Dynamical Properties of Baryons

The internal structure of composite particles is conveniently described in terms of form factors (FFs)—these are experimentally accessible in annihilation and scattering of elementary reactions, and are theoretically calculable by all models that describe the properties of particles. FFs depend only on one kinematical variable, q2. This is the four-momentum transferred by the virtual photon that carries the interaction. Important developments in accelerator and detector techniques have brought impressive advances, both by extending the kinematical region and by reaching a higher precision. A critical review on the underlying methods and findings in polarized and unpolarized experiments is presented. The unique role played by polarization in determining the ratio of electric to magnetic form factors in the space-like region, and the extraction of individual form factors in the whole kinematical region, are described. Recent results at electron accelerators and electron–positron colliders confirm the existence of periodical structure in the annihilation cross section. We suggest a global framework which describes the dynamical structure of charge distribution in baryons, in order to build a coherent view of the creation and annihilation of baryonic matter.

Electrically controllable active plasmonic directional coupler of terahertz signal based on a periodical dual grating gate graphene structure

We propose a concept of an electrically controllable plasmonic directional coupler of terahertz signal based on a periodical structure with an active (with inversion of the population of free charge carriers) graphene with a dual grating gate and numerically calculate its characteristics. Proposed concept of plasmon excitation by using the grating gate offers highly effective coupling of incident electromagnetic wave to plasmons as compared with the excitation of plasmons by a single diffraction element. The coefficient which characterizes the efficiency of transformation of the electromagnetic wave into the propagating plasmon has been calculated. This transformation coefficient substantially exceeds the unity (exceeding 6 in value) due to amplification of plasmons in the studied structure by using pumped active graphene. We have shown that applying different dc voltages to different subgratings of the dual grating gate allows for exciting the surface plasmon in graphene, which can propagate along or opposite the direction of the structure periodicity, or can be a standing plasma wave for the same frequency of the incident terahertz wave. The coefficient of unidirectionality, which is the ratio of the plasmon power flux propagating along (opposite) the direction of the structure periodicity to the sum of the absolute values of plasmon power fluxes propagating in both directions, could reach up to 80 percent. Two different methods of the plasmon propagation direction switching are studied and possible application of the found effects are suggested.

Durability Improvement of Concentrated Polymer Brushes by Multiscale Texturing

Concentrated polymer brushes (CPBs) are promising soft-material coatings for improving tribological properties under severe sliding conditions, even in the macroscopic scale. Therefore, they are expected to be applied to mechanical sliding components. However, the durability of CPBs has remained challenging for industrial applications. Previous studies revealed that applying a groove texture to the CPB substrate is effective in improving the durability of CPBs. In order to achieve further improvement of durability of CPBs, we attempted to apply the periodical structure, which is a microfabricated structure corresponding to a surface roughness 0.02 μm, whereas the groove texture applied in previous studies has widths and depths in micrometres. In this study, the effect of the nano-periodic structure in addition to the groove texture applied to the CPB substrate on the durability of CPB is investigated. The results demonstrate a significant improvement in the durability of CPBs by up to 90% compared with non-textured CPB when an appropriate nano-periodic structure is applied (i.e. a nano-periodic structure oriented parallel to the groove texture).

Electrically Controllable Active Plasmonic Directional Coupler of Terahertz Signal based on a Periodical Dual Grating Gate Graphene Structure

