Obsidian is volcanic glass that results from the rapid cooling of silica-rich melt. Nanoscale crystallites precipitate out of the melt prior to solidification and remain embedded in the amorphous matrix. These crystallites provide information on the flow kinetics and composition of the melt. Due to the sparsity and size of nanolites, studies often focus on supramicron crystallites. This research takes advantage of the conchoidal fracture of obsidian by knapping samples with nanometer-thin edges for transmission electron microscopy characterization. Nanolites in the amorphous matrix are studied using energy-dispersive spectroscopy (EDS) and electron diffraction. Certain alkali and alkaline-earth cations exhibit patterns of depletion near Fe-oxide nanolites. EDS is used to identify nanolites and variations in the composition of the matrix. Parallel beam diffraction and radial distribution function analysis of nearest-neighbor distances determine average bond lengths in the matrix near nanolites, showing that nanolites influence the nearby short-range ordering and atomic character of the matrix. Analysis reveals decreased mean nearest-neighbor distances in the matrix adjacent to nanolites compared to the bulk. Our methods exhibit the required sensitivity to detect variations in the composition and structure near nanolites, and our findings indicate that obsidian nanolites contribute to quantifiable localized changes in the amorphous structure.

The effect of sample preparation on a pre-aged Al–Mg–Si–Cu alloy has been evaluated using atom probe tomography. Three methods of preparation were investigated: electropolishing (control), Ga+ focused ion beam (FIB) milling, and Xe+ plasma FIB (PFIB) milling. Ga+-based FIB preparation was shown to introduce significant amount of Ga contamination throughout the reconstructed sample (≈1.3 at%), while no Xe contamination was detected in the PFIB-prepared sample. Nevertheless, a significantly higher cluster density was observed in the Xe+ PFIB-prepared sample (≈25.0 × 1023 m−3) as compared to the traditionally produced electropolished sample (≈3.2 × 1023 m−3) and the Ga+ FIB sample (≈5.6 × 1023 m−3). Hence, the absence of the ion milling species does not necessarily mean an absence of specimen preparation defects. Specifically, the FIB and PFIB-prepared samples had more Si-rich clusters as compared to electropolished samples, which is indicative of vacancy stabilization via solute clustering.

The morphology and composition of the corrosion products of archaeological arsenical copper alloys buried in a specific environment for a long time were investigated using optical microscopy (OM), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), micro X-ray fluorescence (μ-XRF), and X-ray diffraction (XRD). The analyses demonstrated that the alloy composition of the artifacts was copper-arsenic (Cu-AS) with significant amounts of lead in some samples. Cuprite, malachite, and copper (II) hydroxychlorides were observed on a completely mineralized matrix. The surface of the objects was covered by two main corrosion layers formed on the internal cuprous oxide. However, several successive layers of corrosion products were recognized on the artifacts in some cases. Furthermore, phosphatic corrosion products including, mimetite [Pb5(AsO4)3Cl], and pyromorphite [Pb5(PO4)3Cl] were identified. Conichalcite [CaCu(AsO4)](OH), which grew in the form of Liesegang rings, was also identified as a corrosion product of one of the objects. As and Pb exhibited some enrichment in the corrosion crusts of the artifacts. Environmental changes in burial conditions, particularly due to seasonal streams and consequent changes in soil corrosivity, vicinity of objects to bone material, along with the migration of alloy elements, especially lead and arsenic, can explain the morphological features of these archaeological objects.

Carbon xerogel nanocomposites with integrated Bi and Fe particles (C–Bi–Fe) represent an interesting model of carbon nanostructures decorated with multifunctional nanoparticles (NPs) with applicability for electrochemical sensors and catalysts. The present study addresses the fundamental aspects of the catalyzed growth of nano-graphites in C–Bi–Fe systems, relevant in charge transport and thermo-chemical processes. The thermal evolution of a C–Bi–Fe xerogel is investigated using different pyrolysis treatments. At lower temperatures (~750°C), hybrid bismuth iron oxide (BFO) NPs are frequently observed, while graphitization manifests under more specific conditions such as higher temperatures (~1,050°C) and reduction yields. An in situ heating TEM experiment reveals graphitization activity between 800 and 900°C. NP motion is directly correlated with textural changes of the carbon support due to the catalyzed growth of graphitic nanoshells and nanofibers as confirmed by HR-TEM and electron tomography (ET) for the graphitized sample. An exponential growth model for the catalyst dynamics enables the approximation of activation energies as 0.68 and 0.29–0.34 eV during reduction and graphitization stages. The results suggest some similarities with the tip growth mechanism, while oxygen interference and the limited catalyst–feed gas interactions are considered as the main constraints to enhanced growth.

