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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probing charge density in materials with atomic resolution in real space

The charge distribution in materials at the nanoscale can often explain the origin of macroscopic properties such as localized conductivity or the plasmonic response and illuminate more fundamental changes in the microscopic structure such as changes in chemical bonding characteristics. Previously, direct visualization of the charge density with high spatial resolution was often a missing link in the formation of structure–property relationships, especially in heterogeneous materials systems. Furthermore, recent advancements in microscopy technology have enabled researchers to visualize the charge distribution in materials down to subatomic length scales. In this Technical Review, we discuss the developments in high-resolution real-space charge distribution imaging using diffraction techniques and electron microscopy, with a focus on the recent advancement of four-dimensional scanning transmission electron microscopy, electron holography, and applications to materials interfaces.

Electronic properties and materials↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2024

Unravelling Disorder in Aperiodic Crystals – Diffuse Scattering and Atomic Resolution Holography

The atomic–scale disorder of aperiodic crystals, and quasicrystals in particular, is inherently difficult to explore by experimental methods due to their complex atomic arrangements. Two advanced characterization techniques, a revived and an emerging one, offer direct experimental access even to such complex atomic structures: Diffuse Scattering and Atomic Resolution Holography. Finally, in this overview, we introduce their specific application to aperiodic crystals and discuss their merits and difficulties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantitative approaches for multiscale structural analysis with atomic resolution electron microscopy

Atomic-resolution imaging with scanning transmission electron microscopy is a powerful tool for characterizing the nanoscale structure of materials, in particular features such as defects, local strains, and symmetry-breaking distortions. In addition to advanced instrumentation, the effectiveness of the technique depends on computational image analysis to extract meaningful features from complex datasets recorded in experiments, which can be complicated by the presence of noise and artifacts, small or overlapping features, and the need to scale analysis over large representative areas. Here, we present image analysis approaches which synergize real and reciprocal space information to efficiently and reliably obtain meaningful structural information with picometer scale precision across hundreds of nanometers of material from atomic-resolution electron microscope images. Damping superstructure peaks in reciprocal space allows symmetry-breaking structural distortions to be disentangled from other sources of inhomogeneity and measured with high precision. Real-space fitting of the wavelike signals resulting from Fourier filtering enables absolute quantification of lattice parameter variations and strain, as well as the uncertainty associated with these measurements. Implementations of these algorithms are made available as an open source python package.

36 MATERIALS SCIENCE↗
DOE OSTI2024

2D Polyhedral Template Matching for Atomic Resolution Microscopy

SAND2024-13879O The 2D Polyhedral Template Matching for Atomic Resolution Microscopy is a suite of functions for analyzing atomic resolution electron microscopy images using the 2D polyhedral template matching (2D-PTM) method. This software analyzes atomic resolution microscopy data from electron microscopic imaging or scanning probe microscopies. The primary application is for identifying different crystal phases, crystal orientations, and defect structures obtained in such atomic resolution images. Written in MATLAB and starting from an atomic resolution image, the code identifies the positions of atomic intensity peaks. It then matches predefined structural templates to the local atomic environments. Outputs include the local structural identification, the template scaling factor and rotation angle, root-mean-squared deviation (RMSD), and centrosymmetry parameter.

Medlin, Douglas↗
DOE OSTI2020

Atomic‐Resolution Imaging of Halide Perovskites Using Electron Microscopy

Abstract Atomic‐resolution imaging of halide perovskites (HPs) using transmission electron microscopy (TEM) is challenging because of the sensitivity of their structures to the electron beam. In this article, recent achievements in this area are reviewed, covering both all‐inorganic and organic–inorganic hybrid HPs, with an emphasis on the specific imaging conditions that have proven to be effective in avoiding electron beam‐induced structural damage. The discussion focusses on the total electron dose that HPs can bear before being damaged and the effects of different imaging modes, accelerating voltages, and temperatures. The crucial role of a direct‐detection electron‐counting camera in reducing the required electron dose is outlined, which is indispensable for imaging extremely sensitive organic–inorganic hybrid perovskites. In addition to reviewing published works, the results of initial attempts to perform atomic‐resolution elemental mapping for an all‐inorganic HP and image a hybrid HP using scanning TEM are introduced. The preparation of a TEM specimen from macroscopic crystals or devices of HPs, which is very important for practical applications but has not yet received attention, is also discussed. This article aims to provide guidance on the acquisition of atomic‐resolution TEM images of HPs and inspire the development of more imaging technologies for sensitive materials.

