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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermoresponsive polymer assemblies via variable temperature liquid-phase transmission electron microscopy and small angle X-ray scattering

Abstract Herein, phase transitions of a class of thermally-responsive polymers, namely a homopolymer, diblock, and triblock copolymer, were studied to gain mechanistic insight into nanoscale assembly dynamics via variable temperature liquid-cell transmission electron microscopy (VT-LCTEM) correlated with variable temperature small angle X-ray scattering (VT-SAXS). We study thermoresponsive poly(diethylene glycol methyl ether methacrylate) (PDEGMA)-based block copolymers and mitigate sample damage by screening electron flux and solvent conditions during LCTEM and by evaluating polymer survival via post-mortem matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). Our multimodal approach, utilizing VT-LCTEM with MS validation and VT-SAXS, is generalizable across polymeric systems and can be used to directly image solvated nanoscale structures and thermally-induced transitions. Our strategy of correlating VT-SAXS with VT-LCTEM provided direct insight into transient nanoscale intermediates formed during the thermally-triggered morphological transformation of a PDEGMA-based triblock. Notably, we observed the temperature-triggered formation and slow relaxation of core-shell particles with complex microphase separation in the core by both VT-SAXS and VT-LCTEM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Observation of electron orbital signatures of single atoms within metal-phthalocyanines using atomic force microscopy

Resolving the electronic structure of a single atom within a molecule is of fundamental importance for understanding and predicting chemical and physical properties of functional molecules such as molecular catalysts. However, the observation of the orbital signature of an individual atom is challenging. We report here the direct identification of two adjacent transition-metal atoms, Fe and Co, within phthalocyanine molecules using high-resolution noncontact atomic force microscopy (HR-AFM). HR-AFM imaging reveals that the Co atom is brighter and presents four distinct lobes on the horizontal plane whereas the Fe atom displays a “square” morphology. Pico-force spectroscopy measurements show a larger repulsion force of about 5 pN on the tip exerted by Co in comparison to Fe. Our combined experimental and theoretical results demonstrate that both the distinguishable features in AFM images and the variation in the measured forces arise from Co’s higher electron orbital occupation above the molecular plane. The ability to directly observe orbital signatures using HR-AFM should provide a promising approach to characterizing the electronic structure of an individual atom in a molecular species and to understand mechanisms of certain chemical reactions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Abstract Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Structural polymorphism of amyloid fibrils in ATTR amyloidosis revealed by cryo-electron microscopy

ATTR amyloidosis is caused by the deposition of transthyretin in the form of amyloid fibrils in virtually every organ of the body, including the heart. This systemic deposition leads to a phenotypic variability that has not been molecularly explained yet. In brain amyloid conditions, previous studies suggest an association between clinical phenotype and the molecular structures of their amyloid fibrils. Here we investigate whether there is such an association in ATTRv amyloidosis patients carrying the mutation I84S. Using cryo-electron microscopy, we determined the structures of cardiac fibrils extracted from three ATTR amyloidosis patients carrying the ATTRv-I84S mutation, associated with a consistent clinical phenotype. We found that in each ATTRv-I84S patient, the cardiac fibrils exhibited different local conformations, and these variations can co-exist within the same fibril. Our finding suggests that one amyloid disease may associate with multiple fibril structures in systemic amyloidoses, calling for further studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Differential programming enabled functional imaging with Lorentz transmission electron microscopy

Abstract Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Rapid and flexible segmentation of electron microscopy data using few-shot machine learning

Abstract Automatic segmentation of key microstructural features in atomic-scale electron microscope images is critical to improved understanding of structure–property relationships in many important materials and chemical systems. However, the present paradigm involves time-intensive manual analysis that is inherently biased, error-prone, and unable to accommodate the large volumes of data produced by modern instrumentation. While more automated approaches have been proposed, many are not robust to a high variety of data, and do not generalize well to diverse microstructural features and material systems. Here, we present a flexible, semi-supervised few-shot machine learning approach for segmentation of scanning transmission electron microscopy images of three oxide material systems: (1) epitaxial heterostructures of SrTiO 3 /Ge, (2) La 0.8 Sr 0.2 FeO 3 thin films, and (3) MoO 3 nanoparticles. We demonstrate that the few-shot learning method is more robust against noise, more reconfigurable, and requires less data than conventional image analysis methods. This approach can enable rapid image classification and microstructural feature mapping needed for emerging high-throughput characterization and autonomous microscope platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Imaging atomic-scale chemistry from fused multi-modal electron microscopy

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic-resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark-field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty-aware particle segmentation for electron microscopy at varied length scales

