Recent works on the development of various electrorheological (ER) fluids composed of TiO 2 , SrÀTiÀO, and CaÀTiÀO particles coated with CÀO/ HÀO polar groups are summarized, in which an extremely large yield stress up to 200 kPa is measured and the dynamical yield stress reaches 117 kPa at a shear rate of 775 s À1 . Moreover, unlike that of traditional dielectric ER fluids, the yield stress displays a linear dependence on electric field strength. Experimental results reveal that it is the polar molecules adsorbed onto the dielectric particles that play the decisive role: the polar-molecule-dominated ER effect arises from the alignment of polar molecules by the enhanced local electric field in the gap between neighboring particles. The pretreatment of electrodes and the contrivance of new measuring procedures, which are desirable for the characterization and practical implementation of this material, are also discussed. The successful synthesis of these fluids has made many of the long since conceived applications of the ER effect available.

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As modern electronics are developed towards miniaturisation, high-degree integration and intelligentisation, a large amount of heat will be generated during the operation of devices. How to efficiently remove needless heat is becoming more and more crucial for the lifetime and performance of electronic devices. Many efforts have been made to improve the thermal conductivity of polymer composites, which is an important component of electronics. Herein, the authors report on preparation of boron nitride micosphere/epoxy composites. The cross-plane thermal conductivity of the resultant composites is up to 1.03 Wm-1 K-1. This is attributed to the thermally conductive network formed by the peeled hexagonal boron nitride flakes. Thanks to the superior thermal stability of boron nitride micosphere, the boron nitride micosphere/epoxy composite shows a decreased coefficient of thermal expansion (53.47 ppm/K) and an increased glass transition temperature (147.2°C) compared with the pure epoxy resin. In addition, the boron nitride micosphere/epoxy composite exhibits a lower dielectric constant compared with that of the hexagonal boron nitride/epoxy composite. This strategy can potentially pave the way for the design and fabrication of materials with high cross-plane thermal conductivity and lower dielectric properties.

BackgroundThe tidal flat is one of the important components of coastal wetland systems in the Yellow River Delta (YRD). It can stabilize shorelines and protect coastal biodiversity. The erosion risk in tidal flats in coastal wetlands was seldom been studied. Characterizing changes of soil particle size distribution (PSD) is an important way to quantity soil erosion in tidal flats.Method/Principal findingsBased on the fractal scale theory and network analysis, we determined the fractal characterizations (singular fractal dimension and multifractal dimension) soil PSD in a successional series of tidal flats in a coastal wetland in the YRD in eastern China. The results showed that the major soil texture was from silt loam to sandy loam. The values of fractal dimensions, ranging from 2.35 to 2.55, decreased from the low tidal flat to the high tidal flat. We also found that the percent of particles with size ranging between 0.4 and 126 μm was related with fractal dimensions. Tide played a great effort on soil PSD than vegetation by increasing soil organic matter (SOM) content and salinity in the coastal wetland in the YRD.Conclusions/SignificanceTidal flats in coastal wetlands in the YRD, especially low tidal flats, are facing the risk of soil erosion. This study will be essential to provide a firm basis for the coast erosion control and assessment, as well as wetland ecosystem restoration.

The recent developments in organoborane initiated C1 polymerization of ylides open unique horizons towards perfectly linear polymethylenes (equivalent to PE) and PE-based complex structures. This review summarizes research on conventional and newly discovered initiators/ylides, as well as initial efforts on C3 polymerization.

Novel polyethylene (PE)-based 3-miktoarm star copolymers A2B, (AB)2B and terpolymers (AC)2(BC) [A: PE; B, C: polystyrene (PS) or poly­(methyl methacrylate) (PMMA)] were synthesized by combining boron chemistry, polyhomologation, and atom transfer radical polymerization (ATRP). 1,4-Pentadiene-3-yl 2-bromo-2-methyl­propanoate was first synthesized followed by hydroboration with thexylborane to afford B-thexyl­boracyclanes, a multi-heterofunctional initiator with two initiating sites for polyhomologation and one for ATRP. After polyhomologation of dimethyl­sulfoxonium methylide the α,ω-dihydroxyl polyethylene (PE-OH)2-Br produced served as macroinitiator for the ATRP of styrene to afford (PE-OH)2-(PS-Br). Both (PE-OH)2-Br and (PE-OH)2-(PS-Br) were transformed to two new trifunctional macroinitiators (PE-Br)2-Br and (PE-Br)2-(PS-Br) through esterification reactions and used for the synthesis of (AB)2B and (AC)2(BC) 3-miktoarm star co/terpolymers. All intermediates and final products were characterized by 1H NMR, high temperature gel permeation chromatography (HT-GPC), and differential scanning calorimetry (DSC). The synthetic method is a general one and can be used for the synthesis of complex PE-based architectures by combination with other living/living-controlled polymerization techniques.

The first regioselective catalytic asymmetric [3 + 2] cycloaddition of benzofuranone-derived olefins with allenoates and substituted allenoates has been developed in the presence of (R)-SITCP.

Feature selection plays a crucial role in scientific research and practical applications. In the real world applications, labeling data is time and labor consuming. Thus, unsupervised feature selection methods are desired for many practical applications. Linear discriminant analysis (LDA) with trace ratio criterion is a supervised dimensionality reduction method that has shown good performance to improve classifications. In this paper, we first propose a unified objective to seamlessly accommodate trace ratio formulation and K-means clustering procedure, such that the trace ratio criterion is extended to unsupervised model. After that, we propose a novel unsupervised feature selection method by integrating unsupervised trace ratio formulation and structured sparsity-inducing norms regularization. The proposed method can harness the discriminant power of trace ratio criterion, thus it tends to select discriminative features. Meanwhile, we also provide two important theorems to guarantee the unsupervised feature selection process. Empirical results on four benchmark data sets show that the proposed method outperforms other sate-of-the-art unsupervised feature selection algorithms in all three clustering evaluation metrics.