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.

A method is presented to determine the feature resolution of physically relevant metrics of data obtained from segmented image sets. The presented method determines the best-fit distribution curve of a dataset by analyzing a truncated portion of the data. An effective resolvable size for the metric of interest is established when including parts of the truncated dataset results in exceeding a specified error tolerance. As such, this method allows for the determination of the feature resolution regardless of the processing parameters or imaging instrumentation. Additionally, the number of missing objects that exist below the resolution of the instrumentation may be estimated. The application of the developed method was demonstrated on data obtained via 2D scanning electron microscopy of a pressed explosive material and from 3D micro X-ray computed tomography of a polymer-bonded explosive material. It was shown that the minimum number of pixels/voxels required for the accurate determination of a physically relevant metric is dependent on the metric of interest. This proposed method, utilizing the prior knowledge of the distribution of metrics of interest, was found to be well suited to determine the feature resolution in applications where large datasets can be achieved.

Methods within the domain of artificial intelligence are gaining traction for solving a range of materials science objectives, notably the use of deep neural networks for computer vision for the analysis of electron diffraction patterns. An important component of deploying these models is an understanding of the performance as experimental diffraction conditions are varied. This knowledge can inspire confidence in the classifications over a range of operating conditions and identify where performance is degraded. Elucidating the relative impact of each parameter will suggest the most important parameters to vary during the collection of future training data. Knowing which data collection efforts to prioritize is of concern given the time required to collect or simulate vast libraries of diffraction patterns for a wide variety of materials without considering varying any parameters. In this work, five parameters, frame averaging, detector tilt, sample-to-detector distance, accelerating voltage, and pattern resolution, essential to electron diffraction are individually varied during the collection of electron backscatter diffraction patterns to explore the effect on the classifications produced by a deep neural network trained from diffraction patterns captured using a fixed set of parameters. The model is shown to be resilient to nearly all the individual changes examined here.

Scanning transmission X-ray microscopy is a powerful method for mapping chemical phases in nano-materials. The point spread function (PSF) of a conventional zone-plate-based microscope limits the achievable spatial resolution and also results in spatially resolved spectra that do not accurately reflect the spatial heterogeneity of the samples when the scale of the detail approaches the probe size. X-ray ptychography, a coherent-scattering-based imaging scheme that effectively removes the probe from the image data, returns accurate spectra from regions smaller than the probe size. We show through simulation how the long tails on the PSF of an x-ray optic can cause spectral distortion near a boundary between two spectrally distinct regions. The resulting apparent point spectra can appear mixed, with the species on one side of the boundary seeming to be present on the other even at a distance from the boundary equal to several times the spatial resolution. We further demonstrate the effect experimentally and show that ptychographic microscopy can return the expected spectra from a model system, whereas conventional microscopy does not.

Renal aging is a progressive, physiological, and anatomical change that naturally occurs in all animal species. To date, no information is available concerning the aging-related structural and functional changes in camel kidneys. A total of 25 healthy male camels (14 aged 4–6 years and 11 aged 18–22 years) were included in this study. After the camels were slaughtered, samples were collected from all the camels’ kidneys and prepared for histopathological, immunohistochemical, and gene expression evaluations. The most striking observation was the significant decline in the immunohistochemical abundance of podocin and the significant upregulation of smoothening in the aging camels’ kidneys. However, the nonsignificant changes have reported for nephrin, calbindin, autophagy 5 (ATG5), aquaporin 1, and toll-like receptor 9. Furthermore, the mRNA expressions of sirtuin 1, superoxide dismutase 1, superoxide dismutase 2, peroxisome proliferator-activated receptor alpha, B-cell lymphoma 2 (Bcl-2), and erythropoietin were significantly decreased in the aging camels’ kidneys. While the significant upregulation of Bcl-2-associated X protein and the nonsignificant increase in ATG5 expression levels were reported in the aging camels’ kidneys. The present findings provide better understanding of the complex events and initiating factors of aging, allowing for the development of a future therapeutic strategy to preserve adequate renal function throughout life.

The recent development of electron-sensitive and pixelated detectors has attracted the use of four-dimensional scanning transmission electron microscopy (4D-STEM). Here, we present a precession electron diffraction-assisted 4D-STEM technique for automated orientation mapping using diffraction spot patterns directly captured by an in-column scintillator-based complementary metal-oxide-semiconductor (CMOS) detector. We compare the results to a conventional approach, which utilizes a fluorescent screen filmed by an external charge charge-coupled device camera. The high-dynamic range and signal-to-noise characteristics of the detector greatly improve the image quality of the diffraction patterns, especially the visibility of diffraction spots at high scattering angles. In the orientation maps reconstructed via the template matching process, the CMOS data yield a significant reduction of false indexing and higher reliability compared to the conventional approach. The angular resolution of misorientation measurement could also be improved by masking reflections close to the direct beam. This is because the orientation sensitive, weak, and small diffraction spots at high scattering angles are more significant. The results show that fine details, such as nanograins, nanotwins, and sub-grain boundaries, can be resolved with a sub-degree angular resolution which is comparable to orientation mapping using Kikuchi diffraction patterns.

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.

Smooth muscle cells (SMCs) are dynamic and transition from a contractile to a synthetic phenotype under different circumstances. Plasma factors (fibrin and transforming growth factors, TGFs) are possible components affecting SMCs differentiation and behavior. Thus, the objective of this work was to investigate how the fibrin matrix and TGFs affect SMCs differentiation and motility behavior. SMCs invaded the fibrin gel and adopted a stellate phenotype while reducing the expression of differentiation markers (Acta2, Myh11, and Smtn). At the ultrastructural level, SMCs did not assemble a basal lamina and showed numerous blebs along the entire cell surface. This transition was not associated with changes in focal adhesion kinase (FAK) content and phosphorylation status but reflected a marked change in FAK distribution in the cytoplasm. After 48 h in culture, SMCs caused an active degradation of the fibrin gel. Additionally, we tested the SMCs response to TGFs in a cell layer wound repair assay. TGFα, but not TGFβ1 or TGFβ3, had significantly increased motility. In conclusion, prostatic SMCs present a phenotypical transition when cultured on fibrin, adopting a micro-blebbing based motility behavior and increasing migration in response to TGFα.

