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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 793 records · Page 44
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 OSTI2016

Charge collection kinetics on ferroelectric polymer surface using charge gradient microscopy

Here, a charge gradient microscopy (CGM) probe was used to collect surface screening charges on poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] thin films. These charges are naturally formed on unscreened ferroelectric domains in ambient condition. The CGM data were used to map the local electric current originating from the collected surface charges on the poled ferroelectric domains in the P(VDF-TrFE) thin films. Both the direction and amount of the collected current were controlled by changing the polarity and area of the poled domains. The endurance of charge collection by rubbing the CGM tip on the polymer film was limited to 20 scan cycles, after which the current reduced to almost zero. This degradation was attributed to the increase of the chemical bonding strength between the external screening charges and the polarization charges. Once this degradation mechanism is mitigated, the CGM technique can be applied to efficient energy harvesting devices using polymer ferroelectrics.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Synchrotron-based X-ray fluorescence microscopy enables multiscale spatial visualization of ions involved in fungal lignocellulose deconstruction

Ions play an important role in the growth and development of filamentous fungi, particularly in the fungal decay process of lignocellulose materials. The role of ions in wood degradation, and more broadly fungal metabolism, have implications for diverse research disciplines ranging from plant pathology and forest ecology, to wood protection. Despite the importance of ions in both enzymatic and non-enzymatic fungal decay mechanisms, the spatial distribution of ions in wood and fungal hyphae during decay is not known. Here we employ synchrotron based X-ray fluorescence microscopy (XFM) to map physiologically relevant ions, such as K, Ca, Mn, Fe, and Zn, in wood being decayed by the model brown rot fungus Serpula lacrymans . Two-dimensional XFM maps were obtained to study the ion spatial distributions from mm to submicron length scales in wood and hyphae. Three-dimensional ion volume reconstructions with submicron spatial resolution were also acquired of wood cell walls and fungal hyphae, and an estimation of oxalate concentration at the microscale was made. Results show that the fungus actively transports some ions, such as Fe, into the wood and controls the distribution of ions at both the bulk wood and cellular length scales. Within the fungal hyphae, ion volume reconstructions show inhomogeneous ion distributions at the micron length scale and this localization may be indicative of both physiological status and requirements or in some cases, potentially sites associated with the initiation of metal-catalyzed wood degradation. Finally, these measurements illustrate how synchrotron based XFM is uniquely qualified for probing the role of ions in the growth and metabolic processes of filamentous fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Traceable atomic force microscopy of high-quality solvent-free crystals of [6,6]-phenyl-C{sub 61}-butyric acid methyl ester

We report high-resolution, traceable atomic force microscopy measurements of high-quality, solvent-free single crystals of [6,6]-phenyl-C61-butyric acid methyl ester (PCBM). These were grown by drop-casting PCBM solutions onto the spectrosil substrates and by removing the residual solvent in a vacuum. A home-built atomic force microscope featuring a plane mirror differential optical interferometer, fiber-fed from a frequency-stabilized laser (emitting at 632.8 nm), was used to measure the crystals' height. The optical interferometer together with the stabilized laser provides traceability (via the laser wavelength) of the vertical measurements made with the atomic force microscope. We find that the crystals can conform to the surface topography, thanks to their height being significantly smaller compared to their lateral dimensions (namely, heights between about 50 nm and 140 nm, for the crystals analysed, vs. several tens of microns lateral dimensions). The vast majority of the crystals are flat, but an isolated, non-flat crystal provides insights into the growth mechanism and allows identification of “molecular terraces” whose height corresponds to one of the lattice constants of the single PCBM crystal (1.4 nm) as measured with X-ray diffraction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Vectorial mapping of noncollinear antiferromagnetic structure of semiconducting FeSe surface with spin-polarized scanning tunneling microscopy

Antiferromagnetic semiconductors gain increasing interest due to their possible application in spintronics. Using spin polarized scanning tunneling microscopy operating in a vector field, we mapped the noncollinear antiferromagnetic spin structure of a semiconducting hexagonal FeSe surface on the atomic scale. The surface possesses an in-plane compensated Néel structure which is further confirmed by first-principles calculations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Anti-phase boundaries at the SrTiO{sub 3}/Si(001) interface studied using aberration-corrected scanning transmission electron microscopy

In this work, the atomic structure of anti-phase boundary defects at the SrTiO{sub 3}/Si (001) interface is investigated by aberration-corrected scanning transmission electron microscopy. Atomic-resolution images reveal an abrupt SrTiO{sub 3}/Si interface with no intermediate oxide layer. Both single and double Si atomic columns (“dumbbells”) from different terraces of the Si(001) surface are visible at the interface. Anti-phase boundaries (APB) consisting of two adjacent TiO{sub 2} planes in the SrTiO{sub 3} (STO) film resulting either from Si surface steps or from the merging of crystalline domains from different surface nucleation sites are identified. These APBs occur on either {110} or {010} planes and both types have displacement vectors of a{sub STO}/2〈110〉.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