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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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Related Experiment Video

Updated: Jun 23, 2025

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Published on: April 2, 2021

Cell recognition based on features extracted by AFM and parameter optimization classifiers.

Junxi Wang^1,2,3,4, Fan Yang^1,2,3,4, Bowei Wang^1,2,3,4

¹International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China. wangz@cust.edu.cn.

Analytical Methods : Advancing Methods and Applications

|June 26, 2024

Summary

This summary is machine-generated.

This study introduces an automated method using nano-resolution imaging and machine learning to accurately identify cancer cells. This approach aids in precise medical diagnosis and reduces diagnostic errors.

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Last Updated: Jun 23, 2025

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

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Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Area of Science:

Biomedical Engineering
Computational Biology
Nanotechnology

Background:

Intelligent technology is crucial for advancing precision medicine, particularly in cancer diagnosis and prognosis.
Accurate cancer cell identification is vital for patient survival and effective treatment strategies.

Purpose of the Study:

To develop a precise diagnostic method using nano-resolution imaging for identifying cancer cells.
To implement cell feature engineering and machine learning for automated cell analysis.

Main Methods:

Utilized Atomic Force Microscopy (AFM) for nano-resolution cell imaging.
Applied cell feature engineering and machine learning classifiers for image analysis.
Employed a feature ranking method for optimizing feature combinations and understanding cell-type differences.

Main Results:

Achieved high accuracy rates of 90.37% and 92.68% on distinct cell datasets using a Bayesian optimized backpropagation neural network.
Successfully identified cancer cells and abnormal cells through automated analysis.

Conclusions:

The proposed method offers an automated solution for identifying cancer and abnormal cells, reducing the burden on medical and research professionals.
This technology supports precise medical care by decreasing misjudgments and improving diagnostic accuracy.