Microstructure and damage evolution of SiCf/PyC/SiC and SiCf/BN/SiC mini-composites: A synchrotron X-ray computed microtomography study

SiC_f/PyC/SiC and SiC_f/BN/SiC mini-composites comprising single tow SiC fibre-reinforced SiC with chemical vapor deposited PyC or BN interface layers are fabricated. The microstructure evolutions of the mini-composite samples as the oxidation temperature increases (oxidation at 1000, 1200, 1400, and 1600?°C in air for 2?h) are observed by scanning electron microscopy, energy dispersive spectrometry, and X-ray diffraction characterization methods. The damage evolution for each component of the as-fabricated SiC_f/SiC composites (SiC fibre, PyC/BN interface, SiC matrix, and mesophase) is mapped as a three-dimensional (3D) image and quantified with X-ray computed tomography. The mechanical performance of the composites is investigated via tensile tests.The results reveal that tensile failure occurs after the delamination and fibre pull-out in the SiC_f/PyC/SiC composites due to the volatilization of the PyC interface at high temperatures in the air environment. Meanwhile, the gaps between the fibres and matrix lead to rapid oxidation and crack propagation from the SiC matrix to SiC fibre, resulting in the failure of the SiC_f/PyC/SiC composites as the oxidation temperature increases to 1600?°C. On the other hand, the oxidation products of B₂O₃ molten compounds (reacted from the BN interface) fill up the fracture, cracks, and voids in the SiC matrix, providing excellent strength retention at elevated oxidation temperatures. Moreover, under the protection of B₂O₃, the SiC_f/BN/SiC mini-composites show a nearly intact microstructure of the SiC fibre, a low void growth rate from the matrix to fibre, and inhibition of new void formation and the SiO₂ grain growth from room to high temperatures. This work provides guidance for predicting the service life of SiC_f/PyC/SiC and SiC_f/BN/SiC composite materials, and is fundamental for establishing multiscale damage models on a local scale.     

Research on hydrophobicity of electrospun Fe3O4/PVDF nanofiber membranes under different preparation conditions

Abstract

Preparation condition can affect the structure and the properties of nanofiber membrane. In order to explore suitable conditions to prepare the Fe₃O₄/PVDF nanofiber membrane with good hydrophobicity, the hydrophobicity of Fe₃O₄/PVDF nanofiber membranes obtained by electrospinning was investigated by changing preparation conditions like weight percentage of Fe₃O₄ nanoparticles, blending quality concentration of poly (vinylidene fluoride) (PVDF) and Fe₃O₄ nanoparticles, and positive voltage. And the variations of hydrophobicity of Fe₃O₄/PVDF nanofiber membranes modified by 1H, 1H, 2H, 2H-perfluorodecyl trimethoxysilane were studied. The results show that the hydrophobicity of Fe₃O₄/PVDF nanofiber membranes has changed under different preparation conditions. The contact angles of samples increased after a modification by 1H, 1H, 2H, 2H-perfluorodecyl trimethoxysilane, which indicates that the hydrophobicity of Fe₃O₄/PVDF nanofiber membranes has been enhanced.     

Semantic word shifts in a scientific domain

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.     

Do funded papers attract more usage?

Research funding has been seen as one of the most important resource in the reward system of science. And usage of publications denotes an interesting perspective of user behavior in scientific communication. This study aims to address the relationship between funding and Usage Count, which is a new metrics item established on the platform of Web of Science. Full records of 300,010 articles published in 2013 were downloaded in October 2015, and divided into six disciplines, including information science library science, education educational research, economics, computer science, materials science, and chemistry. Seven indicators were proposed to measure the impact, including Funding rate, Citation per paper, Usage rate, Usage per paper, Citation difference, Usage difference, and Conversion rate. It concluded funding has impact on usage and citation, and funded papers attract more usage, but varying in different disciplines. Usage Count can be used in the extension of citation metrics but with limits. This study originally engages with usage metrics and detected that there is positive correlation between usage and funding.     

A multi-criteria context-sensitive approach for social image collection summarization

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.