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Ophus, Colin

Publications and source records attributed to Ophus, Colin.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The 4D Camera: Very High Speed Electron Counting for 4D-STEM

A vast array of new experimental modalities have been enabled in the past several years through the development of pixelated detectors synchronized to probe scanning electronics. Such camera systems can then acquire the rich information present in the central portion of the convergent beam electron diffraction pattern as a function of probe position (4D-STEM). Here, we present the development, installation, and characterization of the 4D Camera, a CMOS Active Pixel Sensor that consists of a 576 x 576 array of 10 μm pixels of a design related to the original TEAM detector and an outer HAADF detector with 16 concentric quadrant diodes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
DOE OSTI2019

Formation of two-dimensional transition metal oxide nanosheets with nanoparticles as intermediates

According to the article, two-dimensional (2D) materials have attracted significant interest because of their large surface-to-volume ratios and electron confinement. Compared to common 2D materials such as graphene or metal hydroxides, with their intrinsic layered atomic structures, the formation mechanisms of 2D metal oxides with a rocksalt structure are not well understood. Here, we report the formation process for 2D cobalt oxide and cobalt nickel oxide nanosheets, after analysis by in situ liquid-phase transmission electron microscopy. Our observations reveal that three-dimensional (3D) nanoparticles are initially formed from the molecular precursor solution and then transform into 2D nanosheets. Ab initio calculations show that a small nanocrystal is dominated by positive edge energy, but when it grows to a certain size, the negative surface energy becomes dominant, driving the transformation of the 3D nanocrystal into a 2D structure. Uncovering these growth pathways, including the 3D-to-2D transition, provides opportunities for future material design and synthesis in solution.

Chemistry↗
DOE OSTI2019

Observing crystal nucleation in four dimensions using atomic electron tomography

Nucleation plays a critical role in many physical and biological phenomena that range from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments, especially in the early stage when several atoms or molecules start to form a new phase from a parent phase. A number of experimental and computational methods have been used to investigate nucleation processes, but experimental determination of the three-dimensional atomic structure and the dynamics of early stage nuclei has been unachievable. Here we use atomic electron tomography to study early stage nucleation in four dimensions (4D: that is, including time) at atomic resolution. Using FePt nanoparticles as a model system, we find that early stage nuclei are irregularly shaped, each has a core of one to a few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations are corroborated by molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid–solid phase transitions of Pt. Our experimental and molecular dynamics results differ from classical nucleation theory, indicating that a theory beyond this is needed to describe early stage nucleation at the atomic scale. Looking forward, we anticipate that the reported approach will open the door to the study of many fundamental problems in materials science, nanoscience, condensed matter physics and chemistry, such as phase transition, atomic diffusion, grain boundary dynamics, interface motion, defect dynamics and surface reconstruction with 4D atomic resolution.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Dynamic deformability of individual PbSe nanocrystals during superlattice phase transitions

The behavior of individual nanocrystals during superlattice phase transitions can profoundly affect the structural perfection and electronic properties of the resulting superlattices. However, details of nanocrystal morphological changes during superlattice phase transitions are largely unknown due to the lack of direct observation. Here, we report the dynamic deformability of PbSe semiconductor nanocrystals during superlattice phase transitions that are driven by ligand displacement. Real-time high-resolution imaging with liquid-phase transmission electron microscopy reveals that following ligand removal, the individual PbSe nanocrystals experience drastic directional shape deformation when the spacing between nanocrystals reaches 2 to 4 nm. The deformation can be completely recovered when two nanocrystals move apart or it can be retained when they attach. The large deformation, which is responsible for the structural defects in the epitaxially fused nanocrystal superlattice, may arise from internanocrystal dipole–dipole interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Direct measurement of nanostructural change during in situ deformation of a bulk metallic glass

Abstract To date, there has not yet been a direct observation of the initiation and propagation of individual defects in metallic glasses during deformation at the nanoscale. Here, we show through a combination of in situ nanobeam electron diffraction and large-scale molecular dynamics simulations that we can directly observe changes to the local short to medium range atomic ordering during the formation of a shear band. We observe experimentally a spatially resolved reduction of order prior to shear banding due to increased strain. We compare this to molecular dynamics simulations, in which a similar reduction in local order is seen, and caused by shear transformation zone activation, providing direct experimental evidence for this proposed nucleation mechanism for shear bands in amorphous solids. Our observation serves as a link between the atomistic molecular dynamics simulation and the bulk mechanical properties, providing insight into how one could increase ductility in glassy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Diffraction imaging of nanocrystalline structures in organic semiconductor molecular thin films

