Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Enhanced performance of polyvinylidene fluoride ultrafiltration membranes by incorporating TiO2/graphene oxide

Abstract: Aiming to enhance the water flux and anti-fouling performance of poly(vinylidene fluoride) (PVDF) ultrafiltration membranes, TiO2–GO/PVDF membranes were first fabricated via blending and immersion precipitation phase inversion. Following that, TiO2–GO nanocomposites grafted with different polyethylene glycol (PEG) were employed as the additives to prepare the novel PVDF hybrid membranes. FTIR spectroscope, SEM, zeta potentiometer, and static contact angle analyzer were employed to characterize the structure and surface properties of the hybrid membranes. Ultrafiltration was used to evaluate the membrane performance. Results showed that the interaction between GO and TiO2 led to the good distribution of TiO2–GO in the polymeric membrane, which increased the polarity and porosity of the hybrid membranes, thus increasing their water flux and anti-fouling performance. The maximum flux of hybrid membranes was four times that of the neat PVDF membrane. The PEG grafted on the TiO2–GO nanocomposites did not only act as a pore-forming additive, but also improved the hydrophilicity and porosity of the hybrid membrane due to the enhancement on the distribution and surface polarity of TiO2–GO. Therefore, both the flux and anti-fouling performance of PVDF membranes containing PEG-modified TiO2–GO were significantly higher than those of membranes including unmodified TiO2–GO.

Controlled synthesis of pentachlorophenol-imprinted polymers on the surface of magnetic graphene oxide for highly selective adsorption

Abstract: Correction for ‘Controlled synthesis of pentachlorophenol-imprinted polymers on the surface of magnetic graphene oxide for highly selective adsorption’ by Sheng-Dong Pan et al., J. Mater. Chem. A, 2014, 2, 15345–15356.

Fine-grained Iterative Attention Network for Temporal Language Localization in Videos

Abstract: Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. To tackle this task, designing an effective model to extract ground-ing information from both visual and textual modalities is crucial. However, most previous attempts in this field only focus on unidirectional interactions from video to query, which emphasizes which words to listen and attends to sentence information via vanilla soft attention, but clues from query-by-video interactions implying where to look are not taken into consideration. In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. Specifically, in the iterative attention module, each word in the query is first enhanced by attending to each frame in the video through fine-grained attention, then video iteratively attends to the integrated query. Finally, both video and query information is utilized to provide robust cross-modal representation for further moment localization. In addition, to better predict the target segment, we propose a content-oriented localization strategy instead of applying recent anchor-based localization. We evaluate the proposed method on three challenging public benchmarks: ActivityNet Captions, TACoS, and Charades-STA. FIAN significantly outperforms the state-of-the-art approaches.