Atomic-resolution scanning transmission electron microscopy (STEM) characterization requires precise tilting of the specimen to a high symmetric zone axis, which is usually processed in reciprocal space by following the diffraction patterns. However, for small-sized nanocrystalline materials, their diffraction patterns are often too faint to guide the tilting process. Here, a simple and effective tilting method is developed based on the diffraction contrast change of the shadow image in the Ronchigram. The misorientation angle of the specimen can be calculated and tilted to the zone axis based on the position of the shadow image with lowest intensity. This method requires no prior knowledge of the sample and the maximum misorientation angle that can be corrected is >±6.9° with sub-mrad accuracy. It operates in real space, without recording the diffraction patterns of the specimens, making it particularly effective for nanocrystalline materials. Combined with the scripting to control the microscope, the sample can be automatically tilted to the zone axis under low dose conditions (<0.17 e- Å- 2 s-1), facilitating the imaging of beam sensitive materials such as zeolites or metal-organic frameworks. This automated tilting method can significantly contribute to the atomic-scale characterization of the nanocrystalline materials by STEM imaging.

Recent advances in liquid phase scanning transmission electron microscopy (LP-STEM) have enabled the study of dynamic biological processes at nanometer resolutions, paving the way for live-cell imaging using electron microscopy. However, this technique is often hampered by the inherent thickness of whole cell samples and damage from electron beam irradiation. These restrictions degrade image quality and resolution, impeding biological interpretation. Using graphene encapsulation, scanning transmission electron microscopy (STEM), and energy-dispersive X-ray (EDX) spectroscopy to mitigate these issues provides unprecedented levels of intracellular detail in aqueous specimens. This study demonstrates the potential of LP-STEM to examine and identify internal cellular structures in thick biological samples. Specifically, it highlights the use of LP-STEM to investigate the radiation resistant, gram-positive bacterium, Deinococcus radiodurans using various imaging techniques.

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modelled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised.

Ferroelectric domain walls are a rich source of emergent electronic properties and unusual polar order. Recent studies show that the configuration of ferroelectric walls can go well beyond the conventional Ising-type structure. Néel-, Bloch-, and vortex-like polar patterns have been observed, displaying strong similarities with the spin textures at magnetic domain walls. Here, the discovery of antiferroelectric domain walls in the uniaxial ferroelectric Pb5Ge3O11 is reported. Highly mobile domain walls with an alternating displacement of Pb atoms are resolved, resulting in a cyclic 180° flip of dipole direction within the wall. Density functional theory calculations show that Pb5Ge3O11 is hyperferroelectric, allowing the system to overcome the depolarization fields that usually suppress the antiparallel ordering of dipoles along the longitudinal direction. Interestingly, the antiferroelectric walls observed under the electron beam are energetically more costly than basic head-to-head or tail-to-tail walls. The results suggest a new type of excited domain-wall state, expanding previous studies on ferroelectric domain walls into the realm of antiferroic phenomena.

Defect engineering is widely used to impart the desired functionalities on materials. Despite the widespread application of atomic-resolution scanning transmission electron microscopy (STEM), traditional methods for defect analysis are highly sensitive to random noise and human bias. While deep learning (DL) presents a viable alternative, it requires extensive amounts of training data with labeled ground truth. Herein, employing cycle generative adversarial networks (CycleGAN) and U-Nets, we propose a method based on a single experimental STEM image to tackle high annotation costs and image noise for defect detection. Not only atomic defects but also oxygen dopants in monolayer MoS2 are visualized. The method can be readily extended to other two-dimensional systems, as the training is based on unit-cell-level images. Therefore, our results outline novel ways to train the model with minimal data sets, offering great opportunities to fully exploit the power of DL in the materials science community.

Structural and chemical characterization of nanomaterials provides important information for understanding their functional properties. Nanomaterials with characteristic structure sizes in the nanometer range can be characterized by scanning transmission electron microscopy (STEM). In conventional STEM, two-dimensional (2D) projection images of the samples are acquired, information about the third dimension is lost. This drawback can be overcome by STEM tomography, where the three-dimensional (3D) structure is reconstructed from a series of projection images acquired using various projection directions. However, 3D measurements are expensive with respect to acquisition and evaluation time. Furthermore, the method is hardly applicable to beam-sensitive materials, i.e. samples that degrade under the electron beam. For this reason, it is desirable to know whether sufficient information on structural and chemical information can be extracted from 2D-projection measurements. In the present work, a comparison between 3D-reconstruction and 2D-projection characterization of structure and mixing in nanoparticle hetero-aggregates is provided. To this end, convolutional neural networks are trained in 2D and 3D to extract particle positions and material types from the simulated or experimental measurement. Results are used to evaluate structure, particle size distributions, hetero-aggregate compositions and mixing of particles quantitatively and to find an answer to the question, whether an expensive 3D characterization is required for this material system for future characterizations.

