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...     

吉林大学化学学院研究生课程思政的多维度构建与探索*

研究生课程思政的建设影响着研究生的培养质量,关乎着能否实现立德树人这一人才培养的根本任务。介绍了吉林大学化学学院立足学科特点,在研究生课程思政建设方面取得成功的经验,并对未来的课程思政建设方向进行了探讨。     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

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%.

     

SAPO-34提升铁钒基催化剂抗碱性能研究

采用浸渍法制备Fe-VO_x/SAPO-34和Fe-VO_x/TiO₂脱硝催化剂,探究SAPO-34分子筛与TiO₂两种载体负载铁钒基氧化物催化活性及抗碱性能的差异。借助X射线衍射(XRD)、X射线光电子能谱(XPS)、氨气程序升温脱附(NH₃-TPD)、氢气程序升温还原(H₂-TPR)、原位红外漫反射(in-situ DRIFTs)等表征手段对催化剂的骨架结构、表面物化性质、氧化还原能力以及对反应气体的吸脱附情况进行分析。结果表明:SAPO-34分子筛内部特定的孔道结构和稳定的骨架,有利于活性组分在载体上均匀分散,降低碱金属对表面活性中心的物理覆盖作用;同时其表面丰富的酸位点能够作为碱金属捕获位,保护催化剂表面的活性中心,保证催化剂的吸附-反应过程能够正常进行,从而使Fe-VO_x/SAPO-34表现出良好的抗碱金属能力。     

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...     

On Laser-Induced High-Order Wave Mixing and Harmonic Generation in a Graphene Quantum Dot

JETP Letters - We present results of numerical investigations of high-order wave mixing/harmonic generation (HWM/HHG) with many-body interaction processes derived by a strong two-frequency circular...     

A new class of spherically symmetric gravitational collapse

Theoretical and Mathematical Physics - We discuss the gravitational collapse of a spherically symmetric perfect fluid distribution of uniformly contracting stars. In a uniformly contracting star,...     

以磷化铁为添加剂沿(111)面生长磷掺杂金刚石大单晶

掺杂是调控金刚石性能的一种重要手段。本文采用温度梯度法,在5.6 GPa、1 312 ℃的条件下,选用Fe₃P作为磷源进行磷掺杂金刚石大单晶的合成。金刚石样品的显微光学照片表明,随着Fe₃P添加比例的增加,金刚石晶体的颜色逐渐变深,包裹体数量逐渐增加,晶形由板状转变为塔状直至骸晶。金刚石晶形的变化表明Fe₃P的添加使生长金刚石的V形区向右偏移,这是Fe₃P改变触媒特性的缘故。红外光谱分析表明,Fe₃P的添加使金刚石晶体中氮含量上升,这说明磷的进入诱使氮原子更容易进入金刚石晶格中。激光拉曼光谱测试表明,随着Fe₃P添加比例的增加,所合成的掺磷金刚石的拉曼峰位变化不大,其半峰全宽(FWHM)值变大,这说明磷的进入使得金刚石晶格畸变增加。XPS测试结果显示,随着Fe₃P添加比例的增加,金刚石晶体中磷相对碳的原子百分含量也会增加,这意味着添加Fe₃P所合成的金刚石晶体中有磷存在。