The development of radiation tolerant materials is of technological importance for establishing safe operating systems in the nuclear industry, from power generation to the immobilization of high-level radioactive waste. Harsh radiation environments generate interstitials and vacancies in materials, and their accumulation leads to structural changes, including order-to-disorder phase transformations and amorphization. These structural changes are induced locally on an atomic scale; therefore, transmission electron microscopy is a useful technique for analyzing radiation effects in materials. In addition, the strong interaction between matter and electrons enables the detection of weak signals associated with phase transformations, such as diffuse scattering and halo rings. This article provides an overview of radiation-induced amorphous structures in materials consisting of light elements, such as boron carbide and silicon oxycarbide, as well as the short-range ordered structure that appears during an order-to-disorder phase transformation in fluorite structural derivatives.

Accurate quantification of the individual layers of the intestinal wall is essential for biomechanical modeling and the development of gastrointestinal medical devices. Traditional microscopy techniques, though widely used, are limited by their two-dimensional nature and potential for tissue distortion due to complex sample preparation. This study evaluates X-ray microscopy (XRM) as a non-destructive, three-dimensional alternative for measuring the thicknesses of the four major layers of porcine large intestinal tissue: serosa, muscularis externa, submucosa, and mucosa. Using the ZEISS Xradia 620 Versa, XRM scans were compared to standard light microscopy. XRM successfully visualized all four layers and yielded thickness measurements that were consistent with those obtained via standard microscopy, despite natural biological variability. Notably, XRM scans allowed for 3D reconstructions of tissue vasculature and did not need extensive preparation or staining. These findings establish XRM as a powerful and practical method for morphological analysis of soft tissues and offer the first reported absolute layer thicknesses for each layer of porcine large intestinal tissue which can be used in layer-specific constitutive biomechanical models. This study compares measurements of intestinal tissue layers obtained using X-ray microscopy to those from traditional light microscopy. The results show that X-ray microscopy provides comparable data while offering the advantages of 3D imaging and minimal tissue preparation. (Figure 2).

According to theoretical predictions, local strain in the bent regions of flexible nanowires can alter their electronic structure. However, the experimental validation of such strain-induced effects remains elusive. In this study, we established a clear correlation between local structural deformation and electronic properties in bent hexagonal-WO3 nanowires using four-dimensional scanning transmission electron microscopy and electron energy loss spectroscopy. Although a simple geometric bending model predicts an expansion of the (0001) lattice spacing on the outer side of the bend, our direct observations revealed a larger expansion than predicted. This lattice expansion was accompanied by a significant reduction in bandgap energy. We employed density functional theory calculations and crystal orbital Hamilton population analyses to provide a theoretical framework for these findings. These results provide direct experimental evidence of strain-induced modulation of the electronic structure in metal oxide nanowires.

This review examines the effects of thermal vibrations on core-level excitation spectra, with a particular emphasis on the Ti L  2,3-edge spectra of cubic perovskite-type titanium oxides (SrTiO3 and PbTiO3). Based on combining scanning transmission electron microscopy energy-loss near-edge structure analyses with cluster-type crystal-field multiplet calculations, the influence of atomic thermal vibrations on the fine structure of the Ti L  2,3-edge is investigated, and it is demonstrated that the thermal vibration of oxygen atoms in cubic SrTiO3 can be estimated from the spectrum by fitting experimental and theoretical results. The same approach was extended to cubic PbTiO3 such that isotropic thermal vibrations were identified that relate to the difference in the transition to a low-temperature tetragonal phase. Although the present technique does not directly resolve phonon modes, it treats thermal factors as adjustable parameters, enabling the identification of subtle vibrational features even in materials already widely studied. Further investigation of the relationship between thermal vibrations and the fine structure of core-loss spectra could assist in elucidating certain material properties. This review explores the effects of thermal vibrations on Ti L  2,3-edge spectra of cubic perovskite oxides (SrTiO3, PbTiO3). Combining STEM-ELNES with crystal-field multiplet calculations, it shows that oxygen thermal vibrations can be estimated from spectral fitting, revealing subtle vibrational features and their relation to phase transitions.

Cryoelectron microscopy is a powerful technique for high-resolution imaging of nonaqueous liquids, but challenges remain regarding imaging and data interpretation. Recent advancements in estimating the physicochemical properties of pure organic liquids at cryogenic temperatures have enhanced the selection of imaging and pre-treatment conditions. However, whether binary mixtures behave similarly to pure substances is still unclear. Furthermore, focused ion beam (FIB) milling facilitates site-specific cross-sectioning, but its effects on the microscopic morphology of frozen organic liquids are not well understood. In this study, we investigated water-tetrahydrofuran (THF) binary mixtures as a model to explore their phase behavior and radiation damage under cryogenic conditions. Spectroscopic analyses indicated microscopic phase separation within the seemingly miscible water-THF mixtures, but their detailed structure has been a subject of ongoing debate. Using cryo-scanning electron microscopy with FIB (cryo-FIB-SEM), we visualized bicontinuous phase-separation. The domain sizes were consistent across spectroscopic data, thermally sublimed surfaces, and FIB cross-sections. Notably, FIB milling caused a significant loss of THF-rich regions, likely due to localized temperature increases of approximately 178 K, which is an order of magnitude greater than that in water-rich domains. We also noted the nanoparticles of electron-resistant carbazole-terminated carbon-bridged oligo(para-phenylenevinylene) (COPV2-G1) formed within the THF-rich phase. Extended electron irradiation led to morphological changes and shrinkage, suggesting THF was incorporated into COPV2-G1 aggregates along with THF decomposition induced by the electron beam. These findings underscore critical considerations in cryo-FIB-SEM imaging of binary organic liquids and solvated particles, providing practical insights for reducing or leveraging ion/electron beam-induced artifacts.

Isotope microscopic imaging and atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-MALDI-MSI) provide powerful, complementary approaches for visualizing metabolic dynamics in biological tissues. This study applied these techniques to termite workers fed with 13C-labeled cellulose for one week. Termites are classified as eusocial insects because of their colonies' clear division of labor. The two primary castes in their life cycle are reproductive (king and queen), responsible for reproduction, and non-reproductive (workers and soldiers), who handle tasks such as defense, brood care, and foraging. Although various techniques have been developed to detect 13C-labeled biomolecules in samples, it remains unclear whether the iMScopeTM prototype can visualize these molecules with high spatial resolution. Advanced isotope microscopic imaging technique with high spatial resolution (200-300 nm) offered ultra-high-resolution visualization of the relative abundance of the 13C/12C distribution, suggesting precise localization of isotope enrichment in the abdomen. AP-MALDI-MSI performed in the iMScopeTM prototype enabled spatial mapping of 13C-labeled and unlabeled metabolites, such as acetyl-L-carnitine (ALC) and phosphatidylethanolamine (PE), by detecting characteristic mass shifts due to 13C incorporation. The accumulation of PE in the termite abdomen represents an adaptive strategy to optimize nutrient allocation and promote social cohesion, thereby highlighting its potential role in maintaining colony fitness. Our study shows that the iMScopeTM prototype is a novel AP-MALDI-MSI technique to detect 13C-integrated metabolites in the 13C-labeled sample. This study also demonstrated that this technique can detect 13C-integrated PE, which is abundant mainly in termite abdomen.

Bone dynamically changes its shape and structure in response to extra-tissue environments, so that bone morphometry has been a substantial method to evaluate pathophysiology of bone. Osteocytes embedded in mineralized bone matrix play key roles in systemic bone metabolism and characterize distinct bone sites. The jawbone has been described as a unique bone in the context of vertebrate evolution and function. Bone loss in the mandibular bone is less obvious in osteoporotic conditions than in other bones, such as vertebral and limb long bones, both in animal models and in clinical studies. Since osteocyte lacunae are complex and small (-10µm in length) in shape and size, respectively, comprehensive and unbiased morphometrical analysis of changes in the size of osteocyte lacunae was still an obstacle. This study established an artificial intelligence-driven morphometry with wide-field microscopy-based imaging of osteocyte lacunae. Successive comparative analyses demonstrated active perilacunar bone remodeling in the mandibular bone than in the parietal bone. This approach enabled us to statistically compare morphometric parameters in a more comprehensive and unbiased manner. We further discuss the possible unique contribution of the mandibular bone to the pathophysiology of osteoporosis. This study established an artificial intelligence-driven morphometry with wide-field microscopy-based imaging of osteocyte lacunae. Successive comparative analyses demonstrated active perilacunar bone remodeling in the mandibular bone than in the parietal bone. This approach enabled us to statistically compare morphometric parameters in a more comprehensive and unbiased manner.

Atomic force microscopy (AFM) has developed remarkably in recent years, and its measurement environment has been extended not only to ultrahigh vacuum and air, but also to liquids. Since the solid-liquid interface is the site of various reactions, such as crystal growth and catalytic reactions, its atomic-scale analysis is crucially important. Although AFM analyses in various liquids, such as aqueous solutions, organic solvents, and ionic liquids, have been reported, there have been no studies of AFM analysis in molten metals. One of the reasons for this is the opacity of molten metals. Achieving AFM analysis in molten metal is expected to provide new insights into metallurgy. In this review, AFM that can analyze in molten metal is presented. The key innovation is the utilization of an AFM sensor employing a quartz tuning fork, the so-called qPlus sensor, instead of a silicon cantilever. In addition to the technical fundamentals of AFM in molten metal, we present two applications: in-situ and atomic-resolution analysis of alloy crystal growth processes and measurements of two-body interaction forces.

Dynamics in liquids and glasses can be assessed using X-ray photon correlation spectroscopy or electron correlation microscopy, which involves measuring the temporal changes in diffraction patterns. Two methods are commonly used to evaluate these temporal changes: one-time correlation function or two-time correlation function. However, the specific characteristics of these methods have not been thoroughly studied. In this study, we investigated the differences between these methods and found that the two-time correlation function can measure dynamics for longer periods than the method relying on the one-time correlation function. Additionally, we demonstrated that the two-time correlation function exhibits a weak dependence on the amount of dose applied.

Knife-mark noise often arises in microscopy of materials. Leveraging their simple textures relative to natural images, we simulate knife-marked micrographs and train a deep network without labeled real data. The resulting model surpasses conventional methods, removing artifacts while preserving structure, demonstrating simulation-driven learning as a practical materials-science solution in research. Accurate quantitative analysis of material microstructures from images is often hindered by noise and artifacts generated during sample preparation. While deep learning is a promising approach for this challenge, preparing the large amount of "supervised data" (labeled real images) required for training poses a significant barrier in material science. This study proposes and validates a simulation-driven learning paradigm where a deep learning model is trained exclusively on simulated images that mimic the key features of target structures and noise, serving as a powerful solution to this data scarcity problem. As a specific case study, we applied this paradigm to the removal of "knife-mark noise" from cross-sectional images of rubber materials to enable accurate filler region segmentation. In evaluations using simulated data, the proposed method showed superior performance across all the metrics (PSNR, SSIM, and MAE) compared with conventional methods such as the median filter and TV reconstruction, as well as a U-Net model trained on general-purpose Gaussian noise. More importantly, the model also performed effectively on real images, despite being trained solely on simulated data. It successfully removed both knife-marks and material-derived background textures, which demonstrates the viability of simulation-driven learning to overcome the need for manually annotated datasets. This work highlights the power of task-specific simulations as a practical alternative to manual data annotation in quantitative materials analysis.

Electron spectroscopy implemented in electron microscopes provides high spatial resolution, down to the atomic scale, of the chemical, electronic, vibrational and optical properties of materials. In this review, we will describe how temporal coincidence experiments in the nanosecond to femtosecond range between different electron spectroscopies involving photons, inelastic electrons and secondary electrons can provide information bits not accessible to independent spectroscopies. In particular, we will focus on nano-optics applications. The instrumental modifications necessary for these experiments are discussed, as well as the perspectives for these coincidence techniques.

Lamellipodia are generally defined as thin, sheet-like cell protrusions that constitute the actin cytoskeleton-based motile apparatus, which promotes the movement of migrating cells. Recently, we identified a novel type of lamellipodia, termed ruffle-edge lamellipodia, which have α-actinin-4 (ACTN4)-enriched multilayer membrane folds at their leading edges in certain invasive cancer cell lines. In this study, the role of unconventional myosin-Ie (Myo1E) in ACTN4-enriched ruffle-edge lamellipodia was analyzed using live-cell, immunofluorescence, and scanning electron microscopy. Immunofluorescence microscopy for endogenous Myo1E and live-cell imaging of mApple-Myo1E expressing cells showed that Myo1E was localized to ACTN4-rich lamellipodia tips in A549 cells. The wound healing assay and live-cell movies showed that Myo1E siRNA knockdown significantly suppressed cell migration and ruffle-edge lamellipodia formation. Furthermore, scanning electron microscopy demonstrated that Myo1E knockdown significantly reduced ruffle-edge structures. These results suggest that Myo1E may play an important role not only in the motility of ruffle-edge lamellipodia but also in the construction of ruffle-edge structures, which are probably associated with cancer cell invasion and metastasis.

Ion gradients and membrane potential are fundamental to bacterial physiology, driving essential processes such as ATP synthesis, nutrient uptake, motility, and stress adaptation. Visualizing these ion dynamics has become increasingly feasible through the use of fluorescent probes. This review provides a comprehensive overview of both synthetic dyes and genetically encoded indicators developed or adapted for bacterial systems. This review describe the principles underlying ion detection, highlight representative fluorescent probe tools, and assess their application in monitoring cytoplasmic ions and membrane potential in living bacterial cells. Specific challenges in bacterial imaging, such as cell size, membrane permeability, dye efflux, and signal quantification, are discussed alongside recent advances in probe design and imaging platforms. This review aims to guide future research by outlining current capabilities, identifying limitations, and suggesting opportunities for innovation in bacterial ion imaging.

Scanning electron microscope (SEM) observation in low vacuum can overcome the issue of charge-up at the specimen surface, allowing for the observation of insulating samples without sample pretreatment. The ultra-variable-pressure detector (UVD) was developed as a secondary electron (SE) detector for the low vacuum observation in SEM. It works by collecting the light signal released from the collision between secondary electrons and gas molecules. In this study, we propose a simple method using a stainless steel (SUS) sphere to characterize the feature of UVD signal in low vacuum SEM, and compare it with the traditional Everhart-Thornley (E-T) detector in normal SEM. The UVD signal showed characteristic features, namely a two-round-peak feature in the profile, which is different from that of E-T detector. By experiment and simulation, we revealed that at higher vacuum levels (as a few Pa), SEs provide the primary contribution to the UVD signal, exhibiting a profile similar to that of the E-T signal. As the vacuum deteriorates, as 30 Pa, the main contribution to the UVD signal shifts from SEs to low energy backscattered electrons (BSE). Our finding indicates that by tuning the chamber pressure, we can vary the UVD image between SE and low energy BSE features.

Core-loss spectroscopies using electrons and X-rays, such as electron energy loss near-edge structures (ELNES) and X-ray absorption near-edge structures (XANES), are indispensable tools for materials characterization and development. These techniques provide detailed insights into atomic environments, chemical bonding, and vibrational properties that underpin material functionality. Traditionally, ELNES/XANES analyses have relied on qualitative interpretation or comparisons with reference spectra obtained from experiments and/or simulations. Recent advances in data-driven approaches, however, have enabled more quantitative and predictive use of these spectra. This review highlights newly developed data-driven methodologies that extend far beyond conventional ELNES/XANES analysis. These approaches accelerate ELNES/XANES simulations, enable the extraction of radial distribution functions, and quantify multiple material properties directly from spectral data. To enhance the interpretability of machine learning (ML) predictions, sensitivity analysis is employed to elucidate the relationships between specific spectral features and target properties. The rapid growth of open materials databases, coupled with increasingly powerful ML models, has further fueled these developments. Together, these advances would point to a future in which automated, interpretable and scalable spectroscopy serves as a central driver for deeper understandings and accelerated materials discovery.

Using an electron microscope, thick (30-100 nm wide), linear (not branched), cross-striated protein fibrils with an axial repeat of about 65 nm were detected in mammalian cell nuclei. These fibrils differ from the thin filaments of the nuclear matrix described in the literature. Therefore, in this work, the main efforts were aimed at demonstrating the nuclear origin of thick fibrils. Their presence in the material of nuclei destroyed by ultrasound, their contact with isolated nucleoli, and their presence in residual nuclei (nuclear matrix) are shown. Contacts of thick fibrils with both chromatin and the network of filaments of the nuclear matrix were observed. Thick fibrils, which are axial components of condensed chromosomes, are preserved during mitosis. It is likely that their contacts with chromatin and elements of the nuclear matrix are also preserved, ensuring the reproduction of the internal structure of the nuclei in daughter cells. Thick fibrils disintegrate in a medium with low ionic strength. Perhaps this is the reason for their absence in other authors' nuclear matrix preparations. In this work, the nuclei were isolated, and all experiments were carried out in a "complete medium "simulating the intranuclear salt content. In the cell nuclei, thick (30-100 nm in diameter) linear cross-striated (axial repeat approximately 65 nm) fibrils were detected and described using an electron microscope. They are presumably components of the nuclear matrix.

Scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) has emerged as a state-of-the-art characterization modality in materials science, undergoing transformative advancements over the past decade. Revolutionary developments in monochromator technology have pushed EELS energy resolution into the sub-10 meV regime, enabling investigations of low-energy excitations such as phonons, excitons, plasmons, and polaritons at nanometer and sub-nanometer scales, in addition to traditional core-loss spectroscopy. Besides to the high spatial resolution and high energy resolution, the coherent nature of STEM electron probes now allows momentum-resolved spectral information to be acquired, providing an ideal platform for correlating nanoscale structural features with functional properties at the nanometer and atomic level. This review surveys recent breakthroughs in STEM-EELS methodology, with particular emphasis on the four-dimensional electron energy loss spectroscopy (4D-EELS) technique, which simultaneously captures spectral information across spatial, momentum, and energy dimensions with unprecedented efficiency. We highlight landmark scientific discoveries enabled by this spontaneous spatial-momentum resolving capability, including phonon dispersion mapping, plasmon dispersion mapping, and magnon mapping. The review concludes with perspectives on future technical refinements, such as resolution enhancements, machine learning-driven data analytics, and in-situ characterization capabilities, and the potential of this technology to revolutionize interdisciplinary research in quantum materials and nanophotonics. This review methodically investigates recent breakthroughs in low-loss excitation studies using STEM-EELS with a primary focus on phonon dynamics. Furthermore, we introduce the recently developed 4D-EELS Technique adopting parallel acquisition of spectral information across spatial, momentum, and energy dimensions.

Si-photo diode (Si-PD) is commonly used for the backscattered electron (BSE) detector in scanning electron microscope (SEM). However, it is difficult to detect low-energy electrons below 3 kV. We have developed a thin microchannel plate (MCP) chip with an energy filter grid as an alternative BSE detector for low-energy SEM observations. The MCP can get enough signals even at 1 keV electron beam operation. The energy filtering operation revealed that the MCP image is composed of SE and BSE signals. By filtering SE component, the low-energy BSE images are easily obtained, which will open-up the new observation method of SEM using low-BSE image. A microchannel plate (MCP) detector with an energy filter grid has been developed for low-energy BSE detection in SEM. This detector can detect low energy BSEs from 50 eV to 1 keV. It can also operate at low energy electron beam operation like 1 keV.

Crystal defects are intrinsically linked to the electrical and optical properties of semiconductor materials, making their nanoscale detection essential across all phases (from research and development to manufacturing). Electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM) has emerged as a promising technique for detecting even point defects due to the shape modulation in valence-loss spectra induced by defects. However, previous studies have primarily focused on qualitative detection, leaving the detection limit, ie, the minimum detectable concentration, insufficiently explored. To experimentally evaluate the detection limit of defects and clarify the application scope of valence EELS, we prepared GaN samples with controlled defect concentrations along the depth direction using multi-step He-ion implantation and acquired valence-loss spectra at each depth. Based on the simulated depth profile of defects, we evaluated the detection limit from the depth at which significant modulation in the spectral shape was observed. The detection limit fundamentally depends on the signal-to-noise ratio of the valence-loss spectra. Under typical STEM conditions with an electron dose of 5 × 105 e-/Å2, the detection limit of defects in GaN was determined to be 0.35% (3500 ppm). Detailed structural analysis revealed that GaN contains implantation-induced defects and their clusters, and exhibits lattice strain and local disorder while retaining its wurtzite structure. The shape modulation in the valence-loss spectra was attributed to the indirect detection of defects through the surrounding strain fields. We investigated the detection limit of defect-induced strain in GaN using valence EELS and correlated structural analysis. The detection limit fundamentally depends on the signal-to-noise ratio of the valence-loss spectra and was determined to be 0.35% (3500 ppm) under a typical STEM electron dose condition. Mini Abstract Figure: Figure 2 and 3.

Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.