The formation of fibrous structure of polyamide 6 induced by the three-step forming process and its prominent reinforcement in nitrile rubber

Nitrile rubber (NBR) blends with excellent performance have always been a hot research topic in petroleum field. Due to the excellent performance and compatibility of polyamide 6 (PA6), it provides an opportunity for the preparation of high-performance NBR/PA6 blends. In this article, NBR/PA6 blends were prepared by the three-step molding process. Experimentally, it was found that PA6 has a prominent reinforcement effect in NBR matrix. The variation of this mechanical property was investigated from different aspects of the crystal structure, crystallinities, phase morphology, and so on. It can be cleared that the formation of fibrous structure of PA6 phase is the main factor for reinforcement of the polymer blends. Meanwhile, the formation mechanism of the special phase structure induced by the three-step process is deeply expounded and its structural evolution schematic is established. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47472.     

纳米氧化物对水泥基料化学收缩的影响

采用ASTM化学收缩试验方法,研究纳米SiO2、MgO、Al2O3对水泥基材料化学收缩变化的影响,明确纳米材料种类的差异性。结果表明,各实验组水泥基材料化学收缩均经历三阶段:急速变化区、缓慢变化区、平稳区,7d始水泥基材料化学收缩基本稳定。几种纳米材料的最佳掺量对水泥基材料的化学收缩改善作用大小为:SiO2>Al2O3>MgO。研究发现,纳米材料种类和掺量的不同对化学收缩的影响也较大,在选用时,需综合考虑。     

A novel temperature-tolerant foamed resin for enhanced oil recovery

The synthesis and performance of a novel temperature-tolerant foamed resin for enhanced oil recovery were investigated using various methods, including infrared, NMR, scanning electron microscopy (SEM), and displacement experiments. Polycondensation of furfuryl alcohol prepolymers was confirmed by the infrared and NMR results. The poor temperature tolerance of furfuryl alcohol prepolymers after gelation at high temperatures is mainly due to the fracture of furan rings. The addition of ester additives is an effective method of increasing the temperature tolerance of the prepared foamed resins and can effectively reduce the weight-loss rate of the polycondensation products. The SEM results show that the skeleton structure of the foamed resin remains intact after high-temperature treatment. Thus, the novel plugging agent system has excellent thermal stability and still has a high strength (>0.8 MPa) after high-temperature aging treatment for 40 days, giving the prepared foamed resin a good plugging performance (plugging rate > 91%) at 250 °C. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47161.     

Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。