Simple Python‐based methods for analysis and drift‐correction of STM images.
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| Title: | Simple Python‐based methods for analysis and drift‐correction of STM images. |
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| Authors: | Cazzadori, Francesco1 (AUTHOR), Facchin, Alessandro2 (AUTHOR) alessandro.facchin.1@phd.unipd.it, Reginato, Silvio1 (AUTHOR), Durante, Christian1 (AUTHOR) christian.durante@unipd.it |
| Source: | Journal of Microscopy. Apr2026, Vol. 302 Issue 1, p39-49. 11p. |
| Subjects: | Scanning tunneling microscopy, Python programming language, Image stabilization, Scanning probe microscopy, Signal filtering |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Microscopy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00222720 |
| DOI: | 10.1111/jmi.13426 |
