Eliška Mikmeková

Two-dimensional (2D) materials exhibit electronic and collective excitation properties that are highly sensitive to surface chemistry and thickness, requiring surface-sensitive characterization at low electron energies. Here, we investigate graphene, hexagonal boron nitride (h-BN), molybdenum disulfide (MoS₂), and titanium carbide (Ti₃C₂) MXene using an advanced home-built scanning low-energy electron microscopy system combined with time-of-flight electron spectroscopy (SLEEM/ToF). The system uniquely records electron energy-loss spectra (EELS) from transmitted electrons rather than from the reflected electrons used in conventional SLEEM. Compared with high-energy EELS, our low-energy ToF-EELS approach offers enhanced surface sensitivity and reduced beam-induced damage, enabling direct probing of π and π + σ plasmon excitations. Additionally, complementary techniques, including scanning transmission electron microscopy (STEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), were employed to characterize structural and chemical properties. EELS were acquired for all investigated 2D materials at electron landing energies of 500–1500 eV, and in the 5–50 eV range for selected materials, including graphene and MoS₂. Analysis of these spectra enabled determination of the average plasmon positions across the measured energy range for all studied materials. Furthermore, a quantitative determination of the inelastic mean free path (IMFP) was achieved for graphene in the 10–50 eV range, yielding a value of 1.9 ± 0.2 nm. These results demonstrate the potential of SLEEM–ToF for surface-sensitive analysis of 2D materials.

The success of machine learning (ML) models in object or pattern recognition naturally leads to ML being employed in the classification of the microstructure of steel surfaces. Light optical microscopy (LOM) is the traditional imaging process in this field. However, the increasing use of ML to extract or relate more aspects of the aforementioned materials and the limitations of LOM motivated us to provide an improvement to the established image acquisition process. In essence, we perform style transfer from LOM to scanning electron microscopy (SEM) combined with “intelligent” upscaling. This is achieved by employing an ML model trained on a multimodal dataset to generate an SEM-like image from the corresponding LOM image. This transformation, in our opinion, which is corroborated by a detailed analysis of the source, target and prediction, successfully pushes the limits of LOM in the case of steel surfaces. The expected consequence is the improvement of the precise characterization of advanced multiphase steels’ structure based on these transformed LOM images.

The segmented semiconductor detectors for transmitted electrons in ultrahigh resolution scanning electron microscopes allow observing samples in various imaging modes. Typically, two standard modes of objective lens, with and without a magnetic field, differ by their resolution. If the beam deceleration mode is selected, then an electrostatic field around the sample is added. The trajectories of transmitted electrons are influenced by the fields below the sample. The goal of this paper is a quantification of measured images and theoretical study of the capability of the detector to collect signal electrons by its individual segments. Comparison of measured and ray-traced simulated data were difficult in the past. This motivated us to present a new method that enables better comparison of the two datasets at the cost of additional measurements, so-called calibration curves. Furthermore, we also analyze the measurements acquired using 2D pixel array detector (PAD) that provide a more detailed angular profile. We demonstrate that the radial profiles of STEM and/or 2D-PAD data are sensitive to material composition. Moreover, scattering processes are affected by thickness of the sample as well. Hence, comparing the two experimental and simulation data can help to estimate composition or the thickness of the sample.

The detailed examination of electron scattering in solids is of crucial importance for the theory of solid-state physics, as well as for the development and diagnostics of novel materials, particularly those for micro- and nanoelectronics. Among others, an important parameter of electron scattering is the inelastic mean free path (IMFP) of electrons both in bulk materials and in thin films, including 2D crystals. The amount of IMFP data available is still not sufficient, especially for very slow electrons and for 2D crystals. This situation motivated the present study, which summarizes pilot experiments for graphene on a new device intended to acquire electron energy-loss spectra (EELS) for low landing energies. Thanks to its unique properties, such as electrical conductivity and transparency, graphene is an ideal candidate for study at very low energies in the transmission mode of an electron microscope. The EELS are acquired by means of the very low-energy electron microspectroscopy of 2D crystals, using a dedicated ultra-high vacuum scanning low-energy electron microscope equipped with a time-of-flight (ToF) velocity analyzer. In order to verify our pilot results, we also simulate the EELS by means of density functional theory (DFT) and the many-body perturbation theory. Additional DFT calculations, providing both the total density of states and the band structure, illustrate the graphene loss features. We utilize the experimental EELS data to derive IMFP values using the so-called log-ratio method.

A mesoporous TiO_2−x material comprised of small, crystalline, vacancy‐rich anatase nanoparticles (NPs) shows unique optical, thermal, and electronic properties. It is synthesized using polymer‐derived mesoporous carbon (PDMC) as a template. The PDMC pores serve as physical barriers during the condensation and pyrolysis of a titania precursor, preventing the titania NPs from growing beyond 10 nm in size. Unlike most titania nanomaterials, during pyrolysis the NPs undergo no transition from the anatase to rutile phase and they become catalytically active reduced TiO_2−x. When exposed to a slow electron beam, the NPs exhibit a charge/discharge behavior, lighting up and fading away for an average period of 15 s for an extended period of time. The NPs also show a 50 nm red‐shift in their UV/Vis absorption and long‐lived charge carriers (electrons and holes) at room temperature in the dark, even long after UV irradiation. The NPs as photocatalysts show a good activity for CO₂ reduction.

A mesoporous TiO_2−x material comprised of small, crystalline, vacancy‐rich anatase nanoparticles (NPs) shows unique optical, thermal, and electronic properties. It is synthesized using polymer‐derived mesoporous carbon (PDMC) as a template. The PDMC pores serve as physical barriers during the condensation and pyrolysis of a titania precursor, preventing the titania NPs from growing beyond 10 nm in size. Unlike most titania nanomaterials, during pyrolysis the NPs undergo no transition from the anatase to rutile phase and they become catalytically active reduced TiO_2−x. When exposed to a slow electron beam, the NPs exhibit a charge/discharge behavior, lighting up and fading away for an average period of 15 s for an extended period of time. The NPs also show a 50 nm red‐shift in their UV/Vis absorption and long‐lived charge carriers (electrons and holes) at room temperature in the dark, even long after UV irradiation. The NPs as photocatalysts show a good activity for CO₂ reduction.