Accurate identification and classification of a material based on energy dispersive spectrometry (EDS) has always been a challenge, down to the variability between systems, acquisition conditions, spectrum processing methods and accuracy in quantitative analysis results. Traditional methods rely on template spectra from known chemical compositions to match against an unknown spectrum. The spectrum matching algorithm assumes that template spectra are acquired with the same system and under the same conditions, which poses difficulties and in some cases is impossible. In this paper, a different approach is proposed based on quantitative analysis results as the basis for matching. Quantification-based matching removes the ambiguity introduced by different systems and acquisition conditions. Databases can be created based on the nominal composition of compounds without acquiring a spectrum from known samples. This paper explains the details of the matching algorithm and presents three examples to demonstrate the performance.

Quantitative compositional analysis with energy-dispersive X-ray spectroscopy (EDS) in the scanning transmission electron microscope (STEM) is an important tool for materials science. Here, we test a single-standard approach to quantitative EDS on focused ion beam (FIB) sections of SrTiO3, CaTiO3, and Fe sulfides. We confirm previous reports that shadowing of X-rays is an important factor to consider in robust quantitative analysis and should be mapped out to optimize signal collection. Our data show that the orientation of the half grids used in FIB sample preparation can be adjusted in the transmission electron microscope sample holder to provide optimum line-of-sight from the sample to the EDS detector, minimizing X-ray occlusion. A precision of 2% can be achieved when comparing EDS data in the STEM to quantitative wavelength-dispersive spectrometry in the electron microprobe. These results yield accuracies within 5% of stoichiometric composition for an optimized analytical geometry.

Electron probe microanalysis (EPMA) is a powerful tool for chemical characterization of materials on a microscopic scale. However, EPMA has the drawback that its information volume has a spatial extent of some 100 nm to a few µm. With the introduction of new electron sources, i.e., Schottky Thermal Field and Cold Field Emitter, where the electron beam is focused down to a few nm, measurements can be nowadays performed on the sub-micrometer scale. The goal of the work is to reveal the chemical composition of structures smaller than the excitation volume. New strategies are presented where the acquisition is performed at different positions on the sample and as a scan across a fine structure by using one or more single beam energies. Besides the well-known Monte-Carlo simulation, a deterministic model is also used. The deterministic model is based on moment equations of the Boltzmann equation. Inverse modeling is presented for several case studies. Due to the highly complex nonlinearity of the inverse model, an ill-posed and well-posed problem is shown as well. Finally, the method is extended to reconstruct 2D structures, i.e., rectangular shaped particles, with heterogeneous composition on lateral and depth scale.

Journal Article Use of Spectrum Simulation to Acquire Reliable Scans with a Wavelength Dispersive Spectrometer Get access Philippe Pinard, Philippe Pinard Oxford Instruments NanoAnalysis, High Wycombe, United Kingdom Corresponding author: philippe.pinard@oxinst.com Search for other works by this author on: Oxford Academic Google Scholar Rosie Jones, Rosie Jones Oxford Instruments NanoAnalysis, High Wycombe, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Lucia Spasevski, Lucia Spasevski Oxford Instruments NanoAnalysis, High Wycombe, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Simon Burgess, Simon Burgess Oxford Instruments NanoAnalysis, High Wycombe, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Peter Statham Peter Statham Oxford Instruments NanoAnalysis, High Wycombe, United Kingdom Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 81–82, https://doi.org/10.1093/micmic/ozad067.032 Published: 22 July 2023

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Combining energy dispersive spectrometry (EDS) and wavelength dispersive spectrometry (WDS) on a scanning electron microscope (SEM) provides a powerful and flexible tool for investigating spatial compositional variations in solid samples. EDS is highly sensitive and can quickly generate element maps over large sample areas. With large area silicon drift detectors (SDD) and advanced data processing, EDS X-ray mapping is so rapid that live chemical imaging (the generation of real-time compositional maps as the user moves around the sample) has become a reality [1]. Where EDS mapping reaches its limitations is when there is a need to investigate the spatial distribution of trace elements, as typically the detection limit of EDS is greater than ~1000 ppm (depending on element). Challenges with compositional mapping via EDS also arise when X-ray lines of more than one element are closely spaced, and therefore overlap in the EDS spectrum making them difficult to differentiate. This is where WDS comes in. WDS has a higher spectral resolution in comparison to EDS and consequently, higher peak to background ratios and lower detection limits (<100 ppm for many elements). Therefore, with WDS it is possible to map the distribution of trace elements, as well as produce accurate and representative maps for elements affected by X-ray peak overlaps in EDS. To demonstrate this, here we present an example EDS-WDS dataset collected from a steel sample containing inclusions with a range of compositions [2]. Some of the inclusions have an internal structure, and compositional variations that can simply be observed through BSE imaging. EDS point analysis on the inclusions reveal that some of the inclusions contain Ce, La, and other rare earth elements (REE). Due to the significant number of overlaps of REE peaks it is difficult to positively identify exactly

