Morphologically dependent alternating‐current and direct‐current breakdown strength in silica–polypropylene nanocomposites

In this article, we report the synthesis of a new bimodal surface ligand morphology on silica nanoparticles. Combining grafting‐to and grafting‐from approaches, in this study, we demonstrated the efficacy of anthracene surface modification for improving the dielectric breakdown strength (DBS) under alternating‐current and direct‐current conditions and that of a matrix‐compatible polymer brush for controlling the nanofiller (NF) dispersion. Ligand‐modified spherical colloidal SiO₂ nanoparticles (～14 nm in diameter) were mixed into polypropylene, and the resulting dispersion was improved over the unmodified particles, as shown with transmission electron microscopy. The results suggest that the electronic structure of the anthracene‐modified particle surface was critical to the improvement in DBS. In addition, the DBS of the composite was shown to depend on the dispersion state of the filler and the mode of stress; this indicated that the individually dispersed nanoparticles were not necessarily the optimal morphology for all stress conditions. Additionally, the precise nature of the matrix‐compatible brush was less important than the NF dispersion it produced. The bimodal grafted architectural design has provided a promising solution for the control of the dispersion and surface properties, especially for high‐molecular‐weight polymer matrices. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44347.     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.     

Synthetic and Crystallographic Insight into Exploiting sp2 Hybridization in the Development of α-l-Fucosidase Inhibitors

The sugar fucose plays a myriad of roles in biological recognition. Enzymes hydrolyzing fucose from glycoconjugates, α-l -fucosidases, are important targets for inhibitor and probe development. Here we describe the synthesis and evaluation of novel α-l -fucosidase inhibitors, with X-ray crystallographic analysis using an α-l -fucosidase from Bacteroides thetaiotamicron helping to lay a foundation for future development of inhibitors for this important enzyme class.     

Biological conversion of low-grade coal discard to a humic substance-enriched soil-like material

International Journal of Coal Science & Technology - A mutualistic relationship between grasses, coal-degrading fungi, and arbuscular mycorrhizal fungi was proposed to account for the...     

同步、低EMI LED驱动器具有集成式开关和内部PWM调光能力

具有内部同步、2A开关的36V LED驱动器LT3922,是一款紧凑、通用的LED驱动器。