The quantitative determination of thin film thickness using X-ray emission analysis provides an accurate, non-destructive alternative to conventional characterisation methods. This study established empirical relationships among the X-ray k-ratio, backscattering electron coefficient (η), and film thickness for two-dimensional (2D) silicon (Si) and silicon dioxide (SiO₂) films, and a silicon dioxide (SiO₂) layer on a Si substrate. Monte Carlo simulations using the McXRayLite to model the electron-solid interactions at primary beam energies of 3-10 keV and film thicknesses of 2-10 nm. The Si Kα₁ signal intensity was used to compute the k-ratios for unsupported Si and SiO₂ films, whereas both Si Kα₁ and O₂ Kα₁ intensities were used for SiO₂/Si. For the Si Kα₁ signal used, a strong linear correlation was obtained between the k-ratio and the thickness of the 2D Si and SiO₂ films, where an inverse linear relationship was observed for the SiO₂/Si sample. For the O₂ Kα₁ signal used, a strong linear correlation between the k-ratio and top-film SiO2 thickness on the Si substrate was observed. The k-ratio also exhibited a consistent dependence on the backscattering coefficient (η), confirming the influence of backscattered electrons on X-ray generation. Empirical equations were derived to predict the film thickness with uncertainties below 4%, demonstrating that the k-ratio method is a reliable and accurate tool for nanoscale thin film measurements.

Reliable sample temperature measurements are essential in environmental transmission electron microscope (ETEM) experiments. In this study, the effect of flowing gases on the temperature distribution at a MEMS microheater in gas phase is investigated. A computational fluid dynamics model is developed and compared with experimental data. The modeling results agree well with experimental measurements based on the melting temperature of Zn nanoparticles, confirming the model's reliability. The results show that the temperature profile across the heating chip in the case with H2 environment is less uniform compared to the case of vacuum and O2. For example, at a set temperature of 900 °C in 3 mbar H2, a temperature difference of 60 °C is observed between the central sample position compared to the surrounding arc-shaped heater which also is the temperature sensor, while the difference in the vacuum case is only 13 °C. Temperature is one of the key parameters in in-situ TEM experiments and, therefore, these findings are important in the design of ETEM experiments, especially when using MEMS microheaters with relatively large distances between TEM sample and microheater/sensor.

We demonstrate a multi-beam scanning transmission electron microscopy (STEM) imaging that integrates down-sampling with super-resolution image reconstruction via a compressive sensing framework. A custom condenser aperture with six randomly positioned circular holes is employed to produce a multi-beam STEM probe, with the beam shape and distribution tuned through defocus. While the raw multi-beam images exhibit overlapping patterns, reconstruction using Adam optimization and total variation normalization yields high-fidelity images that closely reproduce the original sample structures, even from substantially down-sampled data. The proposed approach offers a pathway toward significant acceleration of such techniques through multibeam sparse sampling and computational reconstruction potentially useful for the analytical scanning methods in general.