Tuesday, 24 May 2016

Announcing a new technique for SEM super-resolution mineral analysis

This post talks about a newly available technique for analyzing minerals using a scanning electron microscope equipped with EDS detectors, using existing hardware.

The images shown here are an electron back-scatter image, and corresponding mineral map of each pixel in the image analyzed with the new technique.

Figure 1: Automated nanomin analysis of mineral sample (no user intervention)

This approach does not constrain the number of minerals per pixel, unlike a standard QEMSCAN analysis. Rather, it allows multiple minerals to be reported within a single pixel. This goes beyond the EDS limit, revealing sub-pixel detail for the first time. It also allows each mineral to consist of multiple mineral variety end-members that are linked into a solid solution. For example, the plagioclase feldspar solid solution links the albite and anorthite end-member mineral spectra into a solid solution as a single mineral. Any mineral detected on that solid solution is automatically reported as a plagioclase, regardless of the percentage of sodium and calcium in the spectrum. It automatically computes the interchange of sodium and calcium, balancing out the other elements, without any interaction required from the user.

This removes the burden of managing complex mineral chemistry within the analytical software, and frees up time for analysis of the data.

In SEM-EDS mineral analysis, the interaction volume of the EDS signal is approximately 1-5um for typical measurement conditions. That is, each pixel represents the signal from a 5um volume. And when that volume has more than 1 mineral present, the signal measured is a sum of the several minerals, which may or may not have different elements.

The implication here is apparent - any pixel that does not fall wholly within a pure mineral grain should be reported as a mix of minerals. A few commercial SEM-EDS techniques attempt to avoid this problem by trying to avoid measuring pixels that aren’t clearly in the center of a mineral grain. Other try to remediate it by using a statistical approach based on the surrounding mineral classifications to touch-up the classification of the center pixel. Both of these work-arounds will work for a limited set of situations, but they fail where it’s impossible to avoid a fine texture.

Figure 2: Automated QEMSCAN analysis (no user intervention)

The centroid method risks runs a risk that the simplified analysis will miss the important features within the sample. And it also introduces a bias into the result if portions of the sample are skipped or left unclassified. In the image above, an automated QEMSCAN analysis shows large swathes of unclassified pixels, requiring further input from the user. The user must interactively decide whether to skip the unclassified pixels, try to recover them from surrounding data, or manually add mineral entries for the missing minerals. In other words, there is a significant effort required to improve the accuracy from the initial classification. While all these problems can be solved, they also add significant time to the process.

In contrast, the new technique shows a result where the pixels from multiple minerals have been reported, saving a significant amount of time to an operator. The operator has more time for interpreting the results, or classifying other samples.

At the current time, this technique is only offered by FEI (http://www.fei.com) within the Maps-Mineralogy software package.
The only link currently available is https://www.linkedin.com/pulse/finer-side-automated-spatial-mineralogy-lucy-plant