Excitation–emission-spectral unmixing-based fluorescence resonance energy transfer (ExEm-spFRET) microscopy exhibits excellent robustness in living cells. We here develop an automatic ExEm-spFRET microscope with 3.04 s of time resolution for a quantitative FRET imaging. The user-friendly interface software has been designed to operate in two modes: administrator and user. Automatic background recognition, subtraction, and cell segmentation were integrated into the software, which enables FRET calibration or measurement in a one-click operation manner. In administrator mode, both correction factors and spectral fingerprints are only calibrated periodically for a stable system. In user mode, quantitative ExEm-spFRET imaging is directly implemented for FRET samples. We implemented quantitative ExEm-spFRET imaging for living cells expressing different tandem constructs (C80Y, C40Y, C10Y, and C4Y, respectively) and obtained consistent results for at least 3 months, demonstrating the stability of our microscope. Next, we investigated Bcl-xL-Bad interaction by using ExEm-spFRET imaging and FRET two-hybrid assay and found that the Bcl-xL-Bad complexes exist mainly in Bad-Bcl-xL trimers in healthy cells and Bad-Bcl-xL2 trimers in apoptotic cells. We also performed time-lapse FRET imaging on our system for living cells expressing Yellow Cameleon 3.6 (YC3.6) to monitor ionomycin-induced rapid extracellular Ca2+ influx with a time interval of 5 s for total 250 s.

Scanning transmission electron microscopy is a crucial tool for nanoscience, achieving sub-nanometric spatial resolution in both image and spectroscopic studies. This generates large datasets that cannot be analyzed without computational assistance. The so-called machine learning procedures can exploit redundancies and find hidden correlations. Principal component analysis (PCA) is the most popular approach to denoise data by reducing data dimensionality and extracting meaningful information; however, there are many open questions on the accuracy of reconstructions. We have used experiments and simulations to analyze the effect of PCA on quantitative chemical analysis of binary alloy (AuAg) nanoparticles using energy-dispersive X-ray spectroscopy. Our results demonstrate that it is possible to obtain very good fidelity of chemical composition distribution when the signal-to-noise ratio exceeds a certain minimal level. Accurate denoising derives from a complex interplay between redundancy (data matrix size), counting noise, and noiseless data intensity variance (associated with sample chemical composition dispersion). We have suggested several quantitative bias estimators and noise evaluation procedures to help in the analysis and design of experiments. This work demonstrates the high potential of PCA denoising, but it also highlights the limitations and pitfalls that need to be avoided to minimize artifacts and perform reliable quantification.

In the present paper, as part of an interdisciplinary research project (Priority Programme SPP2045), we propose a possible way to design an open access archive for particle-discrete tomographic datasets: the PARROT database (https://parrot.tu-freiberg.de). This archive is the result of a pilot study in the field of particle technology and three use cases are presented for illustrative purposes. Instead of providing a detailed instruction manual, we focus on the methodologies of such an archive. The presented use cases stem from our working group and are intended to demonstrate the advantage of using such an archive with concise and consistent data for potential and ongoing studies. Data and metadata merely serve as examples and need to be adapted for disciplines not concerned here. Since all datasets within the PARROT database and its source code are freely accessible, this study represents a starting point for similar projects.

Liquid-electron microscopy (EM), the room-temperature correlate to cryo-EM, is a rapidly growing field providing high-resolution insights of macromolecules in solution. Here, we describe how liquid-EM experiments can incorporate automated tools to propel the field to new heights. We demonstrate fresh workflows for specimen preparation, data collection, and computing processes to assess biological structures in liquid. Adeno-associated virus (AAV) and the SARS-CoV-2 nucleocapsid (N) were used as model systems to highlight the technical advances. These complexes were selected based on their major differences in size and natural symmetry. AAV is a highly symmetric, icosahedral assembly with a particle diameter of ~25 nm. At the other end of the spectrum, N protein is an asymmetric monomer or dimer with dimensions of approximately 5–7 nm, depending upon its oligomerization state. Equally important, both AAV and N protein are popular subjects in biomedical research due to their high value in vaccine development and therapeutic efforts against COVID-19. Overall, we demonstrate how automated practices in liquid-EM can be used to decode molecules of interest for human health and disease.

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

Due to less light scattering and a better signal-to-noise ratio in deep imaging, two-photon fluorescence microscopy (TPFM) has been widely used in biomedical photonics since its advent. However, optical aberrations degrade the performance of TPFM in terms of the signal intensity and the imaging depth and therefore restrict its application. Here, we introduce adaptive optics based on the genetic algorithm to detect the distorted wavefront of the excitation laser beam and then perform aberration correction to optimize the performance of TPFM. By using a spatial light modulator as the wavefront controller, the correction phase is obtained through a signal feedback loop and a process of natural selection. The experimental results show that the signal intensity and imaging depth of TPFM are improved after aberration correction. Finally, the method was applied to two-photon fluorescence lifetime imaging, which helps to improve the signal-to-noise ratio and the accuracy of lifetime analysis. Furthermore, the method can also be implemented in other experiments, such as three-photon microscopy, light-sheet microscopy, and super-resolution microscopy.

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

As a real-space technique, atomic-resolution STEM imaging contains both amplitude and geometric phase information about structural order in materials, with the latter encoding important information about local variations and heterogeneities present in crystalline lattices. Such phase information can be extracted using geometric phase analysis (GPA), a method which has generally focused on spatially mapping elastic strain. Here we demonstrate an alternative phase demodulation technique and its application to reveal complex structural phenomena in correlated quantum materials. As with other methods of image phase analysis, the phase lock-in approach can be implemented to extract detailed information about structural order and disorder, including dislocations and compound defects in crystals. Extending the application of this phase analysis to Fourier components that encode periodic modulations of the crystalline lattice, such as superlattice or secondary frequency peaks, we extract the behavior of multiple distinct order parameters within the same image, yielding insights into not only the crystalline heterogeneity but also subtle emergent order parameters such as antipolar displacements. When applied to atomic-resolution images spanning large (~0.5 × 0.5 μm2) fields of view, this approach enables vivid visualizations of the spatial interplay between various structural orders in novel materials.

Low-voltage scanning electron microscopes (LV-SEMs) are widely used in nanoscience. However, image resolution for SEMs is restricted by chromatic aberration due to energy spread of the electron beam at low acceleration voltage. This study introduces a new monochromator (MC) with offset cylindrical lenses (CLs) as one solution for LV-SEMs. The MC optics, with highly excited CLs in offset layouts, has advantageous high performance and simple experimental setup, making it suitable for field emission LV-SEMs. In a preliminary evaluation, our MC reduced the energy spread from 770 to 67 meV. The MC was integrated into a commercial SEM equipped with an out-lens (a conventional objective lens without immersion magnetic or retarding electric fields) and an Everhart–Thornley detector. Comparing SEM images under two conditions with the MC turned on or off, the spatial resolution was improved by 58% at 0.5 and 1 keV. The filtering effect of the MC decreased the probe current with a ratio (i.e., transmittance) of 5.7%, which was consistent with estimations based on measured energy spreads. To the best of our knowledge, this is the first report on an effective MC with higher-energy resolution than 100 meV and the results offer encouraging prospects for LV-SEM technology.

Precision and accuracy of quantitative scanning transmission electron microscopy (STEM) methods such as ptychography, and the mapping of electric, magnetic, and strain fields depend on the dose. Reasonable acquisition time requires high beam current and the ability to quantitatively detect both large and minute changes in signal. A new hybrid pixel array detector (PAD), the second-generation Electron Microscope Pixel Array Detector (EMPAD-G2), addresses this challenge by advancing the technology of a previous generation PAD, the EMPAD. The EMPAD-G2 images continuously at a frame-rates up to 10 kHz with a dynamic range that spans from low-noise detection of single electrons to electron beam currents exceeding 180 pA per pixel, even at electron energies of 300 keV. The EMPAD-G2 enables rapid collection of high-quality STEM data that simultaneously contain full diffraction information from unsaturated bright-field disks to usable Kikuchi bands and higher-order Laue zones. Test results from 80 to 300 keV are presented, as are first experimental results demonstrating ptychographic reconstructions, strain and polarization maps. We introduce a new information metric, the maximum usable imaging speed (MUIS), to identify when a detector becomes electron-starved, saturated or its pixel count is mismatched with the beam current.

The simulation program “SEM Nano” is introduced to explain and visualize probe formation in field-emission scanning electron microscopes (SEMs). The program offers an easy and intuitive graphical user interface (GUI) to provide input in terms of understandable SEM parameters and visualization of the output. The simulations are based on wave optics treatment of the electron beam in the SEM column. Based on input parameters provided by the user, the spatial intensity distribution of electrons is calculated at the specimen by incorporating the effects of diffraction, aberrations, coherence, and noise. Given the specimen structure signal (So), the program has the capability to produce an image of the specimen using the electron probe intensity distribution. Finally, a feature is provided to reconstruct the electron probe intensity from the noisy image using a Wiener filter-based deconvolution.

