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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 OSTI2022

Persistent hot carrier diffusion in boron arsenide single crystals imaged by ultrafast electron microscopy

Cubic boron arsenide (BAs) is promising for microelectronics thermal management because of its high thermal conductivity. Recently, its potential as an optoelectronic material is also being explored. However, it remains challenging to measure its photocarrier transport properties because of small sizes of available high-quality crystals. Here, we use scanning ultrafast electron microscopy (SUEM) to directly visualize the diffusion of photocarriers in BAs single crystals. Surprisingly, we observed ambipolar diffusion at low optical fluence with persistent hot carrier dynamics for above 200 ps, which can likely be attributed to the large frequency gap between acoustic and optical phonons, the same feature that is responsible for the high thermal conductivity. At higher optical fluence, we observed spontaneous electron-hole separation. Our results show BAs is an attractive optoelectronic material combining high thermal conductivity and excellent photocarrier transport properties. Finally, our study also demonstrates the capability of SUEM to probe photocarrier transport in emerging materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An artificial intelligence’s interpretation of complex high-resolution in situ transmission electron microscopy data

Complicated nano- and atomic-scale processes with sub-angstrom spatial resolution and millisecond time resolution visualized by in situ transmission electron microscopy (TEM) are often highly dynamical and time consuming to analyze and interpret. Here, we report how variational autoencoders (VAEs) can provide an artificial intelligence’s interpretation of high-resolution in situ TEM data by condensing and deconvoluting complicated atomic-scale dynamics into a latent space with reduced dimensionality. We designed a VAE model with high latent dimensions capable of deconvoluting information from complex high-resolution TEM data. We demonstrate how this model, with high latent dimensions trained on atomically resolved TEM images of lead sulfide (PbS) nanocrystals, is able to capture movements and perturbations of periodic lattices in both simulated and real in situ TEM data. Importantly, the VAE model shows the capability of detecting and deconvoluting dynamical nanoscale physical processes, such as the rotation of crystal lattices and intra-particle ripening during the annealing of semiconductor nanocrystals.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A machine-learning approach to measure 3D sample properties from 2D Transmission Electron Microscopy images

Transmission Electron Microscopy (TEM) is a powerful tool for the characterization of materials at the nanoscale; however, its inherent two-dimensional (2D) nature poses significant challenges to accurately measure three-dimensional (3D) properties. We introduce a supervised machine-learning model that predicts 3D structural information, such as sample thickness and curvature, from a series of conventional 2D TEM images. The model, a U-Net convolutional neural network, is trained on a large synthetic dataset generated from dynamical diffraction simulations that model TEM’s complex, nonlinear image formation, accounting for sample thickness and curvature. This physically realistic framework enables exploration of a broad parameter space impractical to sample experimentally. We demonstrate that the trained model has accurate predictions for experimental single-crystal silicon samples, achieving performance comparable to established measurement techniques. This work highlights the critical role of robust, simulation-based training in overcoming the limitations of real-world imaging artifacts and inconsistent sample geometries. By integrating machine learning with numerical simulations, we offer an efficient and scalable framework for quantitative TEM analysis, paving the way for more sophisticated 3D characterization of complex materials.

Dynamical diffraction↗
DOE OSTI2021

Biofouling of filtration membranes in wastewater reuse: In situ visualization with confocal laser scanning microscopy

The filtration membranes utilized in advanced treatment of municipal wastewater are prone to biological fouling. Permeability loss and water recovery limitations due to fouling necessitate additional pretreatment and membrane cleaning, which raise the cost of water reuse. Better fouling mitigation calls for a deeper understanding of biofilm behavior on water reuse membranes. This study uses confocal microscopy to monitor biofouling under realistic conditions. To simulate water reuse, a miniature flow cell with microfiltration and ultrafiltration membranes was operated under typical filtration conditions using secondary effluent from California's East Bay Municipal Utility District. The growing biofilms were stained for extracellular polymeric substances and live and dead cells, and they were monitored in situ under filtration conditions with a confocal laser scanning microscope to produce 2-D and 3-D images. In situ image series revealed biofilm growth and removal processes including internal and external fouling, extracellular polymeric substance production, wrinkling, delamination, and film re-deposition. Additionally, changes in biofilm morphology between in situ and ex situ images highlighted limitations of ex situ imaging. Here, these results provide insight into the physical and biological mechanisms of biofouling in hopes of informing the development of improved techniques for biofouling mitigation.

