Multiple least-squares fitting for quantitative electron energy-loss spectroscopy — an experimental investigation using standard specimens

WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS

The analysis of energy loss near edge structures in EELS is a powerful method for a precise characterization of elemental oxidation states and local atomic coordination with an outstanding lateral resolution, down to the atomic scale. Given the complexity and sizes of the EELS spectrum images datasets acquired by the state-of-the-art instrumentation, methods with low convergence times are usually preferred for spectral unmixing in quantitative analysis, such as multiple linear least squares fittings. Nevertheless, non-linear least squares fitting may be a superior choice for analysis in some cases, as it eliminates the need of calibrated reference spectra and provides information for each of the individual components included in the fitted model. To avoid some of the problems that the non-linear least squares algorithms may suffer dealing with mixed-composition samples and, thus, a model comprised by a large number of individual curves we proposed the combination of clustering analysis for segmentation and non-linear least squares fitting for spectral analysis. Clustering analysis is capable of a fast classification of pixels in smaller subsets divided by their spectral characteristics, and thus increases the control over the model parameters in separated regions of the samples, classified by their specific compositions. Furthermore, along with this manuscript we provide access to a self-contained and expandable modular software solution called WhatEELS. It was specifically designed to facilitate the combined use of clustering and NLLS, and includes a set of tools for white-lines analysis and elemental quantification. We successfully demonstrated its capabilities with a control sample of mesoporous cerium oxide doped with praseodymium and gadolinium, which posed challenging case-study given its spectral characteristics.

Atomic-Level EELS Mapping Using High-Energy Edges in Dualeels™ Mode

With advancements in aberration correction, the spatial resolution of scanning transmission electron microscopy (STEM) has been enormously improved. In addition to the reduction of the STEM probe size, a dramatic increase in the STEM probe current has been realized, leading to the routine acquisition of high-resolution elemental and chemical maps using electron energy loss spectrometry (EELS). Using EELS combined with these advanced STEM instruments, atomic-level resolution information can be obtained from various types of materials, revealing the nature of interfaces, elemental distribution, presence of defects, and much more. In addition to simple elemental composition distributions, EELS is capable of delivering information about the chemical bonding, local atomic coordination, oxidation states, band gaps, and chemical phases of a broad range of materials at the fundamental resolution limit of the property being probed. Atomic-level EELS maps of these fundamental material properties can now be obtained with the acquisition time, to a large extent, limited only by the speed of the EELS spectrometer and not by the signal being measured. The availability of fast EELS spectrometers with large angular collection efficiencies has closed the gap between the rate of signal generation in the specimen and the speed at which this signal can be detected. This significantly increases the amount of information that can be acquired using EELS. Using the most recent generation of spectrometers, EELS data can be acquired at well over 1,000 spectra per second with a high-duty cycle. Fifth-order spectral aberration correction in this generation of spectrometers allows the use of the large collection angles needed to match the increased convergence angle that Cs-probe-corrected systems present, improving collection efficiency while maintaining energy resolution. These advances, when taken together, result in a well matched source/detector system capable of recording high-energy EELS edges at atomic resolution at a rate fast enough to limit electron beam damage to the sample.

Model based quantification of EELS spectra

Recent advances in model based quantification of electron energy loss spectra (EELS) are reported. The maximum likelihood method for the estimation of physical parameters describing an EELS spectrum, the validation of the model used in this estimation procedure, and the computation of the attainable precision, that is, the theoretical lower bound on the variance of these estimates, are discussed. Experimental examples on Au and GaAs samples show the power of the maximum likelihood method and show that the theoretical prediction of the attainable precision can be closely approached even for spectra with overlapping edges where conventional EELS quantification fails. To provide end-users with a low threshold alternative to conventional quantification, a user friendly program was developed which is freely available under a GNU public license.

Parallel electron energy-loss spectroscopy free from gain variation

A new method is presented for removing the effect of the gain variation of parallel detectors used for the quantitation of trace elements with electron energy loss spectroscopy (EELS). Use of the ratio of two first-difference spectra eliminates the effect of gain variation of the detector, therefore eliminating the need for gain normalization and dark current subtraction. This method is particularly suitable for revealing small signals superimposed on a large background, a typical scenario for trace element quantitation of both biological and inorganic materials. This method has been tested on a system with a cooled CCD camera as the parallel detector and illustrated by the analysis of low concentration Ca in an organic matrix. The method is expected to be generally applicable to spectral analysis affected by gain variations of parallel detectors.