Current generation electron monochromators employed as attachments to scanning transmission electron microscopes (STEM) offer the ability to obtain vibrational information from materials using electron energy-loss spectroscopy (EELS). We show here that in crystals, long- and short-wavelength phonon modes can be probed simultaneously with on-axis vibrational STEM EELS. The long-wavelength phonons are probed via dipole scattering, while the short-wavelength modes are probed via impact scattering of the incident electrons. The localized character of the short-wavelength modes is demonstrated by scanning the electron beam across the edge of a hexagonal boron nitride nanoparticle. It is found that employing convergence angles that encompass multiple Brillouin zone boundaries enhances the short-wavelength phonon contribution to the vibrational energy-loss spectrum much more than that achieved by employing collection angles that encompass multiple Brillouin zone boundaries. Probing short-wavelength phonons at high spatial resolution with on-axis vibrational STEM EELS will help develop a fundamental connection between vibrational excitations and bonding arrangements at atomic-scale heterogeneities in materials.

Atomic force microscopy (AFM) is a well-known tool for studying surface roughness and to collect depth information about features on the top atomic layers of samples. By combining secondary ion mass spectroscopy (SIMS) with focused ion beam (FIB) milling in a scanning electron microscope (SEM), chemical information of sputtered structures can be visualized and located with high lateral and depth resolution. In this paper, a high vacuum (HV) compatible AFM was installed in a TESCAN FIB-SEM instrument that was equipped with a time-of-flight SIMS (ToF-SIMS) detector. To calibrate the sputtering rate and measure the induced roughness caused by the ToF-SIMS analysis, subsequent AFM measurements were performed on an inorganic multilayer vertical cavity surface-emitting laser sample. Normalized sputtering rates were used to aid the accurate three-dimensional reconstruction of the sputtered volume's chemical composition. Achievable resolution, surface roughness during sputtering, and surface oxidation issues were analyzed. The integration of complementary detectors opens up the ability to determine the sample properties as well as to understand the influence of the Ga+ ion sputtering method on the sample surface during the analysis.

Currently, the automated electron backscatter diffraction (EBSD) technique only allows the differentiation of the Laue groups based on an electron backscatter pattern (EBSP). This article shows that information concerning the lattice plane polarity is not only stored in the EBSP, but also in the Hough transformed EBSP where it can be easily accessed for automated evaluation. Polar Kikuchi bands lead to asymmetric peaks during the Hough transformation that are dependent on the atomic number difference of the involved atoms. The effect can be strong enough to be detected when evaluating the intensities of the regular excess and deficiency lines. Polarity detection from the Hough transformation of an EBSP cannot only enhance the utility of the EBSD technique and expand the information gained from it, but also illustrates a path toward automated polarity determination during EBSD scans.

A reformulated implementation of single-sideband ptychography enables analysis and display of live detector data streams in 4D scanning transmission electron microscopy (STEM) using the LiberTEM open-source platform. This is combined with live first moment and further virtual STEM detector analysis. Processing of both real experimental and simulated data shows the characteristics of this method when data are processed progressively, as opposed to the usual offline processing of a complete data set. In particular, the single-sideband method is compared with other techniques such as the enhanced ptychographic engine in order to ascertain its capability for structural imaging at increased specimen thickness. Qualitatively interpretable live results are obtained also if the sample is moved, or magnification is changed during the analysis. This allows live optimization of instrument as well as specimen parameters during the analysis. The methodology is especially expected to improve contrast- and dose-efficient in situ imaging of weakly scattering specimens, where fast live feedback during the experiment is required.

A simple and fast method for thickness measurements using electron probe microanalysis (EPMA) is described. The method is applicable on samples with a thickness smaller than the electron depth range and does not require any knowledge of instrumental parameters. The thickness is determined by means of the distance that electrons travel inside the sample before crossing through it. Samples are first deposited on a substrate that, when reached by the transmitted electrons, produces an X-ray signal. The measured characteristic X-ray line intensity of the substrate is later used to determine the energy of transmitted electrons, which is proportional to the distance that electrons travel inside the sample. The study was performed on spherical K411 glass particles and cylindrical particles of U–Ce oxide with a size ranging from 0.2 to 4 μm. The measured thicknesses of all the studied particles showed good agreements with the real values. Although the method is only validated on particles with usual shapes, it can be applied to determine a local thickness of thin samples with irregular morphologies. This can help solving multiple issues in analysis with EPMA of non-bulk samples exhibiting complex geometries. Three dimensional microscopic imaging could also find a good utility in the described method.

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

Technological opportunities are explored to enhance detection schemes in transmission electron microscopy (TEM) that build on the detection of single-electron scattering events across the typical spectrum of interdisciplinary applications. They range from imaging with high spatiotemporal resolution to diffraction experiments at the window to quantum mechanics, where the wave-particle dualism of single electrons is evident. At the ultimate detection limit, where isolated electrons are delivered to interact with solids, we find that the beam current dominates damage processes instead of the deposited electron charge, which can be exploited to modify electron beam-induced sample alterations. The results are explained by assuming that all electron scattering are inelastic and include phonon excitation that can hardly be distinguished from elastic electron scattering. Consequently, a coherence length and a related coherence time exist that reflect the interaction of the electron with the sample and change linearly with energy loss. Phonon excitations are of small energy (<100 meV), but they occur frequently and scale with beam current in the irradiated area, which is why we can detect their contribution to beam-induced sample alterations and damage.

Accurate control and measurement of real-time sample temperature are critical for the understanding and interpretation of the experimental results from in situ heating experiments inside environmental transmission electron microscope (ETEM). However, quantifying the real-time sample temperature remains a challenging task for commercial in situ TEM heating devices, especially under gas conditions. In this work, we developed a home-made micro-electrical-mechanical-system (MEMS) heater with unprecedented small temperature gradient and thermal drift, which not only enables the temperature evolution caused by gas injection to be measured in real-time but also makes the key heat dissipation path easier to model to theoretically understand and predict the temperature decrease. A new parameter termed as “gas cooling ability (H)”, determined purely by the physical properties of the gas, can be used to compare and predict the gas-induced temperature decrease by different gases. Our findings can act as a reference for predicting the real temperature for in situ heating experiments without closed-loop temperature sensing capabilities in the gas environment, as well as all gas-related heating systems.

We introduce a novel composite holey gold support that prevents cryo-crinkling and reduces beam-induced motion of soft specimens, building on the previously introduced all-gold support. The composite holey gold support for high-resolution cryogenic electron microscopy of soft crystalline membranes was fabricated in two steps. In the first step, a holey gold film was transferred on top of a molybdenum grid. In the second step, a continuous thin carbon film was transferred onto the holey gold film. This support (Au/Mo grid) was used to image crystalline synthetic polymer membranes. The low thermal expansion of Mo is not only expected to avoid cryo-crinkling of the membrane when the grids are cooled to cryogenic temperatures, but it may also act to reduce whatever crinkling existed even before cooling. The Au/Mo grid exhibits excellent performance with specimens tilted to 45°. This is demonstrated by quantifying beam-induced motion and differences in local defocus values. In addition, images of specimens on the Au/Mo grids that are tilted at 45° show high-resolution information of the crystalline membranes that, after lattice-unbending, extends beyond 1.5 Å in the direction perpendicular to the tilt axis.

A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

The quantification of the particle size and the number concentration (PNC) of nanoparticles (NPs) is key for the characterization of nanomaterials. Transmission electron microscopy (TEM) is often considered as the gold standard for assessing the size of NPs; however, the TEM sample preparation suitable for estimating the PNC based on deposited NPs is challenging. Here, we use an ultrasonic nebulizer (USN) to transfer NPs from aqueous suspensions into dried aerosols which are deposited on TEM grids in an electrostatic precipitator of an aerosol monitor. The deposition efficiency of the electrostatic precipitator was ≈2%, and the transport efficiency of the USN was ≈7%. Experiments using SiO2 NPs (50–200 nm) confirmed an even deposition of the nebulized particles in the center of the TEM grids. PNCs of the SiO2 NPs derived from TEM images underestimated the expected PNCs of the suspensions by a factor of up to three, most likely resulting from droplet coagulation and NP aggregation in the USN. Nevertheless, single particles still dominated the PNC. Our approach results in reproducible and even deposition of particles on TEM grids suitable for morphological analysis and allows an estimation of the PNC in the suspensions based on the number of particles detected by TEM.

In shape-from-focus (SFF) methods, a single focus measure is used to compute the focus volume. However, it seems that a single focus measure operator is not capable of computing accurate focus values for the images of diverse types of object shapes. Furthermore, most of the SFF methods try to improve the depth map without considering any additional structural or prior information. Consequently, the extracted shape of the object might lack important details. In this work, we address these problems and suggest a method in which depth hypotheses are combined for a more accurate 3D shape through 3D weighted least squares. First, depth hypotheses are obtained by applying a number of focus operators. Then, structural prior or guidance volume is extracted from the focus measure volumes. Finally, a 3D weighted least squares optimization technique is applied to the depth hypothesis volume, where weights are computed from the guidance volume. Thus, by inducing structural prior, an improved resultant depth map is obtained. The proposed method was tested using various image sequences of synthetic and microscopic real objects. Experimental results and comparative analysis demonstrated the effectiveness of the proposed method.

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid 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.

Cell confluence is an important metric in cell culture, as proper timing is essential to maintain cell phenotype and culture quality. To estimate cell confluence, transparent cells are observed under a phase-contrast or differential interference contrast microscope by a biologist, whose estimations are error-prone and subjective. To overcome the necessity of using the phase-contrast microscope and reducing intra- and inter-observer errors, we have proposed an algorithm that automatically measures cell confluence by using a commonly used brightfield microscope. The proposed method consists of a transport-of-intensity equation-based brightfield microscopic image processing, an image reconstruction method, and an adaptive image segmentation method based on edge detection, entropy filtering, and range filtering. Experimental results have shown that our method has outperformed several popular algorithms, with an F-score of 0.84 ± 0.07, in images with various cell confluence values. The proposed algorithm is robust and accurate enough to perform confluence measurement with various lighting conditions under a low-cost brightfield microscope, making it simple and cost-effective to use for a fully automated cell culture process.