We propose a concept of an electrically controllable plasmonic directional coupler of terahertz signal based on a periodical structure with an active (with inversion of the population of free charge carriers) graphene with a dual grating gate and numerically calculate its characteristics. Proposed concept of plasmon excitation by using the grating gate offers highly effective coupling of incident electromagnetic wave to plasmons as compared with the excitation of plasmons by a single diffraction element. The coefficient which characterizes the efficiency of transformation of the electromagnetic wave into the propagating plasmon has been calculated. This transformation coefficient substantially exceeds the unity (exceeding 6 in value) due to amplification of plasmons in the studied structure by using pumped active graphene. We have shown that applying different dc voltages to different subgratings of the dual grating gate allows for exciting the surface plasmon in graphene, which can propagate along or opposite the direction of the structure periodicity, or can be a standing plasma wave for the same frequency of the incident terahertz wave. The coefficient of unidirectionality, which is the ratio of the plasmon power flux propagating along (opposite) the direction of the structure periodicity to the sum of the absolute values of plasmon power fluxes propagating in both directions, could reach up to 80 percent. Two different methods of the plasmon propagation direction switching are studied and possible application of the found effects are suggested.

Applications of Smart Material of Li2O-(Nb/Ta)2O5-TiO2 Solid Solution Having a Unique Periodical Structure

In the Li2O-M2O5-TiO2 system, Li1+x-yM1-x-3yTix+4yO3 (M = Nb, or Ta, 0.06 ≤ x ≤ 0.33, 0 ≤ y ≤ 0.175 (LMT) forms a superstructure, known as smart material. The superstructure is formed by periodical insertion of a corundum-type intergrowth layer of [Ti2O3]2+ in a matrix having a trigonal structure during the grain growth. To apply this unique structure as a host material of phosphor, new phosphors doped with Mn4+ ion with a red emission colour, which had a broad peak around 685 nm excited by 493 nm. In order to improve the PL intensity, we investigated the compositions, Mn4+ ratio and crystal structure. Results showed that PL intensity was closely related to Mn4+ ratio and its crystal structure.

VISUALIZATION OF MICROWAVE ABSORPTION OF THE GRAPHITE PERIODICAL STRUCTURE WITH THERMOELASTIC OPTICAL MICROSCOPE

This paper shows a non-destructive visualization of the absorption of microwave filed by a graphite periodic structure. The visualization system was a thermo-elastic optical indicator microscope. The article presents the interaction of the electromagnetic field with graphite cylindrical cells of periodicity and shows the distribution of the electromagnetic field over the graphite cells. Depending on the distance between the periodic structure of graphite and the microwave source, the electromagnetic field distribution and absorption rate were different. The visualization was performed using a microwave signal with a frequency of $11~GHz$ and a maximum power of $35~dBm$.

Machine Learning-Based Multidomain Processing for Texture-Based Image Segmentation and Analysis

<a>Atomic and molecular resolved atomic force microscopy (AFM) images </a>offer unique insights into materials properties such as local ordering, molecular orientation and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. Usage of supervised learning and feature extraction allows to retain the consideration of specific problem-dependent features (such as types of periodical structure observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for segmentation of molecular resolved AFM images based on crystal orientation of observed domains, automated selection of boundaries and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique.

Machine Learning-Based Multidomain Processing for Texture-Based Image Segmentation and Analysis

<a>Atomic and molecular resolved atomic force microscopy (AFM) images </a>offer unique insights into materials properties such as local ordering, molecular orientation and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. Usage of supervised learning and feature extraction allows to retain the consideration of specific problem-dependent features (such as types of periodical structure observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for segmentation of molecular resolved AFM images based on crystal orientation of observed domains, automated selection of boundaries and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique.

Metal Chalcogenides Based Heterojunctions and Novel Nanostructures for Photocatalytic Hydrogen Evolution

The photo-conversion efficiency is a key issue in the development of new photocatalysts for solar light driven water splitting applications. In recent years, different engineering strategies have been proposed to improve the photogeneration and the lifetime of charge carriers in nanostructured photocatalysts. In particular, the rational design of heterojunctions composites to obtain peculiar physico-chemical properties has achieved more efficient charge carriers formation and separation in comparison to their individual component materials. In this review, the recent progress of sulfide-based heterojunctions and novel nanostructures such as core-shell structure, periodical structure, and hollow cylinders is summarized. Some new perspectives of opportunities and challenges in fabricating high-performance photocatalysts are also discussed.