SnSxSe2-x单晶纳米片的可控制备与表征

锡二硫族化合物可以通过改变硫和硒的含量来连续调控三元合金材料的带隙、载流子浓度等物理化学性质,在电子和光电子器件应用上具有巨大的潜力。本文采用化学气相沉积(CVD)技术可控地制备了不同元素组分的SnS_xSe_2-x(x=0,0.2,0.5,0.8,1.0,1.2,1.5,1.8,2.0)单晶纳米片。采用扫描电子显微镜(SEM)、原子力显微镜(AFM)、能量色散X射线光谱(EDS)、透射电子显微镜(TEM)以及拉曼光谱等手段对SnS_xSe_2-x纳米片进行了综合表征。结果表明本方法成功实现了元素百分比可调的SnS_xSe_2-x单晶纳米片的可控制备。重点研究了依赖于元素百分比的SnS_xSe_2-x的拉曼特征谱,实验结果与基于密度泛函理论(DFT)的第一性原理计算得到的SnS_xSe_2-x的拉曼仿真谱高度吻合,理论计算结果较好地诠释了实验拉曼光谱发生变化的原因。本研究提供了一种元素百分比可调的三元SnS_xSe_2-x单晶纳米片的可控制备方法,同时对锡二硫族化合物的明确、无损识别提供了方案。     

Electronically Excited States in Model Complexes of Noble Metal Clusters with Carbon Nanodots

Russian Physics Journal - Theoretical calculations of excited states in the complexes of gold and silver three-atom nanoclusters with carbon quantum nanodots are performed using the M062X...     

单层MXene纳米片Ti2N电磁特性的过渡金属(Sc、V、Zr)掺杂效应

二维材料MXene纳米片由于具有较大的比表面积和较高的电子迁移率而受到广泛的关注。本文采用基于密度泛函理论的第一性原理计算,对单层MXene纳米片Ti₂N电磁特性的过渡金属(Sc、V、Zr)掺杂效应进行了系统研究。结果表明,所有过渡金属掺杂体系结合能均为负值,结构均稳定;其中Ti₂N-Sc体系的形成能为-2.242 eV,结构更易形成,且保持稳定;掺杂后Ti₂N-Sc、Ti₂N-Zr体系磁矩增大;此外,Ti₂N-Sc体系中保留了较高的自旋极化率,达到84.9%,可预测该体系在自旋电子学中具有潜在的应用价值。     

Determination of Polycyclic Aromatic Hydrocarbons in Soils and Bottom Sediments by Gas Chromatography–Mass Spectrometry with QuEChERS Sample Preparation

Journal of Analytical Chemistry - The work is dedicated to the determination of polyaromatic hydrocarbons (PAHs) in soils and bottom sediments by gas chromatography–mass spectrometry using...     

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...     

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...     

Plasma Crystal in (3 + 1) Dimensions

JETP Letters - A quasi-two-dimensional plasma crystal in (3 + 1) dimensions has been experimentally observed for the first time; i.e., three spatial coordinates of each microparticles of the...     

Magnetoresistance and Quantum Oscillations in WTe2 Semimetal

Physics of the Solid State - The discovery of extreme magnetoresistance (MR) in nonmagnetic materials attracted attention to WTe2 semimetal. We studied MR in a single crystal of tungsten...