Mechanical Characteristics of Diamond-Like Moiré Films

JETP Letters - The mechanical characteristics of diamond-like films, such as Dn21.8, Dn27.8, and Dn29.4 moiré diamanes, formed by the hydrogenation of graphene layers twisted at an angle of...     

Infrared Reflection Spectra of (FeIn2S4)x∙(In2S3)1–x Solid Solutions

Journal of Applied Spectroscopy - IR reflectance spectroscopy at 50–450 cm–1 was used to study homogeneous monocrystals of (FeIn2S3)x?(In2S3)1–x grown by the method of...     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Quantum Key Distribution with Untrusted Detectors Accessible to an Eavesdropper

JETP Letters - A simple fundamental modification of quantum key distribution protocols has been proposed: it is not required to protect the results of avalanche detectors from an eavesdropper, but...     

MWCNTs/hydroxypropyl cellulose/polyethylene glycol-based shape-stabilized phase change materials

Journal of Thermal Analysis and Calorimetry - Polyethylene glycol (PEG) is widely used as phase change materials (PCMs). However, the leakage and low thermal conductivity issues restrict its...     

Rapid qualitative profiling and quantitative analysis of Juglandis Mandshuricae Cortex and seven flavonoids by ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry

Juglandis Mandshuricae Cortex is the bark of Juglans mandshurica Maxim., which has been used as a folk medicine plant in China and India. In this study, an ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry method was developed to clarify and quantify the chemical profiling of Juglandis Mandshuricae Cortex rapidly. A total of 113 compounds were characterized. Among them, seven flavonoids were simultaneously quantified in 15 min, including myricetin, myricetrin, taxifolin, kaempferol, quercetin, quercitrin, and naringenin. The method was validated for accuracy, precision, and the limits of detection and quantification. All calibration curves showed a good linear relationship (r > 0.9990) within test ranges. The intra- and inter-day relative standard deviations were less than 2.16%. Accuracy validation showed that the recovery was between 95.6 and 101.3% with relative standard deviation values below 2.85%. The validated method was successfully applied to determine the contents of seven flavones in Juglandis Mandshuricae Cortex from seven sources and the contents of these places were calculated respectively. This method provides a theoretical basis for further developing the medicinal value of Juglandis Mandshuricae Cortex.     

Carbohydrate-based nanostructured catalysts: applications in organic transformations

The requirement of green and sustainable materials to prepare heterogeneous catalysts has intensified for practical reasons over the past few decades. Carbohydrates are possibly the most plentiful and renewable organic materials in nature with inimitable physiochemical properties, plausible low-cost and large-scale production, and sustainability features could be exploited in the generation of nanostructured heterogeneous catalysts. This review article outlines the organic transformations catalyzed by diverse carbohydrate-based nanostructured catalysts in greener and environmentally friendly processes. Selected examples are highlighted for a variety of organic reactions exploiting the proposed catalysts’ reactivity and reusability, and interactions with the intrinsic nature of the applied carbohydrate supports; advantages and speculated challenges of the introduced catalysts are deliberated as well.     

Intermetallic Compounds CsnMk (М = Na,K, Rb,Pt, Au,Hg, Te): Geometrical and Topological Analysis,Cluster Precursors,and Self-Assembly of Crystal Structures

Crystallography Reports - A geometrical and topological analysis has been performed, and self-assembly of the crystal structures of intermetallic compounds CsnMk (М = Na, K, Rb, Pt, Au, Hg,...     

X-ray Diffraction Study of Structural Changes in High-Strength Ceramics Based on Zirconium Oxide with Additions of Ytterbium and Neodymium Oxides after Hydrothermal Treatment

Crystallography Reports - A surface structural analysis of two ceramic samples of zirconium oxide (ZrO2) sintered at 1550°C, stabilized in the tetragonal form, before and after hydrothermal...     

BENDING OF THIN ELECTROMAGNETOELASTIC PLATES

Journal of Applied Mechanics and Technical Physics - Main correlations in the theory of bending of thin electromagnetoelastic plates are obtained, in which complex potentials are used. Exact...