The properties of organic solids depend on their structure and morphology, yet direct imaging using conventional electron microscopy methods is hampered by the complex internal structure of these materials and their sensitivity to electron beams. In this work, we manage to observe the nanocrystalline structure of two organic molecular thin-film systems using transmission electron microscopy by employing a scanning nanodiffraction method that allows for full access to reciprocal space over the size of a spatially localized probe (~2 nm). The morphologies revealed by this technique vary from grains with pronounced segmentation of the structure—characterized by sharp grain boundaries and overlapping domains—to liquid-crystal structures with crystalline orientations varying smoothly over all possible rotations that contain disclinations representing singularities in the director field. Lastly, the results show how structure–property relationships can be visualized in organic systems using techniques previously only available for hard materials such as metals and ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A New Detector – the 4D Camera – Reaches a New Frontier in Speed

An electron microscope at Berkeley Lab’s Molecular Foundry has been upgraded with a superfast detector, the 4D Camera. The detector can capture more images at a faster rate, revealing atomic-scale details across much larger areas than was possible before.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Atomic electrostatic maps of 1D channels in 2D semiconductors using 4D scanning transmission electron microscopy

Defects in materials give rise to fluctuations in electrostatic fields that reflect the local charge density, but imaging this with single atom sensitivity is challenging. However, if possible, this provides information about the energetics of adatom binding, localized conduction channels, molecular functionality and their relationship to individual bonds. Here, ultrastable electron-optics are combined with a high-speed 2D electron detector to map electrostatic fields around individual atoms in 2D monolayers using 4D scanning transmission electron microscopy. Simultaneous imaging of the electric field, phase, annular dark field and the total charge in 2D MoS 2 and WS 2 is demonstrated for pristine areas and regions with 1D wires. The in-gap states in sulphur line vacancies cause 1D electron-rich channels that are mapped experimentally and confirmed using density functional theory calculations. We show how electrostatic fields are sensitive in defective areas to changes of atomic bonding and structural determination beyond conventional imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hidden structural and chemical order controls lithium transport in cation-disordered oxides for rechargeable batteries

Structure plays a vital role in determining materials properties. In lithium ion cathode materials, the crystal structure defines the dimensionality and connectivity of interstitial sites, thus determining lithium ion diffusion kinetics. In most conventional cathode materials that are well-ordered, the average structure as seen in diffraction dictates the lithium ion diffusion pathways. Here, we show that this is not the case in a class of recently discovered high-capacity lithium-excess rocksalts. An average structure picture is no longer satisfactory to understand the performance of such disordered materials. Cation short-range order, hidden in diffraction, is not only ubiquitous in these long-range disordered materials, but fully controls the local and macroscopic environments for lithium ion transport. Our discovery identifies a crucial property that has previously been overlooked and provides guidelines for designing and engineering cation-disordered cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Nanoscale mosaicity revealed in peptide microcrystals by scanning electron nanodiffraction

Abstract Changes in lattice structure across sub-regions of protein crystals are challenging to assess when relying on whole crystal measurements. Because of this difficulty, macromolecular structure determination from protein micro and nanocrystals requires assumptions of bulk crystallinity and domain block substructure. Here we map lattice structure across micron size areas of cryogenically preserved three−dimensional peptide crystals using a nano-focused electron beam. This approach produces diffraction from as few as 1500 molecules in a crystal, is sensitive to crystal thickness and three−dimensional lattice orientation. Real-space maps reconstructed from unsupervised classification of diffraction patterns across a crystal reveal regions of crystal order/disorder and three−dimensional lattice tilts on the sub-100nm scale. The nanoscale lattice reorientation observed in the micron-sized peptide crystal lattices studied here provides a direct view of their plasticity. Knowledge of these features facilitates an improved understanding of peptide assemblies that could aid in the determination of structures from nano- and microcrystals by single or serial crystal electron diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Interpretable and Efficient Interferometric Contrast in Scanning Transmission Electron Microscopy with a Diffraction-Grating Beam Splitter

Efficient imaging of biomolecules, 2D materials and electromagnetic fields depends on retrieval of the phase of transmitted electrons. We demonstrate a method to measure phase in a scanning transmission electron microscope using a nanofabricated diffraction grating to produce multiple probe beams. The measured phase is more interpretable than phase-contrast scanning transmission electron microscopy techniques without an off-axis reference wave, and the resolution could surpass that of off-axis electron holography. Here, we apply the technique to image nanoparticles, carbon sub- strates and electric fields. The contrast observed in experiments agrees well with contrast predicted in simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Automated Labeling of Electron Microscopy Images Using Deep Learning

