In-situ cation exchange enhances room temperature phosphorescence of a family of metal-organic frameworks

The rational designability and chemical tunability of metal-organic frameworks(MOFs)are enabling tributes to efficaciously enhance their room temperature phosphorescence(RTP)performance.A family of stable anionic MOFs,[Zn₂(4,5-ImDC)₂]M₂(NKU-132,M=(CH₃)₂NH₂or(CH₂CH₃)₂NH₂),featuring significant RTP have been synthesized.By rational cation selection and in-situ replacement from dimethylammonium to diethylammonium,the phosphorescence lifetime is increased from 30.88 to126.3 ms,along with less sensitivity to air.This work provides an anti-quenching and lifetime tuning example for RTP-MOFmaterials via facile host-guest chemistry.     

The deformed Hermitian-Yang-Mills equation on almost Hermitian manifolds

In this paper,we consider the deformed Hermitian-Yang-Mills equation on closed almost Hermitian manifolds.In the case of the hypercritical phase,we derive a priori estimates under the existence of an admissible C-subsolution.As an application,we prove the existence of solutions for the deformed Hermitian-Yang-Mills equation under the condition of existence of a supersolution.     

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...     

线上线下混合式实验教学模式的改革与探索

大学化学实验课程是培养材料、化学、化工等专业人才应用基本操作和理论解决实际问题的综合性实践训练环节。文章探讨了当前大学化学实验发展由于受到仪器设备大型化、专业化、成本大、更新快等因素限制出现瓶颈,设计了线上线下混合实验教学模式,进行相应的软硬件建设,并开展线上线下混合式实验教学模式的改革与探索。该教学模式具有良好的教学效果,并且可复制可推广,为创新人才的培养和实验教学的创新提供了有益的借鉴。     

基于无人机探测大柴旦地区近地层气溶胶特征

通过改装多旋翼无人机（UAV）和搭载各类载荷以及联合地基观测设备对大柴旦地区大气、环境以及气溶胶参数进行测量。利用获得的数据资料，对该地区近地层气溶胶粒子数浓度（即单位体积空气中气溶胶粒子的数目）、消光系数以及气象要素等特征进行了分析。结果表明，在大柴旦地区，近地层气溶胶粒子数浓度日变化显著，呈现双峰形态，气溶胶粒子数浓度的变化范围为75～220 cm^-3,消光系数的变化范围为0.004～0.038 km^-1;当风速小于6 m/s时，气溶胶粒子数浓度与风速呈负相关关系；当风速大于6 m/s时，二者呈正相关关系；相对湿度对气溶胶粒子的影响较小，这可能是由于该地区以沙尘型气溶胶为主，吸湿性较弱。本研究基于多旋翼无人机探测平台，可以有效地获得近地层精细化大气、环境结构，有助于研究人员了解该地区气溶胶的结构、变化特征以及建立气溶胶模式，同时也为气溶胶及大气环境参数探测方法提供了技术支撑及思路拓展。     

Visible-light-induced,autopromoted nickel-catalyzed three-component arylsulfonation of 1,3-enynes and mechanistic insights

Science China Chemistry - A light-induced, nickel-catalyzed three-component arylsulfonation of 1,3-enynes in the absence of photocatalyst is reported. This methodology exhibited mild conditions,...     

The comparable structural,elastic anisotropic and thermophysical properties of advanced U–Si fuel to baseline UO2: a DTF method

The European Physical Journal B - Molecular dynamics and Monte Carlo methods are common measurements to study the diffusion coefficients of the fluid particles under restricted conditions. Here,...     

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%.