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.     

A mixed collector system for phosphate flotation

Flotation has been used in industry for more than a half century as the primary technique for upgrading phosphate. While the flotation of phosphate was inefficient when oleic acid was used alone as a collector, therefore a mixed collector of oleic acid (HOl), linoleic acid (LA) and linolenic acid (LNA) was employed to improve the recovery of phosphate flotation. The batch flotation results showed that the optimal composition of the mixed collector was 54 wt.% HOl, 36 wt.% LA and 10 wt.% LNA. Additionally, the effect of pH on the mixed collector application was studied while considering the surface tension, contact angle and micro-flotation. The results showed that the mixed collector should be used at a pH of 9.5. Above a pH of 9.5, the adsorption of fatty acids dimers on the apatite surface hindered phosphate flotation. The influence of the mixed collector assembly on apatite flotation was also investigated. It was demonstrated that due to its low critical micelle concentration, a sufficiently hydrophobic apatite surface could be generated at a collector concentration of 60 mg/L. In addition, zeta potential experiments suggested that collector adsorption was governed by chemisorption. FTIR and XPS spectra studies further indicated that the chemical reaction involved the carboxyl groups of fatty acids and Ca species at the apatite surface for each fatty acid in the mixed collector.     

A Multiscale-Porous Anion Exchange Membrane for Convenient and Scalable Electrokinetic Concentration of Cationic Species

A device able to electrokinetically concentrate cationic samples has many potential medical and industrial applications, but until now has remained undeveloped due to the lack of a commercial anion-permselective material leading to a prohibitively complex fabrication procedure. Herein, a novel multiscale-porous anion exchange membrane (MP-AEM) that enables the convenient and scalable electrokinetic concentration of cationic species is proposed. A mechanically enhanced multiscale-porous structure with a solid framework is realized by adopting polyester resin as an additive to overcome the intrinsic limitations of the AEM material. The scalable MP-AEM-embedded electrokinetic concentrator is devised based on the peculiar properties of the MP-AEM that for allow both ion and fluid transport. With the MP-AEM, the concentrator is fabricated in a highly streamlined manner consisting only of a simple insertion and assembly. The concentration performance of the MP-AEM-embedded electrokinetic concentrator is demonstrated with a positively charged fluorescent dye and a fluorescein-labeled protein, and the results show enrichment factors of 250 and 500, respectively. The MP-AEM makes cationic electrokinetic concentration more accessible and scalable, thereby enabling further progress in a wide range of fields.     

基于门控联合池化自编码器的通用性文本表征

为了学习文本的语义表征,以往的研究者主要依赖于复杂的循环神经网络(recurrent neural networks, RNNs)和监督式学习方法。该文提出了一种门控联合池化自编码器(gated mean-max AAE)用于学习中英文的文本语义表征。该文的自编码器完全通过多头自注意力机制(multi-head self-attention mechanism)来构建编码器和解码器网络。在编码阶段,提出了均值—最大化(mean-max)联合表征策略,即同时运用平均池化(mean pooling)和最大池化(max pooling)操作来捕获输入文本中多样性的语义信息。为促使联合池化表征可以全面地指导重构过程,解码器采用门控操作进行动态关注。通过在大规模中英文未标注语料上训练模型,获得了高质量的句子编码器。在重构文本段落的实验中,该文模型在实验效果和计算效率上均超越了传统的RNNs模型。将公开训练好的文本编码器,使其可以方便地运用于后续的研究。     

古建筑木结构透榫节点力学模型研究

为研究古建筑木结构透榫节点的M-θ力学模型，在分析透榫节点构造特征与受力机理的基础上，建立其数值模型，用透榫节点的试验数据验证了该数值模型的正确性，并分析了节点缝隙、木材横纹弹性模量和大榫头长度对透榫节点受弯承载力的影响。根据受力分析结果，建立以弹性点、屈服点与极限点为特征点的三折线多参数M-θ力学模型，其结果与多数的试验结果基本吻合，并将该力学模型应用于木构架的受力分析。研究结果表明：透榫节点的滞回耗能能力强，节点的变形主要集中在榫头处。当榫头与卯口之间的缝隙增大时，节点的受弯承载力降低。随木材横纹弹性模量的提高和大榫头长度的增加，节点的受弯承载力有一定提高。文章建立的M-θ力学模型能较好反映透榫节点的受力过程，适用于木构架的受力分析，其荷载 位移骨架曲线与试验结果基本吻合。研究成果可为古建筑木结构的维修与保护提供参考。     

