Synthesis,Molecular Docking,and Hemorheological Activity of New 4-(Thien-2-yl)-3-aminopyridine-2(1H)-one Derivatives

Russian Journal of General Chemistry - On the basis of 4-(thien-2-yl)-3-aminopyridine-2(1H)-one, the corresponding chloroacetamide and condensed 1H-pyrido[2,3-b][1,4]oxazine-2(3H)-one were...     

Separation,Preconcentration, and Determination of Lead,Cadmium, and Iron Using Cloud Point Extraction with Flame Atomic Absorption Spectrometry

Journal of Applied Spectroscopy - The concentration of heavy metals in drinking water is an important standard for water quality evaluation and water pipeline corrosion detection. This research...     

Dislocation Structure Evolution in Low-Stability States of Solid Solutions of Alloys Under Their Deformation

Russian Physics Journal - The results of a study of the dislocation structure evolution in polycrystals of homogeneous solid solutions in low-stability states in Cu-Mn-based alloys with FCC crystal...     

Alpha-gamma decay studies of $$^{247}$$ Md

The European Physical Journal A - The goal of the present paper is twofold. First, a novel expansion many-body method applicable to superfluid open-shell nuclei, the so-called Bogoliubov in-medium...     

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%.

     

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,...     

Half-Metallic Ferromagnetism in the Co-Doped CdS Diluted Magnetic Semiconductor

JETP Letters - The magnetic properties of Co-doped CdS has been investigated by ab initio calculations using the GGA + U approximation. The study reveals the ferromagnetic ordering of Co-doped CdS...     

Inversion of Aromaticity of NH-Tautomers of Free-Base Corroles in the Lowest Triplet T1-State

Journal of Applied Spectroscopy - The aromaticity in the lowest triplet T1-state of NH-tautomers of corrole free bases with different peripheral substitution architecture was investigated using...     

自掺杂SnO2微球的水热合成及其可见光催化性能

本研究采用水热法,以柠檬酸为螯合剂,通过控制n(Sn⁴⁺)/n(Sn²⁺)的数值,合成了由具有丰富氧空位的SnO₂纳米晶体组装成的微球。通过X射线衍射(XRD)、扫描电子显微镜(SEM)、透射电子显微镜(TEM)、傅里叶红外光谱(FT-IR)、X射线光电子能谱(XPS)及UV-Vis漫反射光谱对SnO₂纳米微球进行表征分析,结果表明:在酸性水热条件和柠檬酸的螯合作用下,二氧化锡纳米晶体聚集形成微球;在Sn⁴⁺/Sn²⁺摩尔比例为3:7时,其微球尺寸最小,整体分散性较好;同时适量二价锡离子的掺杂使得该样品氧空位浓度达到最佳,氧空位的存在将使得样品光吸收范围拓展至可见光,因而该样品显示出较强的可见光催化效率,在8 min内完全降解甲基橙。