Electron probe microanalysis (EPMA) is a microanalytical technique widely used for the characterization of materials. Since its development in the 1950s, different instrumental and analytical developments have been made with the aim of improving the capabilities of the technique. EPMA has utilized crystal diffractors with gas detectors (wavelength-dispersive spectrometers, WDS) and/or solid-state detectors (energy-dispersive spectrometers, EDS) to measure characteristic X-rays produced by an electron beam. In this review, we give an overview of the most significant methodological developments of EPMA that have occurred in the last three decades, including the incorporation of large area diffractors, field-emission guns, high-spectral resolution X-ray grating spectrometers, silicon drift detectors, as well as more powerful Monte Carlo simulations, which have opened a wide range of new possibilities for the characterization of materials using EPMA. The capabilities of the technique are illustrated by a selection of representative applications of EPMA to materials science and engineering, chosen to show the current merits and limitations of the technique. Given the lack of coverage in previous reviews of the excellent capabilities of EPMA for measurements of thin films and coatings, that topic is covered in detail. We finally provide ideas for new research opportunities using EPMA.

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Energy-dispersive X-ray spectroscopy (EDS) is central to the compositional analysis of planetary materials. When coupled to a transmission electron microscope (TEM), EDS can provide both qualitative and quantitative information in the form of false-color maps and as (normalized) elemental abundances, respectively, at scales ranging from the micrometer down to the atomic. Such information is important in planetary science for identifying two-dimensional compositional variations and as an aid to phase identification. Quantitative information is particularly important for comparing material composition to the output of thermodynamic codes as part of the process of reverse engineering the origins and history of planetary materials and parameterizing chemical models of the early solar nebula. Thus, quantitative EDS has been a mainstay tool of the planetary-science community for decades and will continue to be important for analysis of samples we currently have in our collections and those returned by sample-return missions such as Hayabusa2 [1] and to be returned by OSIRIS-REx [2]. There

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The H5OINA file format is based on Hierarchical Data Format 5 (HDF5) and has been designed to exchange microanalysis data within the Oxford Instruments’ AZtec ecosystem as well as with third-party software and libraries. Here we explain the guiding principles used in the design of this file format and its scope of use within the microanalysis community. The first question to address is why we created another file format instead of providing documentation about proprietary file formats used to store data acquired in AZtec. Proprietary file formats are invariably optimised to suit one software platform and may radically change from one version to another as they constantly need to adapt to new features, hardware, compression schemes, etc. The main benefit of an exchange file format is to be independent of internal software changes and ensure some level of backward compatibility when new capability is added.

Besides resolution, sensitivity and speed of acquisition, a key difference between wavelength and energy dispersive spectrometry (WDS/EDS) is the large number of parameters needed to be decided before a WDS acquisition. There is no definitive answer to questions like ‘which X-ray line should I use’, ‘how long should I acquire the peak and backgrounds’, ‘what background positions should I choose’, etc. Beyond rules of thumb and trial and error, Reed and Buckley (1996) and Fournier (2000, 2001) developed tools using stored experimental WD scans or simulation models to assist and optimise the selection of collection parameters [1-3]. Building on these solutions for electron microprobes, we have developed an optimisation engine for combined WDS/EDS analysis in a scanning electron microscope (SEM) to automate the selection of X-ray lines and crystals, background positions and counting times of the WDS acquisition, as well as the EDS collection parameters [4]. The optimisation engine is based on the simulation of a theoretical WD spectrum from first principles. Physical models for EDS spectrum simulation are combined with new efficiency, resolution, and peak profile models for WD spectrometers. Models were constructed by extracting the net peak area and full width at half maximum from scans acquired over several X-ray lines and high-order reflections on a representative set of Oxford Instruments Wave spectrometers and crystals. Peak profiles are generated using a Lorentzian function scaled according to the natural width of X-ray transitions and convolved with a Gaussian function based on the spectrometer resolution. Figure shows a comparison between experimental and simulated spectra of pure silver and Ti-6Al-4V alloy. from (a) pure silver on PET crystal and (b) Ti-6Al-4V alloy on LiF crystal. The experimental spectra were acquired at 20 kV on a JEOL 5900 equipped with a Wave spectrometer.

We describe one approach to standardless quantification for energy-dispersive (ED) X-ray spectrometry that can achieved unnormalised results with an accuracy better than 5 %. This approach relies on a “factory” standards database, a model of the detector efficiency, calculated peak profiles, and a multi-step process to correct spectral artefacts and extract X-ray intensities from ED spectra. These processes and components, as well as the underlying equations involved in the quantification are detailed to illustrate the similarities between this approach and the classical standard-based k-ratio quantification. The accuracy was assessed by measuring and quantifying standard samples at different accelerating voltages (15 and 20 kV) and input count rates (50 and 200 kcps). In all cases, the measured, unnormalised concentrations yielded a centred distribution with a relative error less than 5 %. Excluding the analysis of light elements (B, C, O, N, F) improved the relative error to around 2 %