing of metals is the appearance of microscopic pores in 3D-printed metallic structures. Quality control in LPBF can be accomplished with non-destructive imaging of the actual 3D-printed structures. Thermal tomography (TT) is a promising non-contact, non-destructive imaging method, which allows for the visualization of subsurface defects in arbitrary-sized metallic structures. However, because imaging is based on heat diffusion, TT images suffer from blurring, which increases with depth. We have been investigating the enhancement of TT imaging capability using machine learning. In this work, we introduce a novel multi-task learning (MTL) approach, which simultaneously performs the classification of synthetic TT images, and segmentation of experimental scanning electron microscopy (SEM) images. Synthetic TT images are obtained from computer simulations of metallic structures with subsurface elliptical-shaped defects, while experimental SEM images are obtained from imaging of LPBF-printed stainless-steel coupons. MTL network is implemented as a shared U-net encoder between the classification and the segmentation tasks. Results of this study show that the MTL network performs better in both the classification of synthetic TT images and the segmentation of SEM images tasks, as compared to the conventional approach when the individual tasks are performed independently of each other.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Current measurements in the intermittent-contact mode of atomic force microscopy using the Fourier method: a feasibility analysis

Atomic force microscopy (AFM) is an important tool for measuring a variety of nanoscale surface properties, such as topography, viscoelasticity, electrical potential and conductivity. Some of these properties are measured using contact methods (static contact or intermittent contact), while others are measured using noncontact methods. Some properties can be measured using different approaches. Conductivity, in particular, is mapped using the contact-mode method. However, this modality can be destructive to delicate samples, since it involves continuously dragging the cantilever tip on the surface during the raster scan, while a constant tip–sample force is applied. In this paper we discuss a possible approach to develop an intermittent-contact conductive AFM mode based on Fourier analysis, whereby the measured current response consists of higher harmonics of the cantilever oscillation frequency. Such an approach may enable the characterization of soft samples with less damage than contact-mode imaging. To explore its feasibility, we derive the analytical form of the tip–sample current that would be obtained for attractive (noncontact) and repulsive (intermittent-contact) dynamic AFM characterization, and compare it with results obtained from numerical simulations. Although significant instrumentation challenges are anticipated, the modelling results are promising and suggest that Fourier-based higher-harmonics current measurement may enable the development of a reliable intermittent-contact conductive AFM method.

14 SOLAR ENERGY↗
DOE OSTI2021

A new method for obtaining model-free viscoelastic material properties from atomic force microscopy experiments using discrete integral transform techniques

Viscoelastic characterization of materials at the micro- and the nanoscale is commonly performed with the aid of force–distance relationships acquired using atomic force microscopy (AFM). The general strategy for existing methods is to fit the observed material behavior to specific viscoelastic models, such as generalized viscoelastic models or power-law rheology models, among others. Here we propose a new method to invert and obtain the viscoelastic properties of a material through the use of the Z-transform, without using a model. We present the rheological viscoelastic relations in their classical derivation and their z-domain correspondence. We illustrate the proposed technique on a model experiment involving a traditional ramp-shaped force–distance AFM curve, demonstrating good agreement between the viscoelastic characteristics extracted from the simulated experiment and the theoretical expectations. We also provide a path for calculating standard viscoelastic responses from the extracted material characteristics. The new technique based on the Z-transform is complementary to previous model-based viscoelastic analyses and can be advantageous with respect to Fourier techniques due to its generality. Additionally, it can handle the unbounded inputs traditionally used to acquire force–distance relationships in AFM, such as ramp functions, in which the cantilever position is displaced linearly with time for a finite period of time.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Characterizing Dislocation Defects in Dark Field X-ray Microscopy Images of Bulk Aluminum

Material defects play a large role in material response under shock loading, yet the process of how these defects initiate, propagate, and annihilate is not well understood. Using a newly developed diagnostic, dark field X-ray microscopy (DFXM), we can now visualize the behavior of dislocation defects in materials at the mesoscale under varying conditions. Using data from the European Synchrotron Radiation Facility, we apply a variety of image processing techniques to capture relevant features to locate and characterize size and orientation of dislocation defects in bulk aluminum DFXM images. Beyond simple visualization, this analysis drives statistical characterization of the defects and their dynamic response to further improve the relevant physics models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Examining Bioethanol-Producing Ultrastructures with Electron Microscopy and Molecular Dynamics

A longstanding goal in the biofuel sector is to increase the efficiency of the circularization of the use of materials (1). One such instance is the degradation of plant material, such as cellulose, and converting it to biofuel via bacterial digestion (2). The bifunctional aldehyde-alcohol dehydrogenase (AdhE) from the anaerobic thermophile C. thermocellum seems to be vital for the production and cellular tolerance of bioethanol; however, it lacks the efficiency to produce ethanol at industry standards (3). Therefore, to understand how C. thermocellum AdhE functions, we used cryo-electron microscopy (cryo-EM) to obtain a 3.2 A structure of the AdhE complex. When compared to previously published structures from E. coli (4-6), we identified potential regions that control the native conformation of the ultrastructure, as well as located channels that isolate the intermediate aldehyde from the cellular milieu. This high-resolution structure, in combination with molecular dynamics simulations, provides insight into one example of substrate channeling and establishes a basis for future mutagenesis studies.

AdhE↗
DOE OSTI2022

Leveraging Super High Optical Resolution Microscopy to Probe the Interaction Zone Between Clostridium thermocellum and Biomass

This poster, part of the Pacific Northwest National Laboratory-sponsored Integration 2020: Visualizing the Proteome virtual conference, discusses super high optical resolution microscopy to probe the interaction zone between Clostritium thermocellum and biomass. Clostridium thermocellum is one of the most efficient microorganisms for the deconstruction of biomass. To achieve this high level of cellulolytic activity, C. thermocellum uses large multienzyme complexes known as cellulosomes to sugars in 4-5 break down polysaccharides found in plant cell walls. The attachment of bacterial switchgrass 70% cells to the nearby substrate via the cellulosome has been hypothesized to be the reason for this high efficiency. The region lying between the cell and the substrate has shown great variation and dynamics that are affected by the growth stage of cells and the substrate used for growth.

bacteria↗