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In this study, Nb modification was applied to promote the performance of selective catalytic reduction (SCR) of NOx and NH4HSO4 (ABS) resistance of pure silica molecular sieve KIT-6-supported copper nanoporous catalysts. Experimental results revealed that the optimal Cu5–NK sample possessed above 80% deNOx efficiency with good N2 selectivity in the temperature range of 260–400 °C and ABS deposition only reduced the SCR efficiency of the Cu5–NK sample by 20% at 320 °C. Characterization results indicated that Nb modification would induce formation of strong interactions with Cu, which enlarged pore size, amplified dispersion of active Cu species, and enhanced redox ability and surface acidity. Importantly, the enlarged pore size could weaken the thermal stability of ABS and promote its decomposition, while more active Cu sites retained by strong Cu–Nb interactions could participate in the SCR reaction and easily consume NH4+ from the deposited ABS on the catalyst. These were the main reasons for promoting SCR performances and ABS resistance of Cu-KIT-6 nanoporous catalysts by Nb modification. Such findings could pave a way for the development of highly efficient SCR catalysts with good ABS resistance for real application. [ABSTRACT FROM AUTHOR]

A mitochondria-targeted ratiometric fluorescent probe (Mito-Zn) was first designed and synthesized with dual emissions both located in the near-infrared region, for Zn2+ detection with high sensitivity and selectivity. By using the developed Mito-Zn, a high level of Zn2+ in the depressed mouse brain was discovered for the first time. [ABSTRACT FROM AUTHOR]

Quasi-bilayered actuators composed of Fe3O4-decorated graphene oxide and polyvinylidene fluoride have been fabricated in a magnetic field. The actuators could stably respond to multiple stimuli including infrared light, acetone vapour and a magnetic field. The actuator is also patternable because of the magnetism-induced spatial distribution of fillers in the matrix. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of leather defect detection, a leather defect detection algorithm based on improved bilateral filtering was proposed. Through constructing machine vision detection platform, different kinds of defects in the finished leather sample image were obtained, sample images were processed with improved bilateral filtering algorithms to make the leather background texture fuzzy and keep its defect edge profile. Then, various kinds of defects of four characteristic parameters were calculated as the input vector, and the automatic identification of least squares support vector machine (SVM) mode was constructed. The results showed that compared with cluster analysis algorithm, threshold segmentation algorithm and wavelet analysis algorithm, the algorithm adopted in this paper could detect various defects of leather more efficiently. The average detection time was 0.83s, and the accuracy of defect detection was 93. 3%. The results provide an effective way for real-time leather detection. [ABSTRACT FROM AUTHOR]