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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2020

A transmission electron microscopy study of dislocation propagation and filtering in highly mismatched GaSb/GaAs heteroepitaxy

Monolithic integration of lattice-mismatched semiconductor materials opens up access to a wide range of bandgaps and new device functionalities. However, it is inevitably accompanied by defect formation. A thorough analysis of how these defects propagate and interact with interfaces is critical to understanding their effects on device parameters. In this study, we present a comprehensive study of dislocation networks in the GaSb/GaAs heteroepitaxial system using transmission electron microscopy (TEM). Specifically, the sample analyzed is a GaSb film grown on GaAs using dislocation–reduction strategies such as interfacial misfit array formation and introduction of a dislocation filtering layer. Using various TEM techniques, it is shown that such an analysis can reveal important information on the dislocation behavior including filtering mechanism, types of dislocation reactions, and other interactions with interfaces. A novel method that enables plan-view imaging of deeply embedded interfaces using TEM and a demonstration of independent imaging of different dislocation types are also presented. While clearly effective in characterizing dislocation behavior in GaSb/GaAs, we believe that the methods outlined in this article can be extended to study other heteroepitaxial material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

DR-RINS: Digital real-time relative intensity noise suppressor for pump–probe spectroscopy and microscopy

Relative intensity noise (RIN) inherent in fiber lasers poses a serious obstacle to their use in pump–probe spectroscopy and imaging. RIN can be removed through an analog balanced detector, or, as we have previously shown, software adaptive noise cancellation (ANC) on digitized signals. One major drawback to software ANC is the added time required for digitizing and post-processing. In this article, we describe a design for ANC on a field-programmable gate array (FPGA), making use of high-level synthesis tools and fixed-point arithmetic to achieve real-time laser RIN suppression at 25 MHz sample rates. Unlike the software-ANC approach, the FPGA-ANC device can serve as a dedicated drop-in denoiser, placed between the detectors and a commercial lock-in amplifier. We demonstrate its application to transient absorption spectroscopy and microscopy, lowering the noise floor to ~17 dB above the shot noise limit. Furthermore, we demonstrate a dramatic improvement in data acquisition time from ~6 h to ~5 min in a real-time imaging scenario.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Magnetic resonance force microscopy with matching frequencies of cantilever and spin

We have studied theoretically magnetic resonance force microscopy with a high-frequency nanomechanical cantilever when the cantilever frequency matches the resonant frequency of a single electron spin. Our analysis shows that in this situation, there is a small probability that the cantilever will oscillate with a large frequency shift. In conclusion, this can open new experimental opportunities for increasing the sensitivity in the detection of a single electron spin or even a single nuclear spin by using a high-frequency cantilever.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Piezoelectric property of PZT nanofibers characterized by resonant piezo-force microscopy

Nano-piezoelectric materials have drawn tremendous research interest. However, characterization of their piezoelectric properties, especially measuring the piezoelectric strain coefficients, remains a challenge. Normally, researchers use an AFM-based method to directly measure nano-materials’ piezoelectric strain coefficients. But, the extremely small piezoelectric deformation, the influence from the parasitic electrostatic force, and the environmental noise make the measurement results questionable. In this paper, a resonant piezo-force microscopy method was used to accurately measure the piezoelectric deformation from 1D piezoelectric nanofibers. During the experiment, the AFM tip was brought into contact with the piezoelectric sample and set to work at close to its first resonant frequency. A lock-in amplifier was used to pick up the sample’s deformation signal at the testing frequency. By using this technique, the piezoelectric strain constant d 33 of the Lead Zirconate Titanate (PZT) nanofiber with a diameter of 76 nm was measured. The result showed that d 33 of this PZT nanofiber was around 387 pm/V. Meanwhile, by tracking the piezoelectric deformation phase image, domain structures inside PZT nanofibers were identified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High resolution atomic force microscopy with an active piezoelectric microcantilever

Active microcantilevers with on-chip sensing and actuation provide significant advantages in tapping mode Atomic Force Microscopy (AFM). Collocated transduction allows for effective manipulation of cantilever dynamics through feedback control, enabling higher scan rates. However, the adjacency of the sensing and actuation electrodes is known to result in a high level of feedthrough, leading to a low imaging resolution. Readout circuit noise further deteriorates the imaging precision. Here, we investigate the noise sources that affect AFM microcantilevers with collocated aluminum nitride (AlN) actuator–sensor pairs. We reported these cantilevers in earlier work and demonstrated that they display a very low level of feedthrough between the actuation and sensing electrodes. We present a high signal-to-noise ratio (SNR) sensing method that enables us to demonstrate high-resolution AFM on a calibration grating with nm-step silicon carbide (SiC) terraces. In conclusion, measuring the Lorentzian response of the cantilever’s Brownian motion with the on-chip active sensor at resonance enables us to calibrate the dynamic stiffness at the first fundamental resonance mode, without utilizing an optical sensor.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Deep-learning-aided extraction of optical constants in scanning near-field optical microscopy

Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here, we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. The network provides an initial estimate that is subsequently refined by a traditional fitting algorithm. In conclusion, we show that this method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