Song, Kepeng↗
DOE OSTI2024

Atomic resolution scanning transmission electron microscopy at liquid helium temperatures for quantum materials

Fundamental quantum phenomena in condensed matter, ranging from correlated electron systems to quantum information processors, manifest their emergent characteristics and behaviors predominantly at low temperatures. This necessitates the use of liquid helium (LHe) cooling for experimental observation. Atomic resolution scanning transmission electron microscopy combined with LHe cooling (cryo-STEM) provides a powerful characterization technique to probe local atomic structural modulations and their coupling with charge, spin and orbital degrees-of-freedom in quantum materials. However, achieving atomic resolution in cryo-STEM is exceptionally challenging, primarily due to sample drifts arising from temperature changes and noises associated with LHe bubbling, turbulent gas flow, etc. In this work, we demonstrate atomic resolution cryo-STEM imaging at LHe temperatures using a commercial side-entry LHe cooling holder. Firstly, we examine STEM imaging performance as a function of He gas flow rate, identifying two primary noise sources: He-gas pulsing and He-gas bubbling. Secondly, we propose two strategies to achieve low noise conditions for atomic resolution STEM imaging: either by temporarily suppressing He gas flow rate using the needle valve or by acquiring images during the natural warming process. Lastly, we show the applications of image acquisition methods and image processing techniques in investigating structural phase transitions in Cr 2 Ge 2 Te 6 , CuIr 2 S 4 , and CrCl 3 . In conclusion, our findings represent an advance in the field of atomic resolution electron microscopy imaging for quantum materials and devices at LHe temperatures, which can be applied to other commercial side-entry LHe cooling TEM holders.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Disentangling Coexisting Structural Order Through Phase Lock-In Analysis of Atomic-Resolution STEM Data

As a real-space technique, atomic-resolution STEM imaging contains both amplitude and geometric phase information about structural order in materials, with the latter encoding important information about local variations and heterogeneities present in crystalline lattices. Such phase information can be extracted using geometric phase analysis (GPA), a method which has generally focused on spatially mapping elastic strain. Here we demonstrate an alternative phase demodulation technique and its application to reveal complex structural phenomena in correlated quantum materials. As with other methods of image phase analysis, the phase lock-in approach can be implemented to extract detailed information about structural order and disorder, including dislocations and compound defects in crystals. Extending the application of this phase analysis to Fourier components that encode periodic modulations of the crystalline lattice, such as superlattice or secondary frequency peaks, we extract the behavior of multiple distinct order parameters within the same image, yielding insights into not only the crystalline heterogeneity but also subtle emergent order parameters such as antipolar displacements. When applied to atomic-resolution images spanning large (~0.5 × 0.5 μm 2 ) fields of view, this approach enables vivid visualizations of the spatial interplay between various structural orders in novel materials.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1993

Imaging Genetic Molecules At Atomic Resolution

Proposed method of imaging informational polymeric biological molecules at atomic resolution enables determination of sequences of component monomers about 10 to the 3rd power to 10 to the 4th power times as fast as conventional methods do. Accelerates research on genetic structures of animals and plants. Also contributes significantly to imaging processes like scanning electron microscopy (SEM), atomic-force microscopy (AFM), and scanning tunneling microscopy (STM) in cases in which necessary to locate or identify small specimens on relatively large backgrounds and subtract background images to obtain images of specimens in isolation. V-grooves on silicon wafer laid out in square pattern, intersections of which marked to identify coordinates. Specimen molecules held in grooves for reproducible positioning and scanning by AFM or STM.

Coles, L. Stephen↗
DOE OSTI2020

mpfit: a robust method for fitting atomic resolution images with multiple Gaussian peaks

The standard technique for sub-pixel estimation of atom positions from atomic resolution scanning transmission electron microscopy images relies on fitting intensity maxima or minima with a two-dimensional Gaussian function. While this is a widespread method of measurement, it can be error prone in images with non-zero aberrations, strong intensity differences between adjacent atoms or in situations where the neighboring atom positions approach the resolution limit of the microscope. Here we demonstrate mpfit, an atom finding algorithm that iteratively calculates a series of overlapping two-dimensional Gaussian functions to fit the experimental dataset and then subsequently uses a subset of the calculated Gaussian functions to perform sub-pixel refinement of atom positions. Based on both simulated and experimental datasets presented in this work, this approach gives lower errors when compared to the commonly used single Gaussian peak fitting approach and demonstrates increased robustness over a wider range of experimental conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Time-resolved atomic-resolution Brownian tomography of single nanocrystals reveals size-dependent dynamics

Atomic-resolution structure identification of nanocrystals by graphene liquid cell electron microscopy (GLC-EM) has revealed that small, solubilized platinum nanocrystals consist of an ordered crystalline core surrounded by mobile surface atoms, which dissociate during oxidative etching, resulting in distinct temporal structural states. Requirements imposed by the 3D reconstruction algorithm limit the number of structural states that can be resolved. We introduce a regularized 3D reconstruction algorithm that exploits the redundancy inherent in the experimental data, allowing us to improve the time resolution. Our developments provide a comprehensive molecular picture at unprecedented spatial and temporal resolution of the nonlinear, linear, and fluctuating dynamic phenomena that single nanocrystals undergo during the GLC-EM experiment. We determined atomic structures of 66 temporal structural states, extracted from 15 time trajectories of individual nanocrystals. Large (478 to 698 atoms) and small (<300 atoms) nanocrystals show etching that preserves a stable core, whereas mid-sized (351 to 571 atoms) nanocrystals present dynamics that change the coordination of the core.

Meana-Pañeda, Rubén↗
DOE OSTI2022

Atomic-resolution lithography with an on-chip scanning tunneling microscope

In this work, atomic-resolution lithography with a Microelectromechanical-System (MEMS) based Scanning Tunneling Microscope (STM) is demonstrated for the first time. The microscope consists of a commercial UltraHigh-Vacuum (UHV) STM whose regular tip is replaced with a 1-Degree-of-Freedom (1-DOF) MEMS nanopositioner. This results in a hybrid STM system where XY-plane motions are provided by the piezotube of the original system and Z-axis motion by the MEMS with a higher bandwidth. Sharp tips made of Pt or W are added to the MEMS devices with postfabrication techniques. With this hybrid system, STM-based lithography is demonstrated on an H-passivated Si (100)-2×1 sample under UHV condition. Results prove the capability of the hybrid STM system for atomic-scale lithography. This capability, paired with the small footprint of the MEMS device, makes this approach a candidate for building a high-throughput parallel STM lithography platform by incorporating an array of 1-DOF MEMS devices that perform lithography in parallel.

Alipour, Afshin (ORCID:0000000263252067)↗
DOE OSTI2020

Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO 3 collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Autonomous Molecular Structure Imaging with High-Resolution Atomic Force Microscopy for Molecular Mixture Discovery

Due to its single-molecule sensitivity, high-resolution atomic force microscopy (HR-AFM) has proved to be a valuable and uniquely advantageous tool to study complex molecular mixtures, which hold promise for developing clean energy and achieving environmental sustainability. However, significant challenges remain to achieve the full potential of the sophisticated and time-consuming experiments. Automation combined with machine learning (ML) and artificial intelligence (AI) is key to overcoming these challenges. Here we present Auto-HR-AFM, an AI tool to automatically collect HR-AFM images of petroleum-based mixtures. In this study, we trained an instance segmentation model to teach Auto-HR-AFM how to recognize features in HR-AFM images. Auto-HR-AFM then uses that information to optimize the imaging by adjusting the probe-molecule distance for each molecule in the run. Auto-HR-AFM is the initial tool that will lead to fully automated scanning probe microscopy (SPM) experiments, from start to finish. This automation will allow SPM to become a mainstream characterization technique for complex mixtures, an otherwise unattainable target.

36 MATERIALS SCIENCE↗