BaAl2O4催化同时去除NOx和碳烟的性能研究

BaAl2O4催化同时去除碳烟和NOx具有较高的活性.与非催化燃烧比,在BaAl2O4催化作用下,碳烟的起燃温度(tig)降低了175℃以上,最大燃烧温度(tm)降低了240℃以上.碳烟与BaAl204紧密接触、反应气体中O2含量越高、反应气体的总流量越低越有利于催化反应进行.通过漫反射红外光谱(DRIFTS)分析,证实了以单齿硝酸盐、离子硝酸盐和桥位硝酸盐的形式存在于BaAl2O4表面的NO2(ad)物种与碳烟的反应在同时去除碳烟和NOx的过程中起主要作用.     

Selective photocatalytic aerobic oxidation of methane into carbon monoxide over Ag/AgCl@SiO2

Design of active catalysts for chemical utilization of methane under mild conditions is of great importance, but remains a challenging task. Here, we prepared a Ag/AgCl with SiO₂ coating (Ag/AgCl@SiO₂) photocatalyst for methane oxidation to carbon monoxide. High carbon monoxide production (2.3 μmol h⁻¹) and high selectivity (73%) were achieved. SiO₂ plays a key role in the superior performance by increasing the lifetime of the photogenerated charge carriers. Based on a set of semi in situ infrared spectroscopy, electron paramagnetic resonance, and electronic property characterization studies, it is revealed that CH₄ is effectively and selectively oxidized to CO by the in situ formation of singlet ¹O₂via the key intermediate of COOH*. Further study showed that the Ag/AgCl@SiO₂ catalyst could also drive valuable conversion using real sunlight under ambient conditions. As far we know, this is the first work on the application of SiO₂ modified Ag/AgCl in the methane oxidation reaction.

The Ag/AgCl@SiO₂ catalyst exhibits excellent photocatalytic activity in selective aerobic oxidation of methane to carbon monoxide with high selectivity, and extended real light simulation feasibility shows potential in practical application.     

ATR-FTIR光谱技术在聚合物膜研究中的应用

红外光谱是聚合物研究中常用的一种表征手段,而衰减全反射红外光谱（ATR-FTIR）更是由于在研究聚合物薄膜方面具有显著的优势而被广泛使用。逐层组装（layer-by-layer Assembly）技术是一种常用的组装聚合物超薄膜的方法,ATR-FTIR光谱技术的引入可以在获取膜组装过程中相应信息的同时有效地避免表征过程中对样品的损害。另一方面,ATR-FTIR方法与二维相关光谱技术（two-dimensional correlation spectroscopy, 2D correlation spectroscopy）相结合也是研究小分子（主要是水分子）在聚合物薄膜中的渗透行为的有效手段。本文对ATR-FTIR的基本原理和显著特点作了介绍,并以实例阐述该方法在逐层组装技术和水分子在薄膜内渗透行为研究两方面的应用。     

手性联萘酚配合物催化的缺电子烯烃不对称环氧化反应

缺电子烯烃的不对称环氧化反应是有机合成领域最具有挑战性的课题之一。手性联萘酚配体所修饰的催化剂是一种很优异的C2轴对称手性诱导源,可以催化各种α,β－不饱和羰基化合物如α,β－不饱和酮、α,β－不饱和羧酸脂等的不对称环氧化反应,具有良好的催化活性和对映选择性。本文对由手性联萘酚类配体所修饰的小分子催化剂、聚合物负载的催化剂和自负载催化剂在不饱和羰基化合物的催化不对称环氧化反应中的应用进行了综述,探讨了催化剂结构、配位金属原子、添加物、氧化剂、溶剂和反应温度等因素对手性联萘酚催化剂催化效能和对映选择性的影响。     

