Quantification and optimization of ADF-STEM image contrast for beam-sensitive materials

Atomic-Resolution STEM Image Denoising by Total Variation Regularization

Atomic-resolution electron microscopy imaging of solid state material is a powerful method for structural analysis. Scanning transmission electron microscopy (STEM) is one of the actively used techniques to directly observe atoms in materials. However, some materials are easily damaged by the electron beam irradiation, and only noisy images are available when we decrease the electron dose to avoid beam damages. Therefore, a denoising process is necessary for precise structural analysis in low-dose STEM. In this study, we propose total variation (TV) denoising algorithm to remove quantum noise in a STEM image. We defined an entropy of STEM image that corresponds to the image contrast to determine a hyperparameter and we found that there is a hyperparameter that maximize the entropy. We acquired atomic resolution STEM image of CaF2 viewed along the [001] direction, and executed TV denoising. The atomic columns of Ca and F are clearly visualized by the TV denoising, and atomic position of Ca and F are determined with the error of ± 1 pm and ± 4 pm, respectively.

Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Development of a Practicable Digital Pulse Read-Out for Dark-Field STEM

When characterizing beam-sensitive materials in the scanning transmission electron microscope (STEM), low-dose techniques are essential for the reliable observation of samples in their true state. A simple route to minimize both the total electron-dose and the dose-rate is to reduce the electron beam-current and/or raster the probe at higher speeds. At the limit of these settings, and with current detectors, the resulting images suffer from unacceptable artifacts, including signal-streaking, detector-afterglow, and poor signal-to-noise ratios (SNRs). In this article, we present an alternative approach to capture dark-field STEM images by pulse-counting individual electrons as they are scattered to the annular dark-field (ADF) detector. Digital images formed in this way are immune from analog artifacts of streaking or afterglow and allow clean, high-SNR images to be obtained even at low beam-currents. We present results from both a ThermoFisher FEI Titan G2 operated at 300 kV and a Nion UltraSTEM200 operated at 200 kV, and compare the images to conventional analog recordings. ADF data are compared with analog counterparts for each instrument, a digital detector-response scan is performed on the Titan, and the overall rastering efficiency is evaluated for various scanning parameters.

Unhindered Brownian Motion of Individual Nanoparticles in Liquid-Phase Scanning Transmission Electron Microscopy

Liquid-phase transmission electron microscopy (LPTEM) offers label-free imaging of nanoparticle (NP) processes in liquid with sub-nanometer spatial and millisecond temporal resolution. However, LPTEM studies have reported only on NPs moving orders of magnitude slower than expected from bulk aqueous liquid conditions, likely due to strong interactions with the LPTEM liquid-enclosing membranes. We demonstrate how scanning transmission electron microscope (STEM) imaging can be used to measure the motion of individual NPs and agglomerates, which are not hindered by such interactions. Only at low electron flux do we find that individual NPs exhibit Brownian motion consistent with optical control experiments and theoretical predictions for unhindered passive diffusive motion in bulk liquids. For increasing electron flux, we find increasingly faster than passive motion that still appears effectively Brownian. We discuss the possible origins of this beam-sample interaction. This establishes conditions for the use of STEM as a reliable tool for imaging nanoscale hydrodynamics in situ TEM.

Using HAADF-STEM for atomic-scale evaluation of incorporation of antibacterial Ag atoms in a β-tricalcium phosphate structure

Structural evaluation of ionic additions in calcium phosphates that enhance their performance is a long-lasting area of research in the field of biomedical materials. Ionic incorporation in β-tricalcium phosphate (β-TCP) structures is indispensable for obtaining desirable properties for specific functions and applications. Owing to its complex structure and beam-sensitive nature, determining the extent of ion incorporation and its corresponding location in the β-TCP structure is challenging. Further, very few experimental studies have been able to estimate the location of Ag atoms incorporated in a β-TCP structure while considering the associated changes in lattice parameters. Although the incorporation alters the lattice parameters, the alteration is not significant enough for estimating the location of the incorporated Ag atoms. Here, Ag incorporation in a β-TCP structure was evaluated on atomic scale using scanning transmission electron microscopy (STEM). To the best of our knowledge, this is the first report to unambiguously determine the location of the incorporated Ag atoms in the β-TCP structure by comparing z-contrast profiles of the Ag and Ca atoms by combining the state-of-art STEM observations and STEM image simulations. The Ag incorporation in the Ca(4) sites of β-TCP, as estimated by the Rietveld refinement, was in good agreement with the high-angle annular dark-field STEM observations and the simulations of the location of Ag atoms for [001] and [010] zone axes.

Liquid-Phase Electron Microscopy for Soft Matter Science and Biology

Innovations in liquid-phase electron microscopy (LP-EM) have made it possible to perform experiments at the optimized conditions needed to examine soft matter. The main obstacle is conducting experiments in such a way that electron beam radiation can be used to obtain answers for scientific questions without changing the structure and (bio)chemical processes in the sample due to the influence of the radiation. By overcoming these experimental difficulties at least partially, LP-EM has evolved into a new microscopy method with nanometer spatial resolution and sub-second temporal resolution for analysis of soft matter in materials science and biology. Both experimental design and applications of LP-EM for soft matter materials science and biological research are reviewed, and a perspective of possible future directions is given.

Partial Scanning Transmission Electron Microscopy with Deep Learning

Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.