Predicting Visibility in Elemental Maps Derived from X-ray Spectrum Images

A third-generation silicon drift detector (SDD) in the form of a silicon multicathode detector (SMCD) was tested as an analytical x-ray spectrometer on a scanning electron microscope. Resolution, output count rate, and spectral quality were examined as a function of the detector peaking time from 8 micros to 250 ns and over a range of input count rate (dead time). The SDD-SMCD (50 mm2 active area) produced a resolution of 134 eV with a peaking time of 8 micros. The peak width and peak channel were nearly independent of the input count rate (at 8 micros peaking time, the peak width degradation was 0.003 eV/percent dead time and peak position change was -0.7 eV over the dead time range tested). Maximum output count rates as high as 280 kHz were obtained with a 500 ns peaking time (188 eV resolution) and 500 kHz with a 250 ns peaking time (217 eV resolution). X-ray spectrum imaging was achieved with a pixel dwell time as short as 10 ms (with 1.3 ms overhead) in which a 2048 channel (10 eV/channel) spectrum with 2-byte intensity range was recorded at each pixel (scanned at 128 x 128). With a 220 kHz output count rate, a minor constituent of iron (present at a concentration of 0.04 mass fraction or 4 weight %) in an aluminum-nickel alloy could be readily detected in the x-ray maps derived from the x-ray spectrum image database accumulated in 185 s.

Elemental mapping at the microstructural level by scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry (EDS), while widely applied in science, engineering, and technology, has been limited in performance by the throughput of the lithium-drifted silicon detector [Si(Li)-EDS] which restricts the number of X-ray counts measured per image pixel. The emergence of the silicon drift detector (SDD-EDS) has greatly extended the X-ray throughput, by a factor of 25 to 70 for the same spectral resolution. This improved performance enables practical X-ray spectrum imaging (XSI), in which a complete X-ray spectrum is recorded at each image pixel. By performing complete quantitative corrections for background, peak overlap, and matrix effects to each pixel spectrum, full compositional mapping can be achieved. Various elemental mapping collection strategies are described, including quantitative mapping at the major (concentration C > 0.1 mass fraction), minor (0.01 ≤ C ≤ 0.1), and trace (C < 0.01) constituent levels, extreme pixel density (gigapixel) mapping, rapid mapping (in 10 seconds or less), and high spatial resolution mapping with the thermal field emission gun scanning electron microscope.

Energy-dispersive X-ray (EDX) spectrum imaging, where a full spectrum is acquired at each pixel in a rastered image, combines the advantages of imaging and analysis for quantitative compositional mapping. This method has great advantages for the analysis of compositional variations, because the contrast inherent in EDX spectrum images arises from the distinctiveness of the characteristic X-ray spectra in various regions of the surveyed microstructure. EDX spectrum imaging provides for simultaneous spatial, spectral and temporal resolution of the X-rays emitted from the specimen. Many EDX manufacturers have incorporated a spectrum imaging capability within their acquisition and analysis software, and ongoing improvements in computing power will enhance the practicality of spectrum imaging methods, in particular the acquisition, manipulation and analysis of the large raw data files that are generated with this method. This paper surveys the potential role of spectrum imaging in tasks that support true quantitative X-ray microanalysis of bulk specimens: robust qualitative analysis, the construction of representative spectra that describe the specimen, and the extraction of elemental concentrations from the representative spectra.

Spectrum imaging, where a complete spectrum is acquired at each pixel in an image, is potentially a very powerful technique for the characterization of materials. The primary challenge to overcome is to extract all of the relevant information from what are typically large data sets that cannot be readily visualized in their entirety. For energy-dispersive X-ray (EDX) spectrum images, simple mapping has typically been done. In this work, the application of multivariate statistical analysis (MSA) to the characterization of a metal/alumina braze by SEM-EDX spectrum images is described. The braze characterized in this work joins polycrystalline alumina and a copper-silver eutectic alloy containing some titanium (an ‘active’ metal). The specimen geometry consisted of a sandwich of two pieces of alumina, two braze layers and a Kovar (primarily Fe, Ni and Co) filler layer in the middle. The entire assembly was heat-treated to bond the interfaces and then a polished cross-section was prepared.

PdAu nanocrystals are synthesised by Geobacter sulfurreducens, a dissimilatory metal-reducing bacterium, and the resulting bimetallic nanocrystal-decorated microbes are imaged using a range of advanced electron microscopy techniques. Specifically, the first example of elemental mapping of fully hydrated biological nanostructures using scanning transmission electron microscope (STEM) energy dispersive X-ray (EDX) spectrum imaging within an environmental liquid-cell is reported. These results are combined with cryo-TEM and ex situ STEM imaging and EDX analysis with the aim of better understanding microbial synthesis of bimetallic nanoparticles. It is demonstrated that although Au and Pd are colocalized across the cells, the population of nanoparticles produced is bimodal, containing ultrasmall alloyed nanocrystals with diameters 200 nm in diameter) which show higher Pd contents and exhibit a Pd enriched shell only a few nanometers thick. The application of high-resolution imaging techniques described here offers the potential to visualize the microbe-metal interface during the bioproduction of a range of functional materials by microbial “green” synthesis routes, and also key interfaces underpinning globally relevant environmental processes (e.g., metal cycling).

