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Atomic Force Microscopy

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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Updated: Oct 12, 2025

Probing Surface Electrochemical Activity of Nanomaterials using a Hybrid Atomic Force Microscope-Scanning Electrochemical Microscope AFM-SECM

Probing Surface Electrochemical Activity of Nanomaterials using a Hybrid Atomic Force Microscope-Scanning Electrochemical Microscope AFM-SECM

Published on: February 10, 2021

Electrostatic Discovery Atomic Force Microscopy.

Niko Oinonen¹, Chen Xu¹, Benjamin Alldritt¹

¹Department of Applied Physics, Aalto University, 00076 Aalto, Helsinki, Finland.

|November 22, 2021

Summary

This summary is machine-generated.

Electrostatic discovery atomic force microscopy uses machine learning to map electrostatic potential at the atomic scale. This new method provides reliable electrostatic maps for diverse molecular systems with low computational cost.

Keywords:

atomic force microscopy chemical identification electrostatics machine learning tip functionalization

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Area of Science:

Surface Science
Materials Science
Computational Chemistry

Background:

Scanning probe microscopy (SPM) offers high-resolution atomic and electronic structure imaging.
However, reliable electrostatic characterization at the same scale remains a significant challenge for SPM techniques.

Purpose of the Study:

To develop a machine learning-based method for atomic-scale electrostatic characterization using SPM.
To provide immediate and reliable electrostatic potential maps directly from SPM images.

Main Methods:

Electrostatic discovery atomic force microscopy (ED-AFM), a novel machine learning approach.
Utilizes functionalized tips in atomic force microscopy (AFM) to acquire data.
Generates electrostatic potential maps directly from AFM images.

Main Results:

Successfully applied ED-AFM to characterize electrostatic properties of various molecular systems.
Demonstrated good agreement between ED-AFM results and reference simulations.
Achieved reliable atomic-scale electrostatic mapping with minimal computational overhead.

Conclusions:

ED-AFM offers a powerful new tool for atomic-scale electrostatic characterization.
The method is applicable to a wide range of molecular systems.
Provides a computationally efficient alternative for electrostatic mapping.