ultrafiltration↗
DOE OSTI2023

Measurement of G-phase volume fraction and number density in duplex stainless steels using transmission electron microscopy

Duplex stainless steels (DSS) have a high toughness and strength due the presence of both austenitic and ferritic phases. These alloys have had limited use in power production applications due to thermal embrittlement caused by spinodal decomposition and development of G-phase precipitates in the ferrite. Lean grade DSS alloys (e.g., 2101, 2003) may offer improved thermal stability due to the reduction of Cr- and Ni-equivalent elements when compared to standard grade compositions (e.g., 2205, 2209). The abundance of the G-phase was measured in five duplex stainless steels, three wrought alloys (2101, 2003, 2205) and their matching filler metals (2101-w, 2209-w), after aging at 427 °C for 1000 h and 10,000 h. The G-phase volume fraction, number density, size, and precipitate spacing were found using quantitative analysis of transmission electron microscopy dark field images and the composition of G-phase precipitates on other clusters were characterized with atom probe tomography (APT). In the welded alloys, the G-phase was found to develop rapidly, relative to the wrought material. A positive correlation was found between the nickel equivalent composition of the alloy and the G-phase volume fraction. The alloys 2205, 2209, and 2101-w, which are higher in Cr and Ni, all showed significant G-phase precipitation, further strengthening the hypothesis that lean grade DSS alloys are more thermally stable against precipitation in the ferrite. Electron diffraction showed a secondary phase present in the 2101 wrought alloy at 10,000 h, but it was not crystallographically consistent with the G-phase; APT showed the presence of nanoclusters rich in both nickel and copper for this alloy. In conclusion, no secondary phases or clusters were found in 2003 after 10,000 h of aging, so it may be a candidate alloy for applications that require long-life times at high operating temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Lithium metal stripping mechanisms revealed through electrochemical liquid cell electron microscopy

An understanding of lithium stripping is as important as that of lithium plating to achieve significant advances in using lithium metal anodes for high-energy rechargeable batteries. However, there have been limited studies on lithium stripping compared to lithium plating. Here we report the lithium stripping mechanisms revealed through in-situ electrochemical liquid cell transmission electron microscopy (TEM). We directly observe and compare the stripping behavior of the in-situ grown lithium dendrites and lithium nanograins covered by a lithium fluoride-rich solid-electrolyte interphase (SEI). Furthermore, we find the sporadic lithium stripping behavior and three important modes that can describe the stripping of individual lithium deposits, regardless of their morphology: (i) symmetric stripping, (ii) surface-preferred asymmetric stripping, and (iii) interface-preferred asymmetric stripping. In addition, SEI chemical mapping with high spatial resolution shows a remarkable SEI loss at the end of the lithium metal stripping, which illustrates the importance of SEI protection in the subsequent cycles.

25 ENERGY STORAGE↗
DOE OSTI2024

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this paper a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

3D instance segmentation↗
DOE OSTI2023

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Characterization of Polyamide Thin Films by Atomic Force Microscopy

This study directly compares the mechanical behavior of novel molecular layer deposition (MLD) and analogous interfacial polymerization (IP) polyamide thin films in environments relevant to reverse osmosis (RO) membrane operation. The elastic modulus of the films was determined using atomic force microscopy (AFM) in dry, hydrated, and chlorinated states. Surface roughness characteristics were also obtained given their potential influence on AFM modulus measurements. The much smoother MLD films demonstrated a statistically higher modulus in all states as compared to their IP counterparts. The MLD films maintained a modulus ~3X and ~5X greater than that of IP films after hydration and chlorination, respectively. Such differences in behavior may be due to the higher density and correspondingly lower void content of the MLD films. Results from this study provide a rationale for future development of MLD for fabrication of polyamide films for incorporation in RO membranes.

