Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

In-situ heating-and-electron tomography for materials research: from 3D (in-situ 2D) to 4D (in-situ 3D)

In-situ observation has expanded the application of transmission electron microscopy (TEM) and has made a significant contribution to materials research and development for energy, biomedical, quantum, etc. Recent technological developments related to in-situ TEM have empowered the incorporation of three-dimensional observation, which was previously considered incompatible. In this review article, we take up heating as the most commonly used external stimulus for in-situ TEM observation and overview recent in-situ TEM studies. Then, we focus on the electron tomography (ET) and in-situ heating combined observation by introducing the authors' recent research as an example. Assuming that in-situ heating observation is expanded from two dimensions to three dimensions using a conventional TEM apparatus and a commercially available in-situ heating specimen holder, the following in-situ heating-and-ET observation procedure is proposed: (i) use a rapid heating-and-cooling function of a micro-electro-mechanical system holder; (ii) heat and cool the specimen intermittently and (iii) acquire a tilt-series dataset when the specimen heating is stopped. This procedure is not too technically challenging and can have a wide range of applications. Essential technical points for a successful 4D (space and time) observation will be discussed through reviewing the authors' example application.

Atomic-Scale Imaging of Clay Mineral Nanosheets and Their Supramolecular Complexes through Electron Microscopy: A Supramolecular Chemist's Perspective

Recent advancements in electron microscopy techniques have revolutionized the ability to directly visualize and understand the intricate world of supramolecular chemistry. This paper provides a concise overview of a study delving into the atomic-scale imaging of monolayer clay mineral nanosheets and their associated supramolecular complexes. The imaging is conducted by harnessing the power of aberration-corrected scanning transmission electron microscopy (STEM). Clay mineral nanosheets, with their anionic charge and ultrathin thickness (of 1 nm), serve as a stable Coulombic host material for cationic guest molecules through electrostatic interactions, facilitating exceptional stability and control during observation. By incorporation of heavy-metal atom markers coordinated within the target molecules, high-angle annular dark field STEM enables a clear visualization of these supramolecular complexes. This approach helps to overcome the limitations of graphene-based systems and expands the possibilities of atomic-scale imaging of nonperiodic molecular assemblies formed by weak supramolecular interactions. The fusion of electron microscopy techniques with the principles of supramolecular and material chemistry offers exciting opportunities for studying the structure, behavior, and properties of complex supramolecular systems. It sheds light on the intricate molecular architectures and design principles governing these systems. This study showcases the immense potential of electron microscopy in supramolecular chemistry and invites researchers from various disciplines to explore the transformative possibilities of atomic-scale imaging in the field.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy

Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well known, STEM's image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM's current temporal resolution is far below the conventional TEM's. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.