Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Scanning Electron Microscopy

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Testing the Langer-Bar-on-Miller-Akcasu Equation for the Time Evolution of the Structure Factor during Polymeric Spinodal Decomposition and Dissolution.

Macromolecules·2026

Same author

Structure, Conformations, and Diffusion in PDMS/Silica Nanocomposites via Atomistic MD Simulations.

Macromolecules·2026

Same author

Modeling the time evolution of the structure factor during polymeric spinodal decomposition using dynamic mode decomposition.

The Journal of chemical physics·2025

Same author

Modeling photo-generated charge extraction in bulk heterojunction nanoparticles.

Soft matter·2024

Same author

Thermodynamics of Highly Interacting Blend PCHMA/dPS by TOF-SANS.

Macromolecules·2023

Same author

Correction: Migration of nanoparticles across a polymer-polymer interface: theory and simulation.

Soft matter·2023

Same journal

Steady and oscillatory propulsion in reactive swimming droplets.

Soft matter·2026

Same journal

Axial forces in capillary liquid bridges of polymer solutions.

Soft matter·2026

Same journal

Dual-mode pH-programmable enzymatic hydrogel system for on-demand glucose generation.

Soft matter·2026

Same journal

Loading iron(III)porphyrin as the gas/anion binding site into methylated β-cyclodextrin-incorporated polymer hydrogels.

Soft matter·2026

Same journal

Disorder-induced persistent random motion and trapping of microswimmers.

Soft matter·2026

Same journal

Metal-ion induced coacervation of a short peptide under acidic conditions.

Soft matter·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 17, 2025

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Machine learning real space microstructure characteristics from scattering data.

Matthew Jones¹, Nigel Clarke¹

¹Department of Physics & Astronomy, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK. mpjones1@sheffield.ac.uk.

|October 12, 2021

Summary

This summary is machine-generated.

This study links scattering data to Minkowski functionals for spinodal decomposition microstructures using machine learning. Results show a strong correlation, improving microstructure characterization.

More Related Videos

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

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

Published on: December 4, 2020

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

Related Experiment Videos

Last Updated: Oct 17, 2025

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

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

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

Published on: December 4, 2020

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

Area of Science:

Materials Science
Polymer Science
Computational Science

Background:

Spinodal decomposition is a critical process in polymer blends, influencing material properties.
Characterizing the resulting microstructures is essential for understanding material behavior.
Current methods may lack the precision to fully capture complex microstructural features.

Purpose of the Study:

To establish a correlation between scattering experimental data and Minkowski functionals.
To utilize machine learning for microstructure characterization in polymer blends.
To validate the predictive power of Minkowski functionals derived from scattering data.

Main Methods:

Morphological image analysis to compute Minkowski functionals.
Gaussian process regression (machine learning) applied to simulated spinodal decomposition data.
Analysis of predictions from four Gaussian process regression models using scattering data.

Main Results:

A strong correlation was identified between scattering data and Minkowski functionals.
Machine learning models successfully predicted Minkowski functionals from scattering data.
The study demonstrates the utility of Minkowski functionals in describing spinodal decomposition.

Conclusions:

Minkowski functionals, when combined with machine learning and scattering data, provide a robust method for characterizing spinodal decomposition microstructures.
This approach offers a new pathway for quantitative analysis in polymer blend research.
The findings pave the way for improved material design and performance prediction.