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these images to identify distinct particles and determine their morphology requires careful analysis, and automating this process remains challenging. In this work, we enhance the Mask R-CNN architecture to develop a method for automated segmentation of particles in SEM images. We address several challenges inherent to measurements, such as image blur and particle agglomeration. Moreover, our method accounts for prediction uncertainty when such issues prevent accurate segmentation of a particle. Recognizing that disparate length scales are often present in large datasets, we use this framework to create two models that are separately trained to handle images obtained at low or high magnification. By testing these models on a variety of inorganic samples, our approach to particle segmentation surpasses an established automated segmentation method and yields comparable results to the predictions of three domain experts, revealing comparable accuracy while requiring a fraction of the time. These findings highlight the potential of deep learning in advancing autonomous workflows for materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

BEACON—automated aberration correction for scanning transmission electron microscopy using Bayesian optimization

Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order aberrations called BEACON, which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings. We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano- and atomic-scale experiments. BEACON can correct all first- and second-order aberrations simultaneously to achieve an initial alignment and first- and second-order aberrations independently for fine alignment. Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.

techniques and instrumentation↗
DOE OSTI2026

Deep generative learning of magnetic frustration in artificial spin ice from magnetic force microscopy images

Increasingly large datasets of microscopic images with nanoscale resolution facilitate the development of machine learning methods to identify and analyze subtle physical phenomena embedded within the images. In this work, microscopic images of honeycomb lattice spin-ice samples serve as datasets from which we automate the calculation of net magnetic moments and directional orientations of spin-ice configurations. In the first stage of our workflow, machine learning models are trained to accurately predict magnetic moments and directions within spin-ice structures. Variational Autoencoders (VAEs), an emergent unsupervised deep learning technique, are employed to generate high-quality synthetic magnetic force microscopy (MFM) images and extract latent feature representations, thereby reducing experimental and segmentation errors. The second stage of proposed methodology enables precise identification and prediction of frustrated vertices and nanomagnetic segments, effectively correlating structural and functional aspects of microscopic images. This facilitates the design of optimized spin-ice configurations with controlled frustration patterns, enabling potential on-demand synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Correlative operando microscopy of oxygen evolution electrocatalysts

Transition metal (oxy)hydroxides are promising electrocatalysts for the oxygen evolution reaction.1–3 The properties of these materials evolve dynamically and heterogeneously4 with applied voltage through ion insertion redox reactions, converting inactive materials at open-circuit into active electrocatalysts during operation.5 Thus, the catalytic state is inherently far-from-equilibrium, complicating its direct observation. Here, we establish the link between the oxygen evolution activity and the local operational chemical, physical, and electronic nanoscale structure of single-crystalline β-Co(OH)2 platelet particles through a suite of newly developed correlative operando scanning probe and X-ray microscopy techniques. At pre-catalytic voltages, the particles swell to form an α-CoO2H1.5·0.5H2O-like structure with associated Co2.5+ oxidation state produced through hydroxide intercalation. With increasing voltage to drive oxygen evolution, interlayer water and protons de-intercalate to form contracted β-CoOOH particles with Co3+ species. While these transformations manifest heterogeneously through the bulk of the particles, the electrochemical current is primarily restricted to the particle edge facets. The observed Tafel behavior is correlated to the local concentration of Co3+ at these reactive edge sites, demonstrating the link between bulk ion-insertion and surface catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantitative phase imaging by gradient retardance optical microscopy

Abstract Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A microchip microcontroller-based transducer controller for non-contact scanning probe microscopy with phase-locked loop, amplitude, and Q control

An inexpensive yet versatile transducer controller for non-contact scanning probe microscopy (SPM) based on a PIC32 microcontroller from Microchip Technology, Inc is described. In addition to feedback control using the amplitude or phase of the signal from the non-contact transducer, the controller includes a phase-locked loop for frequency-shift feedback, as well as fixed-amplitude, quality factor (Q) control, and self-excitation modes. Apart from the input amplifiers, output buffers, and the Q-control circuit, all other functions of the controller are instantiated in software on the microchip, enabling rapid changes in operating parameters if needed. The controller communicates with a host personal computer via a simple serial connection. The controller has been tested with a quartz tuning-fork transducer but can be used with any oscillating non-contact transducer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Revisiting heat treatment and surface activation of GaAs photocathodes: In situ studies using scanning tunneling microscopy and photoelectron spectroscopy