Metal sub-microparticles (SMPs) and nanoparticles (NPs) presence in food is attributable to increasing pollution from the environment in raw materials and finished products. In the present study, a multifaceted analytical strategy based on Environmental Scanning Electron Microscopy and High-Angle Annular Dark-Field-Scanning Transmission Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (ESEM-EDX, HAADF-STEM-EDX) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was proposed for the detection and characterization of metal and metal-containing SMPs and NPs in durum wheat samples, covering a size measurement range from 1 nm to multiple µm. ESEM-EDX and ICP-MS techniques were applied for the assessment of SMP and NP contamination on the surface of wheat grains collected from seven geographical areas characterized by different natural and anthropic conditions, namely Italy, the USA, Australia, Slovakia, Mexico, Austria, and Russia. ICP-MS showed significant differences among the mean concentration levels of metals, with the USA and Italy having the highest level. ESEM-EDX analysis confirmed ICP-MS concentration measurements and measured the highest presence of particles < 0.8 µm in size in samples from Italy, followed by the USA. Less marked differences were observed when particles < 0.15 µm were considered. HAADF-STEM-EDX was applied to a selected number of samples for a preliminary assessment of internal contamination by metal SMPs and NPs, and to expand the measurable particle size range. The multifaceted approach provided similar results for Fe-containing SMPs and NPs. ICP-MS and ESEM-EDX also highlighted the presence of a significant abundance of Ti- and Al-containing particles, while for STEM-EDX, sample preparation artifacts complicated the interpretation. Finally, HAADF-STEM-EDX results provided relevant information about particles in the low nm range, since, by applying this technique, no particles smaller than 50 nm were observed in accordance with ESEM-EDX.

The metal-insulator (MI) transition of vanadium dioxide (VO2) is effectively modulated by oxygen vacancies, which decrease the transition temperature and insulating resistance. Oxygen vacancies in thin films can be driven by oxygen transport using electrochemical potential. This study delves into the role of crystallographic channels in VO2 in facilitating oxygen transport and the subsequent tuning of electrical properties. A model system is designed with two types of VO2 thin films: (100)- and (001)-oriented, where channels align parallel and perpendicular to the surface, respectively. Growing an oxygen-deficient TiO2 layer on these VO2 films prompted oxygen transport from VO2 to TiO2. Notably, in (001)-VO2 film, where oxygen ions move along the open channels, the oxygen migration deepens the depleted region beyond that in (100)-VO2, leading to more pronounced changes in metal-insulator transition behaviors. The findings emphasize the importance of understanding the intrinsic crystal structure, such as channel pathways, in controlling ionic defects and customizing electrical properties for applications.

Given the hierarchical nature of bone and bone interfaces, osseointegration, namely the formation of a direct bone-implant contact, is best evaluated using a multiscale approach. However, a trade-off exists between field of view and spatial resolution, making it challenging to image large volumes with high resolution. In this study, we combine established electron microscopy techniques to probe bone-implant interfaces at the microscale and nanoscale with plasma focused ion beam-scanning electron microscopy (PFIB-SEM) tomography to evaluate osseointegration at the mesoscale. This characterization workflow is demonstrated for bone response to an additively manufactured Ti-6Al-4V implant which combines engineered porosity to facilitate bone ingrowth and surface functionalization via genistein, a phytoestrogen, to counteract bone loss in osteoporosis. SEM demonstrated new bone formation at the implant site, including in the internal implant pores. At the nanoscale, scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy confirmed the gradual nature of the bone-implant interface. By leveraging mesoscale analysis with PFIB-SEM tomography that captures large volumes of bone-implant interface with nearly nanoscale resolution, the presence of mineral ellipsoids varying in size and orientation was revealed. In addition, a well-developed lacuno-canalicular network and mineralization fronts directed both towards the implant and away from it were highlighted.

Sequential infiltration synthesis (SIS), also known as vapor phase infiltration (VPI), is a quickly expanding technique that allows growth of inorganic materials within polymers from vapor phase precursors. With an increasing materials library, which encompasses numerous organometallic precursors and polymer chemistries, and an expanding application space, the importance of understanding the mechanisms that govern SIS growth is ever increasing. In this work, we studied the growth of polycrystalline ZnO clusters and particles in three representative polymers: poly(methyl methacrylate), SU-8, and polymethacrolein using vapor phase diethyl zinc and water. Utilizing two atomic resolution methods, high-resolution scanning transmission electron microscopy and synchrotron X-ray absorption spectroscopy, we probed the evolution of ZnO nanocrystals size and crystallinity level inside the polymers with advancing cycles─from early nucleation and growth after a single cycle, through the formation of nanometric particles within the films, and to the coalescence of the particles upon polymer removal and thermal treatment. Through in situ Fourier transform infrared spectroscopy and microgravimetry, we highlight the important role of water molecules throughout the process and the polymers\' hygroscopic level that leads to the observed differences in growth patterns between the polymers, in terms of particle size, dispersity, and the evolution of crystalline order. These insights expand our understanding of crystalline materials growth within polymers and enable rational design of hybrid materials and polymer-templated inorganic nanostructures.