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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 Fe2+ and Fe3+ 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.

Electron tomography is a widely spread technique for recovering the three dimensional (3D) shape of nanostructured materials. Using a spectroscopic signal to achieve a reconstruction adds a fourth chemical dimension to the 3D structure. Up to date, energy filtering of the images in the transmission electron microscope (EFTEM) is the usual spectroscopic method even if most of the information in the spectrum is lost. Unlike EFTEM tomography, the use of electron energy-loss spectroscopy (EELS) spectrum images (SI) for tomographic reconstruction retains all chemical information, and the possibilities of this new approach still remain to be fully exploited. In this article we prove the feasibility of EEL spectroscopic tomography at low voltages (80 kV) and short acquisition times from data acquired using an aberration corrected instrument and data treatment by Multivariate Analysis (MVA), applied to Fe(x)Co((3-x))O(4)@Co(3)O(4) mesoporous materials. This approach provides a new scope into materials; the recovery of full EELS signal in 3D.

High-resolution monochromated electron energy loss spectroscopy~EELS! at subnanometric spatial resolution and ,200 meV energy resolution has been used to assess the valence band properties of a distributed Bragg reflector multilayer heterostructure composed of InAlN lattice matched to GaN. This work thoroughly presents the collection of methods and computational tools put together for this task. Among these are zero-loss-peak subtraction and nonlinear fitting tools, and theoretical modeling of the electron scattering distribution. EELS analysis allows retrieval of a great amount of information: indium concentration in the InAlN layers is monitored through the local plasmon energy position and calculated using a bowing parameter version of Vegard Law. Also a dielectric characterization of the InAlN and GaN layers has been performed through Kramers-Kronig analysis of the Valence-EELS data, allowing band gap energy to be measured and an insight on the polytypism of the GaN layers.

Photoinduced phase separation, which limits the available band gap energies for photovoltaic applications, was reported for a range of mixed-halide perovskites. A microscopic understanding of the phase separation mechanism is still lacking but may be beneficial to rationalize limitations as well as enable the design of phase-stable perovskite semiconductors. In this letter, electron-beam-induced phase separations and transformations were investigated in a small crystallite of CsPb(Br 0.8 I 0.2 ) 3 by means of in situ high-resolution imaging in a transmission electron microscope. The acquired time series was evaluated using principal and independent component analysis to classify the structural change during the illumination by the electron beam. A more iodine-rich phase with the approximate composition of CsPb(Br 0.6 I 0.4 ) 3 was found to form at the edges of the particle, while a ternary pure bromide phase of CsPbBr 3 remained at its center. These results provide an atomistic picture of in-grain phase segregation into iodide-rich phases at grain boundaries and bromide-rich phases in the interior of the grain.

In this article, we report on the properties of indium 6 tin oxide (ITO) deposited on thin-film silicon layers designed for the 7 application as carrier selective contacts for silicon heterojunction 8 (SHJ) solar cells. We find that ITO deposited on hydrogenated 9 nanocrystalline silicon (nc-Si:H) layers presents a significant drop 10 on electron mobility µ e in comparison to layers deposited on 11 hydrogenated amorphous silicon films (a-Si:H). The nc-Si:H layers 12 are not only found to exhibit a larger crystallinity than a-Si:H, 13 but are also characterized by a considerably increased surface rms 14 roughness. As we can see from transmission electron microscopy, 15 this promotes the growth of smaller and fractured features in the 16 initial stages of ITO growth. Furthermore, secondary ion mass 17 spectrometry profiles show different penetration depths of hydro-18 gen from the thin film silicon layers into the ITO, which might both 19 influence ITO and device passivation properties. Comparing ITO to 20 aluminum doped zinc oxide (AZO), we find that AZO can actually 21 exhibit superior properties on nc-Si:H layers. We assess the impact 22 of the modified ITO R sh on the series resistance R s of SHJ solar 23 cells with >23% efficiency for optimized devices. This behavior 24 should be considered when designing solar cells with amorphous 25 or nanocrystalline layers as carrier selective contacts. 26 Index Terms-Aluminum doped zinc oxide (AZO), indium tin 27 oxide (ITO), secondary ion mass spectrometry (SIMS), series 28 resistance, silicon heterojunction (SHJ), transmission electron 29 microscopy (TEM), transparent conductive oxide (TCO).

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.

Inverse problems in image processing, phase imaging, and computer vision often share the same structure of mapping input image(s) to output image(s) but are usually solved by different applicationspecific algorithms. Deep convolutional neural networks have shown great potential for highly variable tasks across many image-based domains, but sometimes can be challenging to train due to their internal non-linearity. We propose a novel, fast-converging neural network architecture capable of solving generic image(s)-to-image(s) inverse problems relevant to a diverse set of domains. We show this approach is useful in recovering wavefronts from direct intensity measurements, imaging objects from diffusely reflected images, and denoising scanning transmission electron microscopy images, just by using different training datasets. These successful applications demonstrate the proposed network to be an ideal candidate solving general inverse problems falling into the category of image(s)-toimage(s) translation.