Simultaneous measurement of methyl tert‐butyl ether and tert‐butyl alcohol in human serum by headspace solid‐phase microextraction gas chromatography–mass spectrometry

The abundant production of methyl tert‐butyl ether (MTBE) and its widespread use have led to an increase in the potential for human exposure. This work described a simple, fast, sensitive, reliable and low‐cost method for the simultaneous measurement of MTBE and its metabolite, tert‐butyl alcohol (TBA) in human serum by headspace solid‐phase microextraction gas chromatography–mass spectrometry. Extraction conditions were optimized and 40 °C, 10 min, 250 rpm and 0.3 g NaCl for a 1 mL sample were the optimal conditions. This method showed good analytical performance in terms of sensitivity with limits of detection in serum (1 mL) of 0.03 µg/L for MTBE and 0.05 µg/L for TBA, accuracy (mean recovery values) from 75.8% to 85.8%, precision (relative standard deviations) <10% and sample stability (biodegradation) <10% after 28 days. A verification experiment proved the reproducibility and stability of this method as well. Finally the method was used to detect 212 specimens, and the internal dose levels for MTBE in human serum were presented in China. Copyright © 2015 John Wiley & Sons, Ltd.     

Synthesis of 1-perfluoroalkynyl phosphonates

Intramolecular Wittig reaction is extended to the synthesis of 1-perfluoroalkynyl phosphonates.     

Characterization of mechanical properties of jute/PLA composites containing nano SiO2 modified by coupling agents

Although jute fiber-reinforced PLA composites show strong application prospects, their low mechanical properties limit their applications to some extent. In this paper, nano-SiO₂ particles as well as nano SiO₂ modified by coupling agents which can efficiently improve the strength and toughness of composite materials are introduced into the PLA matrix. The bending, stretching and thermal properties of designed jute/PLA nonwoven composites were studied. The study shows that the nano-SiO₂ particles are beneficial to the interface performance between the PLA matrix and jute leading to improvement in the mechanical properties and thermal stability. Moreover, thermomechanical properties indicate that the addition of SiO₂ can improve the jute/PLA interfacial adhesion and increase the glass transition temperature of the material. Finally, toughening mechanism of nano-SiO₂ particles in the jute/PLA composite was analyzed.

     

Heterometallic iodoplumbates modified by copper(I) or silver(I) with viologens

Cu/Ag(I) were introduced into iodoplumbate systems to produce two new heterometallic iodoplumbates with viologen as templates, i.e. (PV)₂(Pb₂Cu₂I₁₀) (1) and [(BV)(Pb₂AgI₇)]_n (2) (PV²⁺ = propyl viologen, BV²⁺ = benzyl viologen), in which the common connection of PbI₆ units have been remarkably altered. In (PV)₂(Pb₂Cu₂I₁₀) (1), two PbI₆ octahedra are bridged by two CuI₄ tetrahedra via face-sharing to give a (Pb₂Cu₂I₁₀)^4? cluster, but the ternary one-dimensional polymeric (Pb₂AgI₇)_n^2n? of [(BV)(Pb₂AgI₇)]_n (2) is assembled from edge-sharing AgI₄ tetrahedra and PbI₆ octahedra. Their optical band gaps and fluorescence were also discussed. The absorption edges of haloplumbates could be engineered by introduction of suitable conjugated molecules as templates.     

量子力学在化学上的应用

在谈量子力学在化学上应用之前,先让我们回顾一下量子力学发展的简单历史。1900年间,普朗克为了要解决辐射与周围物体处于平衡状态时的能量分布的黑体辐射问题,引进了能量分立值的量子观念和普朗克常数h=6.625×10-27尔格·秒。因此,我们通常都说量子力学诞生在这一年。事实上,普朗克创立能量分立值的观念后,还经过相当长的一段发展时期,才真正建立目前的量子力学。     

多波长光交叉连接节点中的串扰研究

密集波分复用技术(DWDM)在提供传输能力的同时,还具有无可比拟的联网优势.光交叉连接(OXC)是其中的关键技术.由于OXC可对光波长进行全光的交叉连接,极大的提高了系统的容量.但由于在光链路上没有电的再生,使得噪音在光信道上积累起来,尤其是引入无法消除的同频串扰对系统造成了严重的影响.本文从全光通信网络(AON)的几种节点结构出发,讨论了信道的串扰机制,并对如何优化OXC节点结构进行了研究.     

光学活性N-邻噁唑啉苯基甲基丙烯酰胺聚合物的合成及其手性光学性质

在三氟甲磺酸稀土盐(Ln(OTf)3,Ln=La,Nd,Sm,Y)的存在下,光学纯N-邻唑啉苯基甲基丙烯酰胺((S)-MeOPMAM)经自由基聚合反应得到相应的光学活性聚合物.考察了稀土盐种类、用量及溶剂性质等因素对聚合反应立体化学的影响.研究发现,以Y(OTf)3为调节剂、正丁醇为溶剂的体系能在一定程度上提高聚合反应的全同立体定向性.聚合物的手性光学性质明显依赖于立构规整度,随全同含量增大,聚合物的比旋光度和π-π*电子跃迁区域的Cotton效应强度呈下降趋势.利用1H-NMR技术研究了上述聚合物与1,1′-联-2-萘酚(BINOL)的对映选择性相互作用,结果表明,全同三元组含量较高的聚合物不仅使酚羟基质子峰向低场位移,而且导致信号分裂.     

Photodynamic Effect of Riboflavin on Chitosan Coatings and the Application in Pork Preservation

Riboflavin (RF) was considered to be possessed of photoactivity to generate reactive oxygen species (ROS) under ultraviolet (UV) light, which is thought to be a favorable antibacterial candidate. Herein, RF was incorporated into chitosan (CS) coatings and treated under UV with different exposure times (2, 4, and 6 h) to improve the physicochemical and antibacterial properties. The results showed that the light transmittance and antibacterial performance of chitosan coatings gradually increased with the extension of the UV irradiation time. The antibacterial ability of chitosan coatings correlated with the generation of ROS: ∙OH and H₂O₂, which achieved 1549.08 and 95.48 μg/g, respectively, after 6 h irradiation. Furthermore, the chitosan coatings with UV irradiation also reduced the pH value, total volatile basic nitrogen (TVB-N), ΔE, and total viable counts (TVC) and improved sensory attributes of pork. In conclusion, the UV irradiated chitosan coatings could be used as an environmentally friendly antimicrobial packaging material to effectively delay the spoilage of pork, maintain its sensory quality and prolong its shelf life.     

DeepReac+: deep active learning for quantitative modeling of organic chemical reactions

Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.

Based on GNNs and active learning, DeepReac+ is designed as a universal framework for quantitative modeling of chemical reactions. It takes molecular structures as inputs directly and adapts to various prediction tasks with fewer training data.