This article presents a fast and highly efficient algorithm developed to reconstruct a three-dimensional (3D) volume with a high spatial precision from a set of field ion microscopy (FIM) images, and specific tools developed to characterize crystallographic lattice and defects. A set of FIM digital images and image processing algorithms allow the construction of a 3D reconstruction of the sample at the atomic scale. The capability of the 3D FIM to resolve the crystallographic lattice and the finest defects in metals opens a new way to analyze materials. This spatial precision was quantified on tungsten, analyzed at different analyzing conditions. A specific data mining tool, based on Fourier transforms, was also developed to characterize lattice distortions in the reconstructed volumes. This tool has been used in simulated and experimental volumes to successfully locate and characterize defects such as dislocations and grain boundaries.

The authors present the application of a retarding field between the electron objective lens and sample in an integrated fluorescence and electron microscope. The retarding field enhances signal collection and signal strength in the electron microscope. This is beneficial for samples prepared for integrated fluorescence and electron microscopy as the amount of staining material added to enhance electron microscopy signal is typically lower compared to conventional samples in order to preserve fluorescence. We demonstrate signal enhancement through the applied retarding field for both 80-nm post-embedding immunolabeled sections and 100-nm in-resin preserved fluorescence sections. Moreover, we show that tuning the electron landing energy particularly improves imaging conditions for ultra-thin (50 nm) sections, where optimization of both retarding field and interaction volume contribute to the signal improvement. Finally, we show that our integrated retarding field setup allows landing energies down to a few electron volts with 0.3 eV dispersion, which opens new prospects for assessing electron beam induced damage by in situ quantification of the observed bleaching of the fluorescence following irradiation.

In Part I of this diptych, we outline the parallel mode of differential phase contrast (TEM-DPC), which uses real-space distortion of Fresnel images arising from electrostatic or magnetostatic fields to quantify the phase gradient of samples with some degree of structural contrast. We present an analysis methodology and the associated software tools for the TEM-DPC method and, using them together with numerical simulations, compare the technique to the widely used method of phase recovery based on the transport-of-intensity equation (TIE), thereby highlighting the relative advantages and limitations of each. The TEM-DPC technique is particularly suitable for in situ studies of samples with significant structural contrast and, as such, complements the TIE method since structural contrast usually hinders the latter, but is an essential feature that enables the former. In Part II of this work, we apply the theory and methodology presented to the analysis of experimental data to gain insight into two-dimensional magnetic phase transitions.

In Part I of this diptych, we outlined the theory and an analysis methodology for quantitative phase recovery from real-space distortions of Fresnel images acquired in the parallel mode of transmission electron microscopy (TEM). In that work, the properties of the method, termed TEM-differential phase contrast (TEM-DPC), were highlighted through the use of simulated data. In this work, we explore the use of the TEM-DPC technique with experimental cryo-TEM images of a thin lamella of a low-temperature two-dimensional (2D) ferromagnetic material, K2CuF4, to perform two tasks. First, using images recorded below the ordering temperature, we compare the TEM-DPC method with the transport of intensity one for phase recovery and discuss the relative advantages the former has for experimental data. Second, by tracking the induction of the sample as it is driven through a phase transition by heating, we extract estimates for the critical temperature and critical exponent of the order parameter. The value of the latter is consistent with the 2D XY class, raising the prospect that a Kosterlitz–Thoules transition may have occurred.

Engineered biomaterials provide unique functions to overcome the bottlenecks seen in biomedicine. Hence, a technique for rapid and routine tests of collagen is required, in which the test items commonly include molecular weight, crosslinking degree, purity, and sterilization induced structural change. Among them, the crosslinking degree mainly influences collagen properties. In this study, second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) microscopy are used in combination to explore the collagen structure at molecular and macromolecular scales. These measured parameters are applied for the classification and quantification among the different collagen scaffolds, which were verified by other conventional methods. It is demonstrated that the crosslinking status can be analyzed from SHG images and presented as the coherency of collagen organization that is correlated with the mechanical properties. Also, the comparative analyses of SHG signal and relative CARS signal of amide III band at 1,240 cm−1 to δCH2 band at 1,450 cm−1 of these samples provide information regarding the variation of the molecular structure during a crosslinking process, thus serving as nonlinear optical signatures to indicate a successful crosslinking.

The development of microfabricated liquid cells has enabled dynamic studies of nanostructures within a liquid environment with electron microscopy. While such setups are most commonly found in transmission electron microscope (TEM) holders, their implementation in a scanning electron microscope (SEM) offers intriguing potential for multi-modal studies where the large chamber volume allows for the integration of multiple detectors. Here, we describe an electrochemical liquid cell SEM platform that employs the same cells enclosed by silicon nitride membrane windows found in liquid cell TEM holders and demonstrate the imaging of copper oxide nanoparticles in solution using both backscattered and transmitted electrons. In particular, the transmitted electron images collected at high scattering angles show contrast inversion at liquid layer thicknesses of several hundred nanometers, which can be used to determine the presence of liquid in the cell, while maintaining enough resolution to image nanoparticles that are tens of nanometers in size. Using Monte Carlo simulations, we show that both imaging modes have their advantages for liquid phase imaging and rationalize the contrast inversion observed in the transmitted electron image.