Precise Nanoscale Mapping of Electric Fields Across Random Grain Boundaries in Polycrystalline Oxides Using Precession-Assisted 4D-STEM.

Authors

Sangjun Kang, Hyeyoung Cho, Maximilian Töllner, Anna Rose Nelson, Ziming Ding, Xiaoke Mu, Di Wang, Wolfgang Rheinheimer, Kai Wang, Bai-Xiang Xu, Jakob Konstantin Laux, Mahmoud Serour, Karsten Albe, Andreas Klein, Christian Kübel

Published in

Small (Weinheim an der Bergstrasse, Germany). Pages e75940. Sep 26, 2026. Epub Sep 26, 2026.

Abstract

Space charge layers (SCLs) at grain boundaries play a crucial role in modulating electric fields and thereby influence functional properties of materials. However, experimental analysis of these 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 and singular value decomposition (SVD), enables reliable, 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. Comparison 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 for improved quantitative analysis.

PMID:
42798278
Bibliographic data and abstract were imported from PubMed on 26 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.