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Maximilian Töllner

Publications and source records attributed to Maximilian Töllner.

3 recordsLinked to original sources

Accurate Nanoscale Mapping of Electric Fields across Random Grain Boundaries in Polycrystalline Oxides Using Precession-Assisted 4D-STEM

Space charge layers (SCLs) at grain boundaries play a crucial role in modulating local electric fields and influencing the functional properties of materials, such as oxygen vacancy migration and ionic conductivity in oxide ceramics. However, the direct experimental analysis of such localized electric fields and the corresponding charge distribution remains challenging. Conventional center-of-mass (CoM) analysis in scanning transmission electron microscopy differential phase contrast (STEM-DPC) is strongly affected by orientation-dependent contrast and dynamical scattering. Here, we demonstrate that combining electron beam precession with advanced post-processing, employing iterative edge detection via a Sobel filter and singular value decomposition (SVD), enables reliable and accurate, unbiased diffraction shift measurements with minimal crystallographic artefacts. The new method accurately refines the central disk position in nanobeam electron diffraction (NBED) patterns and thus significantly improves the extraction of the local electric field and corresponding charge distribution. Comparative analysis with conventional CoM methods shows superior accuracy and robustness for random grain boundaries in BaTiO3 and SrTiO3 as exemplary case studies. The experimental work is complemented by atomistic simulations to separate the electric field of the SCL from the mean inner potential difference of the grain boundary and the elemental segregation around the grain boundary. The in-depth analysis shows that our approach enables high-fidelity mapping of electromagnetic fields and their charge distribution in complex polycrystalline specimens, laying the groundwork for improved quantitative analysis using STEM-DPC.

cond-mat.mtrl-sci↗

FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data

We introduce FlowTIE, a neural-network-based framework for phase reconstruction from 4D-Scanning Transmission Electron Microscopy (STEM) data, which integrates the Transport of Intensity Equation (TIE) with a flow-based representation of the phase gradient. This formulation allows the model to bridge data-driven learning with physics-based priors, improving robustness under dynamical scattering conditions for thick specimen. The validation on simulated datasets of crystalline materials, benchmarking to classical TIE and gradient-based optimization methods are presented. The results demonstrate that FlowTIE improves phase reconstruction accuracy, fast, and can be integrated with a thick specimen model, namely multislice method.

cs.LG↗

Simultaneous mapping of magnetic and atomic structure for direct visualization of nanoscale magnetoelastic coupling

Achieving a correlative measurement of both magnetic and atomic structures at the nanoscale is imperative to understand the fundamental magnetism of matters and for fostering the development of new magnetic nanomaterials. Conventional microscopy methods fall short in providing the two information simultaneously. Here, we develop a new approach to simultaneously map the magnetic field and atomic structure at the nanoscale using Lorentz 4-dimensional scanning transmission electron microscopy (Ltz-4D-STEM). This method enables precise measurement of the characteristic atomic and magnetic structures across an extensive field of view, a critical aspect for investigating real-world ferromagnetic materials. It offers a comprehensive visualization and statistical evaluation of the different structural information at a pixel-by-pixel correlation. The new method allows to directly visualize the magnetoelastic coupling and the resulting complex magnetization arrangement as well as the competition between magnetoelastic and magnetostatic energy. This approach opens new avenues for in-depth studying the structure-property correlation of nanoscale magnetic materials.

cond-mat.mtrl-sci↗