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Color Representation of Electron Microprobe Area-Scan Images by a Color Separation Process.

Harvey Yakowitz¹, Kurt F J Heinrich¹

¹Institute for Materials Research, National Bureau of Standards, Washington, D.C. 20234.

Journal of Research of the National Bureau of Standards. Section A, Physics and Chemistry

|January 14, 2020

Summary

This study details a method for creating interpretable composite color photographs from electron probe microanalyzer (EPMA) x-ray scanned images. This technique enhances elemental analysis in fields like metallurgy and biology.

Keywords:

Color photography metallography petrography photomicrography scanning electron microprobe x-ray microscopy

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Area of Science:

Materials Science
Analytical Chemistry
Microscopy

Background:

Electron probe microanalysis (EPMA) generates elemental distribution data.
Visualizing multi-elemental data from EPMA requires advanced imaging techniques.
Conventional methods lack direct elemental-to-color interpretability.

Purpose of the Study:

To develop a method for creating interpretable composite color photographs from EPMA data.
To establish a clear relationship between primary colors (red, green, blue) and specific elements.
To demonstrate the utility of this technique across various scientific disciplines.

Main Methods:

Preparation of composite color photographs using x-ray area scanned images from EPMA.
Conversion of black-and-white scanned images into color-separation positives.
Utilizing appropriate filters and color mixing for accurate element representation.

Main Results:

Three-color composites are fully interpretable based on the assigned primary color for each element.
The procedure allows for convenient color print preparation, especially with Polaroid film.
Demonstrated successful applications in metallurgy, mineralogy, and biology.

Conclusions:

The described method provides a convenient and interpretable way to visualize multi-elemental data from EPMA.
This technique significantly enhances the understanding of elemental distributions in complex samples.
The approach is versatile and applicable to a wide range of scientific research areas.