In Situ Atomic-Scale Observation of Dislocation Climb and Grain Boundary Transformation in Nanostructured Metal

We report atomic-scale observations of grain boundary (GB) dislocation climb in nanostructured Au during in situ straining at room temperature. Climb of a dislocation occurs by stress-induced reconstruction of two atomic columns at the edge of an extra half atomic plane in the dislocation core. Different from the conventional belief of dislocation climb by destruction or construction of a single atomic column at the dislocation core, the new atomic route is demonstrated to be energetically favorable by Monte Carlo simulations. Our in situ observations also reveal GB transformation through dislocation climb, which suggests a means of controlling microstructures and properties of nanostructured metals.

Atomic-scale unveiling of multiphase evolution during hydrated Zn-ion insertion in vanadium oxide

An initial crystalline phase can transform into another phases as cations are electrochemically inserted into its lattice. Precise identification of phase evolution at an atomic level during transformation is thus the very first step to comprehensively understand the cation insertion behavior and subsequently achieve much higher storage capacity in rechargeable cells, although it is sometimes challenging. By intensively using atomic-column-resolved scanning transmission electron microscopy, we directly visualize the simultaneous intercalation of both H2O and Zn during discharge of Zn ions into a V2O5 cathode with an aqueous electrolyte. In particular, when further Zn insertion proceeds, multiple intermediate phases, which are not identified by a macroscopic powder diffraction method, are clearly imaged at an atomic scale, showing structurally topotactic correlation between the phases. The findings in this work suggest that smooth multiphase evolution with a low transition barrier is significantly related to the high capacity of oxide cathodes for aqueous rechargeable cells, where the crystal structure of cathode materials after discharge differs from the initial crystalline state in general.

Exploring (1$$\overline{1}$$2)-related ordered structure in oxidation-synthesized α-Fe2O3 nanowhiskers

Hematite (α-Fe2O3) nanowhiskers (NWs) synthesized via oxidation of iron-based substrates are a promising photoanode material for photoelectrochemical water splitting. Such synthesized α-Fe2O3 NWs have been found to contain ordered axial structures. Herein, we reveal that the known (1$$\overline{1}$$ 1 ¯ 2)-related ordered structure actually exists in bicrystalline α-Fe2O3 NWs instead of single-crystalline α-Fe2O3 NWs and that it is associated with another known (3$$\overline{3}$$ 3 ¯ 0)-related ordered structure. Through a spherical aberration (CS)-corrected high-resolution transmission electron microscopy (HR-TEM) investigation, the microstructural characteristic of the (1$$\overline{1}$$ 1 ¯ 2)-related ordered structure is verified to be periodic atomic column displacements serving as tensile strain accommodation. The HR-TEM observation are also supported by a monochromated O K-edge EELS analysis, which indicates that α-Fe2O3 NWs hosting the (1$$\overline{1}$$ 1 ¯ 2)-related ordered structure are indeed associated with lattice expansion. In sum, our microstructural study elucidates the root cause of the long-asserted relationship between the (1$$\overline{1}$$ 1 ¯ 2)-related ordered structure and oxygen vacancy ordering.

Optimizing Non-Rigid Registration for Scanning Transmission Electron Microscopy Image Series

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using non-rigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 µs/px dwell time. Within the NRR algorithm, the “smoothness factor” that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general the precision of NRR images is robust over a wide range of parameters.

Approaches to Exploring Spatio-Temporal Surface Dynamics in Nanoparticles with In Situ Transmission Electron Microscopy

Many nanoparticles in fields such as heterogeneous catalysis undergo surface structural fluctuations during chemical reactions, which may control functionality. These dynamic structural changes may be ideally investigated with time-resolved in situ electron microscopy. We have explored approaches for extracting quantitative information from large time-resolved image data sets with a low signal to noise recorded with a direct electron detector on an aberration-corrected transmission electron microscope. We focus on quantitatively characterizing beam-induced dynamic structural rearrangements taking place on the surface of CeO2 (ceria). A 2D Gaussian fitting procedure is employed to determine the position and occupancy of each atomic column in the nanoparticle with a temporal resolution of 2.5 ms and a spatial precision of 0.25 Å. Local rapid lattice expansions/contractions and atomic migration were revealed to occur on the (100) surface, whereas (111) surfaces were relatively stable throughout the experiment. The application of this methodology to other materials will provide new insights into the behavior of nanoparticle surface reconstructions that were previously inaccessible using other methods, which will have important consequences for the understanding of dynamic structure–property relationships.

Building Ferroelectric from the Bottom Up: The Machine Learning Analysis of the Atomic-Scale Ferroelectric Distortions

<div>Recent advances in scanning transmission electron microscopy (STEM) have enabled direct visualization of the atomic structure of ferroic materials, enabling the determination of atomic column positions with ~pm precision. This, in turn, enabled direct mapping of ferroelectric and ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up approach, where the atomic coordinates derived from the STEM image are used to explore the extant atomic displacement patterns in the material and build the collection of the building blocks for the distorted lattice. This approach is illustrated for the La-doped BiFeO₃ system.</div><div>The full analysis procedure is available as an interactive paper in a form of a Google Colab (Jupyter) notebook where a classical paper organization is augmented with code cells that appear hidden by default (when viewed in Google Colab). This should allow a reader to retrace the analysis and, more importantly, it enables the readers to use the same codes for their data. The same paper is also available in a standard pdf format (without code).<br></div>

Building Ferroelectric from the Bottom Up: The Machine Learning Analysis of the Atomic-Scale Ferroelectric Distortions

<div>Recent advances in scanning transmission electron microscopy (STEM) have enabled direct visualization of the atomic structure of ferroic materials, enabling the determination of atomic column positions with ~pm precision. This, in turn, enabled direct mapping of ferroelectric and ferroelastic order parameter fields via the top-down approach, where the atomic coordinates are directly mapped on the mesoscopic order parameters. Here, we explore the alternative bottom-up approach, where the atomic coordinates derived from the STEM image are used to explore the extant atomic displacement patterns in the material and build the collection of the building blocks for the distorted lattice. This approach is illustrated for the La-doped BiFeO₃ system.</div><div>The full analysis procedure is available as an interactive paper in a form of a Google Colab (Jupyter) notebook where a classical paper organization is augmented with code cells that appear hidden by default (when viewed in Google Colab). This should allow a reader to retrace the analysis and, more importantly, it enables the readers to use the same codes for their data. The same paper is also available in a standard pdf format (without code).<br></div>