Large Area Mapping

Large Area Mapping (BSE & EDS)

The chamber size of the new JEOL JSM IT800HL Scanning Electron Microscope allows users to mount and analyze several samples at once and enables analysis of large and/or irregularly shaped samples (max sample dimension is ~8 cm). One of the key benefits of this new instrument is the ability to map large areas, allowing users to characterize chemical variability and/or microstructure across a sample. Most of the work done so far in the lab has been performed on samples mounted in 1-inch epoxy rounds or 2”-by1” thin section.

Backscatter Electron maps

One frequent type of analysis performed in our lab is to map the chemical variability of samples using Backscatter Electron imaging (BSE). The BSE intensity of a material is directly related to the mean atomic mass of the region being analyzed and, therefore, sample chemistry. We regularly collect BSE maps of entire thin sections (2”-by-1”) at a resolution better than 1 micron, with analysis completed within 2-4 hours. These maps provide users with an incredible amount of information, helping to identify the distribution and proportion of different mineral phases as well as the chemical variability of a sample at a range of different scales. The image below represents a backscatter map of several fragments from a gabbronorite xenolith from Hualalai volcano on Hawai’i, the image is almost exactly 1-inch across.  

Energy Dispersive Spectroscopy maps

In addition to BSE mapping we offer the option to acquire qualitative and quantitative chemical maps via Energy Dispersive Spectroscopy (EDS). Our Ultim Max 100 mm2 EDS detector from Oxford Instruments offers exceptional peak resolution and a maximum input count rate of 1.5 million counts per second! We regularly map samples up to 2” across at a resolution of ~15 microns, with the option of producing higher resolution maps of smaller features. We have also developed several useful Python tools that aid with the plotting, visualization, and interpretation of results, including a new machine learning method for automatic phase identification. The image below represents a gabbronorite xenolith from Hualalai volcano on Hawai’i, colored by phase, with chemical variations in the orthopyroxene and plagioclase crystals represented by the red and blue colors, respectively.