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Picometer-Precision Atomic Position Tracking through Electron Microscopy

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Simple Python-based methods for analysis and drift-correction of STM images.

Francesco Cazzadori¹, Alessandro Facchin², Silvio Reginato¹

¹Department of Chemical Sciences, University of Padova, Padova, Italy.

Journal of Microscopy

|May 14, 2025

Summary

We present simple Python methods for analyzing scanning tunneling microscopy (STM) images, enabling semi-quantitative data treatment. A universal drift-correction tool for scanning probe microscopy (SPM) images is also introduced.

Keywords:

EC‐STM Python drift high throughput image analysis porphyrin

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Area of Science:

Surface science
Materials science
Nanotechnology

Background:

Scanning Tunneling Microscopy (STM) is crucial for nanoscale imaging.
Analyzing STM data can be complex due to system specificity.
Effective image processing is vital for reliable experimental results.

Purpose of the Study:

To introduce novel, user-friendly Python-based methods for STM image analysis.
To enable semi-quantitative analysis of STM data.
To provide a universal drift-correction tool for Scanning Probe Microscopy (SPM) image sequences.

Main Methods:

Development of simple Python scripts for STM image filtering and analysis.
Application of methods to electrochemical STM data.
Implementation of a universal drift-correction algorithm for SPM images.

Main Results:

Demonstrated semi-quantitative analysis of STM images.
Presented case studies using electrochemical STM data.
Validated a straightforward and effective drift-correction tool for SPM image sequences.

Conclusions:

The developed Python methods simplify STM image analysis.
The tools facilitate semi-quantitative interpretation of experimental data.
The drift-correction tool enhances the reliability of SPM image sequences.