Linear friction welding of Ti6Al4V: experiments and modelling

Linear friction welding of the Ti6Al4V alloy is studied. A new definition of the energy input rate is proposed, based on an integration over time of the in-plane force and velocity; a strong correlation with the upset rate is then found. The effective friction coefficient is estimated to be 0·5±0·1 for varying frequencies and amplitudes, with only a weak dependence on the processing conditions displayed. A model is proposed that accounts for both the conditioning and equilibrium stages of the process, which is shown to be in good agreement with the experimental data. The model is used to study the mechanism by which the flash is formed. A criterion is proposed by which the rippled nature of its morphology can be predicted.     

Al2O3/Yttria‐Stabilized Zirconia Hollow‐Fiber Membrane Incorporated with Iron Oxide for Pb(II) Removal

Removal by absorptive ceramic membranes can simultaneously absorb and separate metal ions from water. Alumina/yttria‐stabilized zirconia (Al₂O₃/YSZ) hollow‐fiber membranes, fabricated using phase inversion and sintering process, were deposited with iron oxide by an in‐situ hydrothermal process. The results showed that α‐Fe₂O₃ was produced and incorporated across the membranes. A reduction in flux was recorded with the deposition of α‐Fe₂O_3. However, it improved the adsorption capacity for heavy metal adsorption. The adsorption‐separation test demonstrated that the optimized membrane is able to completely remove Pb(II) ions after two hours.     

叠合盆地凸起区多期复杂断裂特征及形成机制——以准噶尔盆地车排子凸起为例

以高精度三维地震资料为基础,结合构造演化恢复和区域构造背景,系统研究了准噶尔盆地车排子凸起断裂分布和形成机制。结果表明,车排子凸起发育深部和浅部2套断裂体系,深部断裂体系和浅部断裂体系分别发育3种断裂样式。车排子凸起共经历5个构造演化阶段,其中,晚二叠世挤压-逆冲控制了深部断裂体系的初始形成,晚侏罗世逆冲-走滑控制了深部断裂体系的最终定型,新近纪叠加掀斜、局部伸展控制了浅部断裂体系的形成。不同断裂体系在油气成藏过程中均起到了重要作用。其中:深部断裂体系的红车断裂是重要的油源断裂,低序级断层控制形成了石炭系火山岩优质储集体;浅部断裂体系使得油气向浅部层系运移调整,同时形成了断块圈闭、断鼻圈闭和断层-岩性圈闭等丰富的圈闭类型。     

Isolation of the effect of the hairy layer length on the mechanical properties of waterborne coatings

The effect of the acidic hairy layer length on the interdiffusion of polymer between particles and as a consequence on the mechanical properties of the films produced from waterborne coatings has been studied. In order to isolate this effect, latexes with the same particle diameter and molecular weight but stabilized with poly(acrylic acid)-block-poly(butyl acrylate) (PAA-b-PBA) block copolymers of controlled and different lengths were prepared. Tensile strength measurements showed at the macroscopic level that the presence of AA chains in the particle surface reduced the mechanical properties of the films dried at room temperature, being its effect worse the longer the AA chain length. Higher annealing temperatures erased the negative effect of the acidic hairy layer on mechanical properties. The neutralization with NaOH instead of with NH₄OH also led to worse mechanical properties. These macroscopic results were supported by Fluorescence Resonance Energy Transfer (FRET) experiments that showed that at the microscopic level, the extent of interdiffusion occurred slower when the AA chains in the particles surface increased, the annealing temperature was lower and when NaOH was used as neutralizing agent instead of NH₄OH.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。