Classical $k$-ratio models, for example, ZAF and $\phi ( \rho z)$, used in electron probe microanalysis (EPMA) assume a homogeneous or multilayered material structure, which essentially limits the spatial resolution of EPMA to the size of the interaction volume where characteristic X-rays are produced. We present a new model for characteristic X-ray emission that avoids assumptions on the material structure to not restrict the resolution of EPMA a priori. Our model bases on the spherical harmonic ($P_{\rm N}$) approximation of the Boltzmann equation for electron transport in continuous slowing down approximation. $P_{\rm N}$ models have a simple structure, are hierarchical in accuracy and well-suited for efficient adjoint-based gradient computation, which makes our model a promising alternative to classical models in terms of improving the resolution of EPMA in the future. We present results of various test cases including a comparison of the $P_{\rm N}$ model to a minimum entropy moment model as well as Monte-Carlo (MC) trajectory sampling, a comparison of $P_{\rm N}$-based $k$-ratios to $k$-ratios obtained with MC, a comparison with experimental data of electron backscattering yields as well as a comparison of $P_{\rm N}$ and MC based on characteristic X-ray generation in a three-dimensional material probe with fine structures.

Determining spatial resolution from images is crucial when optimizing focus, determining smallest resolvable object, and assessing size measurement uncertainties. However, no standard algorithm exists to measure resolution from electron microscopy (EM) images, though several have been proposed, where most require user decisions. We present the Spatial Image Resolution Assessment by Fourier analysis (SIRAF) algorithm that uses fast Fourier transform analysis to estimate resolution directly from a single image without user inputs. The method is derived from the underlying assumption that objects display intensity transitions, resembling a step function blurred by a Gaussian point spread function. This hypothesis is tested and verified on simulated EM images with known resolution. To identify potential pitfalls, the algorithm is also tested on simulated images with a variety of settings, and on real SEM images acquired at different magnification and defocus settings. Finally, the versatility of the method is investigated by assessing resolution in images from several microscopy techniques. It is concluded that the algorithm can assess resolution from a large selection of image types, thereby providing a measure of this fundamental image parameter. It may also improve autofocus methods and guide the optimization of magnification settings when balancing spatial resolution and field of view.

NeXL is a collection of Julia language packages (libraries) for X-ray microanalysis data processing. NeXLCore provides basic atomic and X-ray physics data and models including support for microanalysis-related data types for materials and k-ratios. NeXLMatrixCorrection provides algorithms for matrix correction and iteration. NeXLSpectrum provides utilities and tools for energy-dispersive X-ray spectrum and hyperspectrum analysis including display, manipulation, and fitting. NeXL is integrated with the Julia language infrastructure. NeXL builds on the Gadfly plotting library and the DataFrames tabular data library. When combined with the DrWatson package, NeXL can provide a highly reproducible environment in which to process microanalysis data. Data availability and reproducible data analysis are two keys to scientific reproducibility. Not only should readers of journal articles have access to the data, they should also be able to reproduce the analysis steps that take the data to final results. This paper will both discuss the NeXL framework and provide examples of how it can used for reproducible data analysis.

The present study investigated the corneal sub-basal nerve plexus (SNP) modifications in glaucoma. Ninety-five glaucomatous patients were enrolled and divided into Group 1 and 2, preserved and preservative-free mono-therapy (30 and 28 patients), and Group 3, multi-therapy (37). Thirty patients with dry eye disease (DED) and 32 healthy subjects (HC) served as controls. In vivo confocal microscopy evaluated the nerve fibers density (CNFD), length (CNFL), thickness (CNFT), branching density (CNBD), and dendritic cell density (DCD). CNFD, CNFL, and CNBD were reduced in Group 3 and DED compared to HC (p < 0.05). CNFL was reduced in Group 3 compared to Group 2 (p < 0.05), and in Group 1 compared to HC (p < 0.001). CNFD, CNBD, and CNFT did not differ between glaucomatous groups. DCD was higher in Group 3 and DED compared to HC and Group 2 (p < 0.01). Group 3 showed worse ocular surface disease index (OSDI) scores compared to Group 1, 2, and HC (p < 0.05). CNFL and DCD correlated with OSDI score in Group 3 (r = −0.658, p < 0.001; r = 0.699, p = 0.002). Medical therapy for glaucoma harms the corneal nerves, especially in multi-therapy regimens. Given the relations with the OSDI score, SNP changes seem features of glaucoma therapy-related OSD and negatively affects the patient's quality of life.