atomic force microscopy↗
DOE OSTI2020

Optical super-resolution microscopy in polymer science

The advent of optical super-resolution microscopy (OSRM) over two decades ago has transformed light-mediated interrogation of systems down to the nanoscale. This innovative set of approaches to optics breaks the so-called diffraction limit of light (~200 nm), allowing for the first time to use optics in the far field to visualize behavior on nanoscopic length scales. While these visualization tools have rapidly found widespread use in biology and related fields, their implementation in materials and, more specifically, in polymer science has been far slower. To overcome possible barriers and highlight possible future directions, we present in this paper an introduction to OSRM for polymer scientists, beginning with an overview of relevant techniques and existing optical probes. We then show and examine the first examples of OSRM adaptation across major areas of polymer science, including: polymerization and structural characterization, self-assembly and solution behavior, bulk structure and behavior, crystallization, gel structure and behavior, phase transitions, and biofunctionality. We hope and anticipate that the discussions provided in this review will draw the attention of the polymer community to the capacities of these hitherto underexplored optical visualization techniques to further transform polymer science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Squeezing Noise in Microscopy with Quantum Light

Quantum-enhanced microscopes capable of probing classically inaccessible material properties have drawn increasing interest, but most proof-of-principle quantum microscopes have not surpassed classical limits. Here, we discuss the promise and obstacles associated with the use of quantum light with reduced noise (‘squeezed’ light) for quantum-enhanced atomic force microscopy, bioimaging, magneto-optical measurements, and Raman spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cepstral scanning transmission electron microscopy imaging of severe lattice distortions

The development of four-dimensional (4D) scanning transmission electron microscopy (STEM) using fast detectors has opened-up new avenues for addressing some of longstanding challenges in electron imaging. One of these challenges is how to image severely distorted crystal lattices, such as at a dislocation core. Here we develop a new 4D-STEM technique, called Cepstral STEM, for imaging disordered crystals using electron diffuse scattering. In contrast to analysis based on Bragg diffraction, which measures the average and periodic scattering potential, electron diffuse scattering can detect fluctuations caused by crystal disorder. Local fluctuations of diffuse scattering are captured by scanning electron nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron diffuse scattering are detected through Cepstral analysis and used for imaging. By integrating Cepstral analysis with 4D-STEM, we demonstrate that information about the distortive part of electron scattering potential can be separated and imaged at nm spatial resolution. Here, we apply the technique to the analysis of a dislocation core in SiGe and lattice distortions in a high entropy alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identification and correction of temporal and spatial distortions in scanning transmission electron microscopy

Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. Here, the analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Relation between sampling, sensitivity and precision in strain mapping using the Geometric Phase Analysis method in Scanning Transmission Electron Microscopy

The sensitivity and the precision of the Geometric Phase Analysis (GPA) method for strain characterization is a topic widely discussed in the literature and is usually difficult to quantify. Indeed, the GPA precision is intricately linked to the resolution of the strain maps defined when masking the periodic reflections in Fourier space. In this study an additional parameter, sampling, is proposed to be analyzed regarding the precision of GPA by developing the concept of a phase noise in the GPA equations. Both experimentally and theoretically, the following article demonstrates how the precision, and the sensitivity of the GPA method is improved when using a larger pixel spacing to record an electron micrograph in Scanning Transmission Electron Microscopy (STEM). In conclusion, the counterintuitive concept of increasing the field of view to improve the GPA precision results in an extension of the application of strain characterization methods in STEM towards low deformation levels.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Direct observation of oxygen vacancy ordering evolution in cerium oxide at varying concentrations via high-resolution transmission electron microscopy

Under illumination by a 300 keV electron beam, oxygen vacancy ordering structures are induced within cerium oxide grains. Here, our high-resolution transmission electron microscopy (HRTEM) study, supported by image simulation, reveals the evolution of these structures as vacancy concentration increases. The observed fluorite-type superlattice structures are identified as CeO 1.825 , CeO 1.75 , Ce 2 O 3 , displaying a gradient in oxygen vacancy concentration moving away from the grain surface. Correspondingly, the structural sequence transitions from Ce 2 O 3 to CeO 1.75 and then to CeO 1.825 . Without the constraints of surrounding grains, fluorite-type Ce 2 O 3 nanocrystals show a preference for transformation into an A-type trigonal structure. Notably, at temperatures up to 200°C, only the perfect fluorite structure is observed. Structural models were validated through both [110] and [001] projections. Our findings further confirm lattice expansion associated with local oxygen vacancy enrichment, which can be compensated by the formation of stacking faults, where a {111} oxygen plane is lost at defect sites.

Cerium oxide↗
DOE OSTI2021

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