Challenges to Quantitative Multivariate Statistical Analysis of Atomic-Resolution X-Ray Spectral

A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e. g., channels with L and K lines). As an example, the Y2Ti2O7 pyrochlore-structured oxide (assumed here to be ideal! was interrogated at the [011] projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derived k-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable.