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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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Author Spotlight: A Tailor-Made Sample Preparation Approach for Enhanced MALDI-IMS Analysis of Hard Palm Seeds

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Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity

Jasmine Eshun¹, Natalie C Lamar¹, Sinan G Aksoy¹

¹National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|July 31, 2024

Summary

This summary is machine-generated.

Automated particle analysis (APA) now uses a novel neural network method to cluster particle relationships. This technique effectively distinguishes complex samples, such as differentiating gunshot residue from similar materials.

Keywords:

automated particle analysis deep learning few-shot neural network graph theory scanning electron microscopy–energy-dispersive X-ray spectroscopy

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Area of Science:

Materials Science
Forensic Science
Computational Science

Background:

Automated particle analysis (APA) generates extensive compositional, size, and shape data for individual particles using energy-dispersive X-ray spectroscopy and scanning electron microscopy.
APA is crucial for identifying sample origins by detecting specific particle compositions, though these contextual particles often form a small fraction of the sample.
Interpreting complex samples is challenging due to compositional diversity within mixtures and individual particles.

Purpose of the Study:

To develop a method for computing and clustering similarity graphs that reveal inter-particle relationships within complex samples.
To demonstrate the utility of this method in distinguishing between different types of samples, specifically gunshot residue (GSR) and materials that can be mistaken for it.
To enhance the interpretability and quantitative comparability of APA data.

Main Methods:

Utilized a multi-modal few-shot learning neural network to compute and cluster similarity graphs based on APA data.
Integrated standard APA techniques with advanced data processing methods.
Applied the developed workflow to distinguish known gunshot residue samples from potentially confounding samples.

Main Results:

Successfully demonstrated a method to compute and cluster similarity graphs, effectively describing inter-particle relationships.
Showcased the capability to distinguish samples exposed to gunshot residue from those frequently mistaken for it.
Generated additional, interpretable, and quantitatively comparable information from APA data.

Conclusions:

The novel neural network-based approach enhances APA by revealing hidden inter-particle relationships.
This method offers a powerful tool for forensic analysis, improving the accuracy of sample source identification.
The workflow provides a readily interpretable and quantitatively comparable format for complex sample analysis.