EELS tomography in multiferroic nanocomposites: from spectrum images to the spectrum volume

Atomic-Scale Determination of Cation Inversion in Spinel-Based Oxide Nanoparticles

The atomic structure of nanoparticles can be easily determined by transmission electron microscopy. However, obtaining atomic-resolution chemical information about the individual atomic columns is a rather challenging endeavor. Here, crystalline monodispersed spinel Fe₃O₄/Mn₃O₄ core-shell nanoparticles have been thoroughly characterized in a high-resolution scanning transmission electron microscope. Electron energy-loss spectroscopy (EELS) measurements performed with atomic resolution allow the direct mapping of the Mn²⁺/Mn³⁺ ions in the shell and the Fe²⁺/Fe³⁺ in the core structure. This enables a precise understanding of the core-shell interface and of the cation distribution in the crystalline lattice of the nanoparticles. Considering how the different oxidation states of transition metals are reflected in EELS, two methods of performing a local evaluation of the cation inversion in spinel lattices are introduced. Both methods allow the determination of the inversion parameter in the iron oxide core and manganese oxide shell, as well as detecting spatial variations in this parameter, with atomic resolution. X-ray absorption measurements on the whole sample confirm the presence of cation inversion. These results present a significant advance toward a better correlation of the structural and functional properties of nanostructured spinel oxides.

Clustering analysis strategies for electron energy loss spectroscopy (EELS)

In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyze electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe₃O₄/Mn₃O₄ core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyze spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy.

Linear chemically sensitive electron tomography using DualEELS and dictionary-based compressed sensing

We have investigated the use of DualEELS in elementally sensitive tilt series tomography in the scanning transmission electron microscope. A procedure is implemented using deconvolution to remove the effects of multiple scattering, followed by normalisation by the zero loss peak intensity. This is performed to produce a signal that is linearly dependent on the projected density of the element in each pixel. This method is compared with one that does not include deconvolution (although normalisation by the zero loss peak intensity is still performed). Additionally, we compare the 3D reconstruction using a new compressed sensing algorithm, DLET, with the well-established SIRT algorithm. VC precipitates, which are extracted from a steel on a carbon replica, are used in this study. It is found that the use of this linear signal results in a very even density throughout the precipitates. However, when deconvolution is omitted, a slight density reduction is observed in the cores of the precipitates (a so-called cupping artefact). Additionally, it is clearly demonstrated that the 3D morphology is much better reproduced using the DLET algorithm, with very little elongation in the missing wedge direction. It is therefore concluded that reliable elementally sensitive tilt tomography using EELS requires the appropriate use of DualEELS together with a suitable reconstruction algorithm, such as the compressed sensing based reconstruction algorithm used here, to make the best use of the limited data volume and signal to noise inherent in core-loss EELS.

3D Visualization of the Iron Oxidation State in FeO/Fe3O4 Core-Shell Nanocubes from Electron Energy Loss Tomography

The physicochemical properties used in numerous advanced nanostructured devices are directly controlled by the oxidation states of their constituents. In this work we combine electron energy-loss spectroscopy, blind source separation, and computed tomography to reconstruct in three dimensions the distribution of Fe(2+) and Fe(3+) ions in a FeO/Fe3O4 core/shell cube-shaped nanoparticle with nanometric resolution. The results highlight the sharpness of the interface between both oxides and provide an average shell thickness, core volume, and average cube edge length measurements in agreement with the magnetic characterization of the sample.

Retrieving the electronic properties of silicon nanocrystals embedded in a dielectric matrix by low-loss EELS

In this work we apply low-loss electron energy loss spectroscopy (EELS) to probe the structural and electronic properties of single silicon nanocrystals (NCs) embedded in three different dielectric matrices (SiO2, SiC and Si(3)N(4)). A monochromated and aberration corrected transmission electron microscope has been operated at 80 kV to avoid sample damage and to reduce the impact of radiative losses. We present a novel approach to disentangle the electronic features corresponding to pure Si-NCs from the surrounding dielectric material contribution through an appropriate computational treatment of hyperspectral datasets. First, the different material phases have been identified by measuring the plasmon energy. Due to the overlapping of Si-NCs and dielectric matrix information, the variable shape and position of mixed plasmonic features increases the difficulty of non-linear fitting methods to identify and separate the components in the EELS signal. We have managed to solve this problem for silicon oxide and nitride systems by applying multivariate analysis methods that can factorize the hyperspectral datacubes in selected regions. By doing so, the EELS spectra are re-expressed as a function of abundance of Si-NC-like and dielectric-like factors. EELS contributions from the embedded nanoparticles as well as their dielectric surroundings are thus studied in a new light, and compared with the dielectric material and crystalline silicon from the substrate. Electronic properties such as band gaps and plasmon shifts can be obtained by a straightforward examination. Finally, we have calculated the complex dielectric functions and the related electron effective mass and density of valence electrons.

Nanoscale voxel spectroscopy by simultaneous EELS and EDS tomography

Extending the capabilities of electron tomography with advanced imaging techniques and novel data processing methods, can augment the information content in three-dimensional (3D) reconstructions from projections taken in the transmission electron microscope (TEM). In this work we present the application of simultaneous electron energy-loss spectroscopy (EELS) and energy-dispersive X-ray spectroscopy (EDS) to scanning TEM tomography. Various tools, including refined tilt alignment procedures, multivariate statistical analysis and total-variation minimization enable the 3D reconstruction of analytical tomograms, providing 3D analytical metrics of materials science samples at the nanometer scale. This includes volumetric elemental maps, and reconstructions of EDS, low-loss and core-loss EELS spectra as four-dimensional spectrum volumes containing 3D local voxel spectra. From these spectra, compositional, 3D localized elemental analysis becomes possible opening the pathway to 3D nanoscale elemental quantification.