© 2018 IEEE. Searching for scientific data requires metadata providing a relevant context. Today, generating metadata is a time and labor intensive manual process that is often neglected, and important datasets are not accessible through search. We investigate the use of machine learning to generalize metadata from a subset of labeled data, thus increasing the availability of meaningful metadata for search. Specifically, we consider electron microscopy images collected at the National Center for Electron Microscopy (NCEM) at the Lawrence Berkeley National Laboratory (LBNL) and use deep learning to discern characteristics from a small subset of labeled images and transfer labels to the entire image corpus. Relatively small training set sizes and a minimum resolution of 512\times 512 pixels required by the application domain pose unique challenges. We overcome these challenges by using a simple yet powerful convolutional network architecture that limits the number of free parameters to lower the required amount of computational power and reduce the risk of overfitting. We achieve a classification accuracy of approximately 80% in discerning between images recorded in two operating modes of the electron microscope-transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM). We use transfer learning-i.e., re-using the pre-trained convolution layers from the TEM vs. STEM classification problem-to generalize labels and achieve an accuracy of approximately 70 % despite current experiments being limited to small training data sets. We present these predictions as suggestions to domain scientists through a web-based tool to accelerate the labeling process with the goal of further validating our approach and improving the accuracy of automatically created labels.

Weber, Gunther H↗
DOE OSTI2018

ScienceSearch: Enabling Search through Automatic Metadata Generation

© 2018 IEEE. Scientific facilities are increasingly generating and handling large amounts of data from experiments and simulations. Next-generation scientific discoveries rely on insights derived from data, especially across domain boundaries. Search capabilities are critical to enable scientists to discover datasets of interest. However, scientific datasets often lack the signals or metadata required for effective searches. Thus, we need formalized methods and systems to automatically annotate scientific datasets from the data and its surrounding context. Additionally, a search infrastructure needs to account for the scale and rate of application data volumes. In this paper, we present ScienceSearch, a system infrastructure that uses machine learning techniques to capture and learn the knowledge, context, and surrounding artifacts from data to generate metadata and enable search. Our current implementation is focused on a dataset from the National Center for Electron Microscopy (NCEM), an electron microscopy facility at Lawrence Berkeley National Laboratory sponsored by the Department of Energy which supports hundreds of users and stores millions of micrographs. In this paper, we describe a) our search infrastructure and model, b) methods for generating metadata using machine learning techniques, and c) optimizations to improve search latency, and deployment on an HPC system. We demonstrate that ScienceSearch is capable of producing valid metadata for NCEM's dataset and providing low-latency good quality search results over a scientific dataset.

Rodrigo, Gonzalo P.↗
DOE OSTI2018

Probing Light Atoms at Subnanometer Resolution: Realization of Scanning Transmission Electron Microscope Holography

Atomic resolution imaging of light elements in electron-transparent materials has long been a challenge. Biomolecular materials, for example, are rapidly altered by incident electrons. We demonstrate a scanning transmission electron microscopy (STEM) technique, called STEM holography, capable of efficient structural analysis of beam-sensitive nanomaterials. STEM holography measures the absolute phase and amplitude of electrons passed through a specimen via interference with a vacuum reference wave. We use an amplitude-dividing nanofabricated grating to prepare multiple beams focused at the sample. We configure the postspecimen microscope imaging system to overlap the beams, forming an interference pattern. We record and analyze the pattern at each 2D-raster-scan-position, reconstructing the complex object wave. As a demonstration, we image gold nanoparticles on an amorphous carbon substrate at 2.4 Å resolution. In conclusion STEM holography offers higher contrast of the carbon while maintaining gold atomic lattice resolution compared to high angle annular dark field STEM.

4D-STEM↗
DOE OSTI2018

Imaging Unstained Synthetic Polymer Crystals and Defects on Atomic Length Scales Using Cryogenic Electron Microscopy

Properties of soft crystalline materials such as synthetic polymers are governed by locations of constituent atoms. Determining atomic-scale structures in these materials is difficult because they degrade rapidly when studied by electron microscopy, and techniques such as x-ray scattering average over volumes much larger than coherent blocks of the unit cells. We obtained cryo-electron microscopy images of self-assembled nanosheets of a peptoid polymer, made by solid-phase synthesis, in which we see a variety of crystalline motifs. A combination of crystallographic and single particle methods, developed for cryo-electron microscopy of biological macromolecules, was used to obtain high resolution images of the crystals. Individual crystals contain grains that are mirror images of each other with concomitant grain boundaries. We have used molecular dynamic simulations to build an atomic model of the crystal structure to facilitate the interpretation of electron micrographs. Direct visualization of crystalline grains and grain boundaries on atomic length scales represents a new level of information for the polymer field.

36 MATERIALS SCIENCE↗