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A combination of reaction-diffusion models with moving-boundary problems yields a system in which the diffusion (spreading and penetration) and reaction (transformation) evolve the system's state and geometry over time. These systems can be…
The numerical analysis of the diffraction features rendered by transmission electron microscopy (TEM) typically relies either on classical approximations (Monte Carlo simulations) or quantum paraxial tomography (the multislice method and…
Strong multiple scattering of the probe in scanning transmission electron microscopy (STEM) means image simulations are usually required for quantitative interpretation and analysis of elemental maps produced by electron energy-loss…
Accurate multi-slice reconstruction from limited measurement data is crucial to speed up the acquisition process in medical and scientific imaging. However, it remains challenging due to the ill-posed nature of the problem and the high…
The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies…
Modeling artificial scanning electron microscope (SEM) and scanning ion microscope images has recently become important. This is because of the need to provide repeatable images with a priori determined parameters. Modeled artificial images…
Scanning transmission electron microscopy (STEM) has a broad range of applications in materials characterization, including real-space imaging, spectroscopy, and diffraction, at length scales from the micron to sub-{\AA}ngstr\"om. The…
The manuscript considers Scanning Transmission Electron Microscopy (STEM) images and derives transformations needed to correct various distortions occurring during scanning. These transformations form the basis for the correction algorithms…
Diffusion models have shown impressive performance for generative modelling of images. In this paper, we present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that…
We introduce a denoising method for four-dimensional scanning transmission electron microscopy (4D-STEM) that relies on processing local, scan position-independent electron event-sparse data stacks, called event-sparse stack denoising. This…
The evolution of the scanning modules for scanning transmission electron microscopes (STEM) has realized the possibility to generate arbitrary scan pathways, an approach currently explored to improve acquisition speed and to reduce electron…
A real-time image reconstruction method for scanning transmission electron microscopy (STEM) is proposed. With an algorithm requiring only the center of mass (COM) of the diffraction pattern at one probe position at a time, it is able to…
The engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect…
The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are accessible, cascade-based network inference and influence…
Significant progress has been made in spatial resolution using environmental transmission electron microscopes (ETEM), which now enables atomic resolution visualization of structural transformation under variable temperature and gas…
Atomic resolution imaging in transmission electron microscopy (TEM) and scanning TEM (STEM) of light elements in electron-transparent materials has long been a challenge. Biomolecular materials, for example, are rapidly altered when…
Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its…
High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical trade-off between image quality and patient safety. While deep learning methods have shown…
Coherent spin resonance methods such as nuclear magnetic resonance (NMR) and electron spin resonance (ESR) spectroscopy have led to spectrally highly sensitive, non-invasive quantum imaging techniques with groundbreaking applications in…
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). Our method is…