The lifetime of GaAs photocathodes in polarized electron guns is limited due to the delicate activation layer. An atomically clean and smooth GaAs surface is needed to deposit a robust activation layer, such as Cs 2 Te, with longer lifetime compared to traditional (Cs,O) activation. A previous experiment with Cs 2 Te activation on GaAs used heat cleaning temperatures around 400°C to avoid an increase in surface roughness [Bae et al., Appl. Phys. Lett. 112, 154101 (2018)]. Furthermore, high-temperature heat cleaning around 580°C, which results in a relatively contamination-free surface, could be one possible way to improve quantum efficiency. However, one should be cautious about surface roughness degradation during high-temperature heat cleaning. In this paper, we report results of surface roughness measurements on native, heat cleaned, and (Cs,O) activated GaAs photocathodes under vacuum. The results, measured by ultrahigh vacuum scanning tunneling microscopy, show that the surface roughness improves as the heat cleaning temperature is increased, by at least a factor of three for 580°C heat cleaning, compared to the native sample. Activation with (Cs,O) is shown to increase surface roughness by a factor of four compared to a 580°C heat cleaned sample. This confirms that high-temperature heat cleaning can be useful for depositing good quality robust activation layers on GaAs. Additionally, we also report chemical analysis for each step of preparation for p-doped GaAs photocathodes using X-ray photoelectron spectroscopy (XPS), angle-resolved XPS, and ultraviolet photoelectron spectroscopy. Our results indicate that the (Cs,O) activation layer forms a sandwich structure consists of Cs and oxygen. We found no formation of any specific compound such as Cs 2 O or Cs 11 O 3 .

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Atomic force microscopy: Emerging illuminated and operando techniques for solar fuel research

Integrated photoelectrochemical devices rely on the synergy between components to efficiently generate sustainable fuels from sunlight. The micro- and/or nanoscale characteristics of the components and their interfaces often control critical processes of the device, such as charge-carrier generation, electron and ion transport, surface potentials, and electrocatalysis. Understanding the spatial properties and structure–property relationships of these components can provide insight into designing scalable and efficient solar fuel components and systems. These processes can be probed ex situ or in situ with nanometer-scale spatial resolution using emerging scanning-probe techniques based on atomic force microscopy (AFM). In this Perspective, we summarize recent developments of AFM-based techniques relevant to solar fuel research. We review recent progress in AFM for (1) steady-state and dynamic light-induced surface photovoltage measurements; (2) nanoelectrical conductive measurements to resolve charge-carrier heterogeneity and junction energetics; (3) operando investigations of morphological changes, as well as surface electrochemical potentials, currents, and photovoltages in liquids. Opportunities for research covered here include: (1) control of ambient conditions for performing AFM measurements; (2) in situ visualization of corrosion and morphological evolution of electrodes; (3) operando AFM techniques to allow nanoscale mapping of local catalytic activities and photo-induced currents and potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Tilted fluctuation electron microscopy

Fluctuation electron microscopy (FEM) is a scanning nanodiffraction-based method that offers a unique approach to characterizing nanometer-scale medium-range order (MRO) in disordered materials. In addition to determining the degree of MRO, careful analysis of scanning nanodiffraction data can also be used to determine strain in thin film amorphous samples. We applied FEM to characterize the strain and MRO of magnetron sputtered amorphous tantalum (a-Ta) thin films over a range of tilt angles from 0° to 45° in order to measure any deviations between the in-plane and out-of-plane strain and MRO. We validate our approach using electron diffraction simulations of FEM experiments for a-Ta. We measure anisotropic strain in the simulated a-Ta diffraction patterns and find that the experimental a-Ta is isotropically strained within the accuracy of our method. Our approach provides a workflow for acquiring tilted scanning nanodiffraction data, determining the relative strain and ordering as a function of in- A nd out-of-plane directions, and removing any artifacts induced in FEM data due to strain. We also describe some limitations of the tilted FEM method when applied to thin films with very low strains.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Femtosecond photoemission electron microscopy of surface plasmon polariton beam steering via nanohole arrays

Directional control over surface plasmon polariton (SPP) waves is a prerequisite for the development of miniaturized optical circuitry. Here, the efficacy of single and dual component SPP steering elements is explored through photoemission electron microscopy. Our imaging scheme relies on two-color photoemission and counter-propagating SPP generation, which collectively allow SPPs to be visualized in real space. Wave-vector difference mixing between two-dimensional arrays of nanoholes and photon momenta enables SPP steering with directionality governed by the array lattice constant. The magnitude of grating momentum required to achieve counter-propagation guarantees efficient steering over the full range of the negative half-space. In our dual component configuration, separate SPP generation and Bragg diffraction based steering optics are employed. We find that array Bragg planes principally influence the SPP angles through the array band structure, which allows us to visualize both positive and negative refractory waves.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