When will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y2Ti2O7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. Future applications, such as spallation proton-neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

We report results from a 50 ks XMM-Newton observation of the dust-reddened broad-line quasar FTM 0830+3759 (z=0.413) selected from the FIRST/2MASS Red Quasar survey. For this AGN, a very short 9 ks Chandra exposure had suggested a feature-rich X-ray spectrum and HST images revealed a very disturbed host galaxy morphology. Contrary to classical, optically-selected quasars, the X-ray properties of red (i.e. with J-Ks> 1.7 and R-Ks> 4) broad line quasars are still quite unexplored, although there is a growing consensus that, due to moderate obscuration, these objects can offer a unique view of spectral components typically swamped by the AGN light in normal, blue quasars. The XMM-Newton observation discussed here has definitely confirmed the complexity of the X-ray spectrum revealing the presence of a cold (or mildly-ionized) absorber with Nh ~10^{22} cm^-2 along the line of sight to the nucleus and a Compton reflection component accompanied by an intense Fe K emission line in this quasar with a Lum(2-10) ~5 x 10^{44} erg/s. A soft-excess component is also required by the data. The match between the column density derived by our spectral analysis and that expected on the basis of reddening due to the dust suggests the possibility that both absorptions occur in the same medium. FTM 0830+3759 is characterized by an extinction/absorption-corrected X-ray-to-optical flux ratio alphaox = -2.3, that is steeper than expected on the basis of its UV luminosity. These findings indicate that the X-ray properties of FTM 0830+3759 differs from those typically observed for optically-selected broad line quasars with comparable hard X-ray luminosity.

Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected x-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO3 sample, such that ferroelectric domains are mapped with unit-cell resolution.

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

A Monte Carlo simulation code has been developed to describe the x-ray generation in a specimen for electron probe microanalysis (EPMA), enabling x-ray spectra observed by EPMA to be reproduced theoretically. The Monte Carlo simulation is based on the use of the Mott cross section and Bethe stopping power equation in describing elastic and inelastic scattering processes, respectively. With respect to x-ray generation the Sommerfeld theory for bremsstrahlung radiation was described by equations of Kirkpatrick–Wiedmann and of Statham for bremsstrahlung cross section. The up-to-date compilation of mass absorption coefficient by Henke, Gullikson, and Davis [At. Data Nucl. Data Tables 54, 181 (1993)] is used to evaluate attenuation of x-ray intensity. To verify the present Monte Carlo simulation measurements of x-ray spectra for Cu and Rh targets have been performed for primary electron energies of 10–30 keV by energy-dispersive x-ray spectrometry of an electron probe microanalyzer. Excellent agreement between experimental spectra and calculated results has been confirmed in the cases investigated, leading to the conclusion that the Monte Carlo simulation technique will be a powerful tool for more comprehensive understanding of x-ray generation in EPMA specimens.

Towards atomically resolved EELS elemental and fine structure mapping via multi-frame and energy-offset correction spectroscopy

In this research, the discrimination of tungsten and different oxidization states of its oxide has been studied using an electron probe microanalyzer (EPMA), and the results have been compared with those obtained by X-ray photoelectron spectroscopy (XPS). As a result, the relationship between the oxidization state of tungsten oxide and the intensity ratio of W Mβ/Mα in the X-ray spectra of EPMA has been clarified in small-area analysis. We can discriminate W, WO2, W18O49 (WO2.72) and WO3 using EPMA under given conditions. Comparing XPS with EPMA, a practical disadvantage of XPS is that the small-area analysis is sometimes difficult using the conventional XPS equipment, while the commercially available EPMA easily achieves a few micrometer resolution. XPS measurement is sensitive in the surface state; EPMA can be used to measure without being influenced by surface oxidation.

The accurate calculation of characteristic peak intensity is essential for interpreting X-ray spectra in electron microprobe analysis. Conventionally, the measured intensity from a standard of known composition is used as a reference to simplify the calculation. However, if no such standard is available, then all factors influencing X-ray generation and X-ray detection efficiency must be included. If the intensity and energy distribution of the background radiation can also be calculated, the investigator can simulate an entire spectrum from an assumed composition, gaining powerful benefits in setting up an experiment and in confirming the results. The study presented here demonstrates a fast method of spectrum simulation, suitable for energy-dispersive spectroscopy (EDS), and assesses the accuracy using 309 spectra from samples of known composition. These include K, L, and M lines from elements of atomic number 6-92, excited by beam energies in the range of 5-30 keV. The RMS error between 360 measured and calculated peak intensities was found to be 7.1%. Central to the method is the use of the ratio of peak intensity/total background intensity, which allows spectra to be compared from instruments of differing collection efficiency, thereby easing the collection of data over a wide range of conditions.

The accurate calculation of characteristic peak intensity is essential for interpreting X-ray spectra in electron microprobe analysis. Conventionally, the measured intensity from a standard of known composition is used as a reference to simplify the calculation. However, if no such standard is available, then all factors influencing X-ray generation and X-ray detection efficiency must be included. If the intensity and energy distribution of the background radiation can also be calculated, the investigator can simulate an entire spectrum from an assumed composition, gaining powerful benefits in setting up an experiment and in confirming the results. The study presented here demonstrates a fast method of spectrum simulation, suitable for energydispersive spectroscopy (EDS), and assesses the accuracy using 309 spectra from samples of known composition. These include K, L, and M lines from elements of atomic number 6–92, excited by beam energies in the range of 5–30 keV. The RMS error between 360 measured and calculated peak intensities was found to be 7.1%. Central to the method is the use of the ratio of peak intensity/total background intensity, which allows spectra to be compared from instruments of differing collection efficiency, thereby easing the collection of data over a wide range of conditions.

Aberration‐corrected scanning transmission electron microscopy (STEM) is providing previously unattainable views of materials at the atomic scale. The quality of STEM data is now often limited by environmental and experimental factors instead of instrument factors (e.g. electron optics). Some of these environmental limitations can be overcome by collecting and processing STEM data using new data science techniques. These techniques expose new atomic‐scale materials information by improving our ability to measure atomic column positions, 3D structure, single point defects, and atomic‐scale composition. The precision in locating atomic column positions in STEM images is fundamentally limited by the image signal to noise ratio (SNR), but typically practical limits including sample and microscope instabilities that produce distortions in STEM images are encountered before reaching the SNR limit. We have developed a non‐rigid registration (NRR) technique that corrects image distortion of all length scales and enables averaging to enhance the SNR.[1–2] Sub‐pm precision images of single crystal materials have been achieved by NRR and averaging high angle annular dark field (HAADF) STEM images. NRR has allowed measurements of pm‐scale bond length variations of Pt nanocatalyst atoms that may help explain their catalytic activity. Figure 1 shows NRR and averaged HAADF STEM data of a Pt nanocatalyst on an alumina support from a side‐view that exhibits ˜2 pm precision. Displacement measurements of each atomic column position reveal moderate Pt surface bond length contraction and strong but localized strain of Pt atoms near nanoparticle‐support interface. Some of the interface strain is transferred up the twin boundary. Determining the three dimensional atomic structure of materials from two dimensional S/TEM images is a major hurdle. The standardless atom counting technique is one promising route to measure local sample thickness by quantitatively comparing experimental and simulated HAADF STEM images and can be used to deduce 3D structure.[3] Unlike previous examples, NRR and averaging STEM images have allowed standardless atom counting with the uncertainty no longer dominated by Poisson noise [1]. This should allow the unique determination of the number of atoms in atomic columns, although this has not yet been demonstrated due to other sample limitations. Point defects are critical to the properties of a wide range of materials, but imaging single defects is challenging. Quantitative STEM has allowed imaging single substitutional and interstitial dopant impurity atoms[4], but experimentally imaging single vacancies has remained elusive. We have used HAADF STEM frozen phonon multislice simulations to predict the detectability of La vacancies in LaMnO 3 by the reduced atomic column intensity and atomic column distortions around the vacancy. NRR and averaging HAADF STEM images of LaMnO 3 improves the SNR and the image precision sufficiently to potentially detect single La vacancies. Experimental images contain candidate single La vacancies that have local atomic column distortions and intensity variations which match simulated predictions. Atomic‐resolution composition maps can be created using STEM energy dispersive x‐ray spectroscopy (EDS) spectrum imaging (SI). However, long total dwell times that may introduce spatial distortions are required because of low x‐ray production and collection efficiency. The most common approach to minimize distortions is to sum multiple SIs using online drift‐correction software that discards the individual SIs and HAADF images. The quality of EDS SIs of a Nd 2/3 TiO 3 sample was improved by saving the simultaneously acquired raw HAADF and EDS SI series, and applying post acquisition NRR and averaging. The resulting elemental maps show less spatial distortions and more atomic localization of x‐rays. In addition, a novel non‐local principle component analysis further enhances the quality of EDS SIs compared to conventional denoising methods.[5]