arXiv subjects
Publications and source records attributed to Pavel Potapov.
This manuscript summarizes the recent developments in EELS quantification flow as will be implemented in the CEOS Panta Rhei and TEMDM software. This should serve as a technical reference for the algorithms used in the software.
The manuscript considers Scanning Transmission Electron Microscopy (STEM) images and derives transformations needed to correct various distortions occurring during scanning. These transformations form the basis for the correction algorithms implemented in the CEOS Panta Rhei and TEMDM software. The manuscript is intended as a technical reference and is meant to be published only on arXiv rather than in peer-reviewed journals.
Magnetic materials with strong spin-orbit coupling (SOC) are essential for the advancement of spin-orbitronic devices, as they enable efficient spin-charge conversion, complex magnetic structures, spin-valley physics, topological phases and other exotic phenomena. 5d transition-metal oxides such as SrIrO3 feature large SOC, but usually show paramagnetic behavior due to broad bands and a low density of states at the Fermi level, accompanied by a relatively low Coulomb repulsion. Here, we unveil ferromagnetism in 5d SrIrO3 thin films grown on SrTiO3 (111). Through substrate-induced structural engineering, a zigzag stacking of three-unit-cell thick layers along the [111] direction is achieved, stabilizing a ferromagnetic state at the interfaces. Magnetotransport measurements reveal an anomalous Hall effect below ~30 K and hysteresis in the Hall conductivity below 7 K, indicating ferromagnetic ordering. X-ray magnetic circular dichroism further supports these results. Theoretical analysis suggests that the structural engineering of the IrO6 octahedral network enhances the density of states at the Fermi level and thus stabilizes Stoner ferromagnetism. This work highlights the potential of structurally engineered 5d oxides for spin-orbitronic devices, where efficient control of SOC-induced magnetic phases by electric currents can lead to lower energy consumption and improved performance in next-generation device technologies.
Two-dimensional random metal networks possess unique electrical and optical properties, such as almost total optical transparency and low sheet resistance, which are closely related to their disordered structure. Here we present a detailed experimental and theoretical investigation of their plasmonic properties, revealing Anderson (disorder-driven) localized surface plasmon (LSP) resonances of very large quality factors and spatial localization close to the theoretical maximum, which couple to electromagnetic waves. Moreover, they disappear above a geometry-dependent threshold at ca. 1.7 eV in the investigated Au networks, explaining their large transparencies in the optical spectrum.
Hybrid metallic nanoparticles encapsulated in oxide shells are currently intensely studied for plasmonic applications in sensing, medicine, catalysis, and photovoltaics. Here, we introduce a method for the synthesis of Au@Ag@SiO$_2$ cubes with a uniform silica shell of variable and adjustable thickness in the nanometer range; and we demonstrate their excellent, highly reproducible, and tunable optical response. Varying the silica shell thickness, we could tune the excitation energies of the single nanoparticle plasmon modes in a broad spectral range between 2.55 and 3.25\,eV. Most importantly, we reveal a strong coherent coupling of the surface plasmons at the silver-silica interface with the whispering gallery resonance at the silica-vacuum interface leading to a significant field enhancement at the encapsulated nanoparticle surface in the range of 100\,\% at shell thicknesses $t\,$$\simeq\,$20\,nm. Consequently, the synthesis method and the field enhancement open pathways to a widespread use of silver nanoparticles in plasmonic applications including photonic crystals and may be transferred to other non-precious metals.
This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.
STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.