Year: 2025
Type: article
Abstract: A successful scanning tunnelling microscopy (STM) experiment relies on both delicate sample preparation and measurement, and careful image filtering and analysis to provide clear and solid results. Processing and analysis of STM images may result in a tricky task, due to the complexity and specificity of the probed systems. In this paper, we introduce our recently developed, simple Python-based methods for filtering and analysing STM images, with the aim of providing a semi-quantitative treatment of the input data. Case studies will be presented using images obtained through electrochemical STM. Additionally, we propose a straightforward yet effective universal drift-correction tool for SPM image sequences.
Source: Journal of Microscopy
DOI: 10.1111/jmi.13426
Institutions: University of Padua, Technical University of Munich
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,760
OpenAlex ID: W4410376706