A new automatic contact point detection algorithm for AFM force curves

Area of Science:

• Biophysics
• Materials Science
• Nanotechnology

Background:

• Atomic Force Microscopy (AFM) is crucial for nanoscale imaging and material characterization.
• Accurate determination of the contact point in AFM force curves is essential for reliable data analysis.
• Existing methods for contact point estimation have limitations, especially for biological samples.

Purpose of the Study:

• To present a novel, easily implementable algorithm for estimating contact points in AFM force curves.
• To enable fully automatic detection of contact points, particularly for force curves on living cells.
• To validate the reliability and efficiency of the proposed algorithm.

Main Methods:

• Development of a new contact point estimation method based on a local regression algorithm.
• Application of the algorithm for automatic detection of contact points in approach force curves.
• Comparison of the algorithm's results with established methods using statistical analysis.
• Testing the algorithm's suitability for batch processing of large datasets, including force curve maps.

Main Results:

• The new algorithm provides reliable contact point estimations.
• No statistically significant differences were observed between the proposed method and existing techniques.
• The algorithm is suitable for automated analysis of force curves from living cells.
• The method efficiently handles batch processing, as demonstrated on a 625-curve map.

Conclusions:

• The local regression-based algorithm offers a robust and automated solution for contact point estimation in AFM.
• This method enhances the reliability and efficiency of AFM data analysis, especially for biological applications.
• The algorithm's ease of implementation and batch-processing capability make it a valuable tool for researchers.