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Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy.

Abhilash Chandrashekar¹, Pierpaolo Belardinelli², Miguel A Bessa³

¹Precision and Microsystems Engineering, TU Delft Delft The Netherlands a.chandrashekar@tudelft.nl f.alijani@tudelft.nl.

Nanoscale Advances

|May 23, 2022

Summary

This summary is machine-generated.

Machine learning quantifies nanoscale forces in dynamic atomic force microscopy (AFM) without complex models. This approach analyzes experimental data for polymer characterization, revealing insights into elasticity and energy dissipation.

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Area of Science:

Materials Science
Nanotechnology
Data Science

Background:

Dynamic atomic force microscopy (AFM) is crucial for material characterization.
Quantifying nanoscale tip-sample forces typically requires complex physical models.
A challenge exists in analyzing these forces directly from experimental data.

Purpose of the Study:

To develop a machine learning approach for quantifying nanoscale tip-sample forces in dynamic AFM.
To characterize forces purely from experimental data, bypassing complex modeling.
To demonstrate the method's application on polymer blends.

Main Methods:

Utilized machine learning and data science algorithms.
Trained the machine learning model on standard AFM models.
Applied the trained algorithm to experimental data from polystyrene (PS) and low-density polyethylene (LDPE) blends.

Main Results:

Successfully characterized tip-sample forces with sub-microsecond resolution.
Probed the complex physics of tip-sample contact in polymers.
Estimated elasticity and provided insights into energy dissipation during contact.

Conclusions:

Machine learning offers a novel route for dynamic AFM force characterization.
This method enables real-time analysis of transient phenomena.
Potential applications include studying phase transformations and biological processes.