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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.
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Preparation of Samples for Electron Microscopy

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Atomic Emission Spectroscopy: Overview

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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Related Experiment Video

Updated: Feb 19, 2026

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Published on: July 5, 2016

Reduced electron exposure for energy-dispersive spectroscopy using dynamic sampling.

Yan Zhang¹, G M Dilshan Godaliyadda², Nicola Ferrier³

¹Materials Science Division, Argonne National Laboratory, 9700 Cass Ave, Lemont, IL 60439 United States.

Ultramicroscopy

|November 6, 2017

Summary

This study introduces a machine learning method for dynamic sparse sampling in scanning electron microscopy. It significantly reduces data acquisition time by up to 90% while preserving elemental map fidelity.

Keywords:

Dose reduction Dynamic sampling Energy dispersive spectroscopy (EDS)Neural networks SLADS Scanning electron microscopy (SEM)

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Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

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Area of Science:

Materials Science
Analytical Chemistry
Microscopy

Background:

Analytical electron microscopy and spectroscopy are crucial for materials analysis but are limited by irradiation damage to beam-sensitive specimens.
Minimizing sample damage and reducing data acquisition time are critical for high-throughput analysis of material structure and chemistry.

Purpose of the Study:

To develop a novel machine learning-based method for dynamic sparse sampling of Energy-Dispersive X-ray Spectroscopy (EDS) data.
To reduce data acquisition time and minimize irradiation damage in scanning electron microscopy of sensitive materials.

Main Methods:

A supervised learning approach for dynamic sampling was employed.
Neural networks were utilized for the classification of EDS data.
The method was applied to scanning electron microscopy for dynamic sparse sampling of EDS data.

Main Results:

A dramatic reduction in total sampling by up to 90% was achieved.
The fidelity of reconstructed elemental maps and spectroscopic data was maintained.
The method enables analysis of materials previously inaccessible to these techniques.

Conclusions:

The developed machine learning method offers a significant advancement in analytical electron microscopy and spectroscopy.
This approach facilitates high-throughput analysis and the study of beam-sensitive materials.
It broadens the applicability of imaging and elemental mapping techniques for challenging specimens.