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Updated: May 6, 2026

Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders

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Nanoparticle characterisation via 2D classification using single particle averaging.

Iain Harley¹, Anke Kaltbeitzel¹, Francesca Mazzotta¹

¹Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany. lieberw@mpip-mainz.mpg.de.

Nanoscale Horizons

|June 2, 2025

Summary

This summary is machine-generated.

This study introduces 2D class averaging (2D-CA) for nanoparticle (NP) characterization, offering accurate size distribution analysis for complex NP systems. This novel approach overcomes limitations of traditional methods, even for challenging samples like agglomerated NPs.

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Area of Science:

Nanotechnology
Materials Science
Biophysics

Background:

Characterizing nanoparticle (NP) size and morphology, particularly in complex structures like core-shell particles and nanocapsules, is challenging with conventional methods.
Existing techniques often face limitations in resolution and applicability to diverse NP systems.

Purpose of the Study:

To introduce and validate a novel image-based nanoparticle characterization method using 2D class averaging (2D-CA).
To demonstrate the applicability of 2D-CA for analyzing diverse and challenging nanoparticle systems.

Main Methods:

Utilized 2D class averaging (2D-CA) techniques from single particle analysis, adapted from structural biology software.
Applied the method to various nanoparticle systems including nanocapsules, nanorods, and bimodal distributions.
Validated the technique through comparative studies against established characterization methods.

Main Results:

2D-CA provides detailed size distribution analysis for diverse nanoparticle systems.
The method effectively characterizes challenging samples, including highly agglomerated nanoparticles, inaccessible to conventional techniques.
Demonstrated statistically robust results with minimal human bias via automated particle identification.

Conclusions:

2D-CA offers a powerful and accurate approach for nanoparticle size and morphology characterization.
This method enhances the analysis of complex and challenging nanoparticle systems.
Leveraging established software streamlines the workflow for processing large numbers of micrographs.