荧光聚合物研究进展

本文总结了近年来荧光聚合物的研究进展,主要介绍了荧光聚合物的分类：按其溶解性能可分为非水溶性、水溶性和两亲荧光聚合物三大类;荧光聚合物的合成：荧光化合物为引发剂、荧光化合物为链转移剂、荧光功能单体聚合、荧光化合物与聚合物的化学键合、非荧光功能单体聚合等五种制备荧光聚合物的设计合成方法;荧光聚合物的应用：荧光聚合物在荧光化学传感器、荧光分子温度计、荧光造影、药物载体、荧光探针等方面的应用研究。     

我国燃料乙醇生产技术的现状与展望

概述了目前国内外燃料乙醇产业现状.结合我国中粮生化能源(肇东)有限公司燃料乙醇装置,重点介绍了我国目前的燃料乙醇生产工艺技术水平、特点以及与国外的差距.本文从提高燃料乙醇生产技术水平、降低生产成本、寻找廉价非粮原料和开发新生产工艺等几个方面,对燃料乙醇生产技术的发展作了展望.     

水溶液中锌粉促进邻二醇合成的研究

邻二醇是有机合成中重要的合成子,是合成烯烃、酮类化合物的重要中间体,广泛用于农药、医药等精细化工品的合成.合成邻二醇最有效的方法是羰基化合物的还原偶联,通常由羰基化合物与相应的金属试剂或金属络合物作用而实现,往往反应时间长,要求的条件苛刻,有的收率比较低.     

Ultrafast and selective labeling of endogenous proteins using affinity-based benzotriazole chemistry

Chemical modification of proteins is enormously useful for characterizing protein function in complex biological systems and for drug development. Selective labeling of native or endogenous proteins is challenging owing to the existence of distinct functional groups in proteins and in living systems. Chemistry for rapid and selective labeling of proteins remains in high demand. Here we have developed novel affinity labeling probes using benzotriazole (BTA) chemistry. We showed that affinity-based BTA probes selectively and covalently label a lysine residue in the vicinity of the ligand binding site of a target protein with a reaction half-time of 28 s. The reaction rate constant is comparable to the fastest biorthogonal chemistry. This approach was used to selectively label different cytosolic and membrane proteins in vitro and in live cells. BTA chemistry could be widely useful for labeling of native/endogenous proteins, target identification and development of covalent inhibitors.

Affinity-based benzotriazole (BTA) probes selectively and covalently label native proteins or endogenous proteins in cells with a fast reaction rate. It is enormously useful for characterizing protein function in biological systems and for drug development.     

Application of Two Novel Acoustic Emission Parameters on Identifying the Instability of Granite

The issue of monitoring and early warning of rock instability has received increasing critical attention in the study of rock engineering. To investigate the damage evolution process of granite under triaxial compression tests, acoustic emission (AE) tests were performed simultaneously. This study firstly introduced two novel parameters, i.e., the coefficient of variation (CoV) of the information entropy and correlation dimension of the amplitude data from the AE tests, to identify the precursor of the failure of granite. Then the relationship between the changes in these parameters and the stress-time curve was compared and analyzed. The results of this study show that: (1) There is a strong correlation between the CoV of the information entropy and the failure process of granite. The granite failed when the CoV curve raised to a plateau, which could be used as an indicator of rock instability. (2) The fluctuation of the correlation dimension indicates the different stages during the loading process, i.e., the initial compaction stage, the linear elastic stage, the yield stage, and the failure stage. Each stage contains a descending and a rising process in the correlation dimension curve, and the exhibited starting point or the bottom point at the correlation dimension curve could be selected as the indicator point for the rock instability. (3) The combined analysis of the Information entropy and Correlation dimension can improve the accuracy of rock instability prediction. This study provides new insights into the prediction of rock instability, which has theoretical implications for the stability of subsurface engineering rock masses.     

A List-Ranking Framework Based on Linear and Non-Linear Fusion for Recommendation from Implicit Feedback

Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user–item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user–item interaction, we use the interaction grabbing layer to capture the user–item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement.