Microstructure,Mechanical and Electrical Properties of Hybrid Copper Matrix Composites with Fe Microspheres and rGO Nanosheets

Copper matrix composites have a wide application as magnetic conductive materials, electromagnetic materials, electrical discharge machining materials, etc. Such materials are expected to have a good combination of excellent electrical conductivity and good mechanical strength. In this work, micro/nano hybrid reinforcements with Fe microspheres and reduced graphene oxide (rGO) nanosheets were developed for copper matrix composites. The rGO/Fe/Cu powders were firstly wet-mixed and then densified by the vacuum hot-pressing sintering to obtain the bulk compacts. Microstructure, electrical conductivity and mechanical properties of such compacts were investigated. Microstructural result of as-sintered compacts shows that the Fe microspheres could distribute in the matrix uniformly, and rGO nanosheets exhibit both agglomerated and dispersed states. The grain size of Cu matrix decreased with the increase of the rGO content. Hardness, compression and tensile 0.2% yield strength of the as-sintered compacts were improved evidently by the addition of the hybrid Fe/rGO, comparing with pure Cu and single Fe-added composites. However, a lower electrical conductivity appeared in the more rGO-added composites, but still reached more than 33.0% international annealing copper standard (IACS). These performance change could be sought in the spatially geometrical distribution and characteristics of such micro/nano Fe/rGO hybrid addition, and the relevant mechanisms were discussed.     

融合分布对齐和对抗学习的无监督跨域声纹识别

针对声纹识别领域不匹配,且目标领域缺少标注数据的难题,提出在对抗学习基础上融合分布对齐的无监督领域自适应方法,通过训练过程中统计分布的对齐,以减小领域差异,从而提取声音中更有声纹鉴别性的特征,取得了稳定的性能提升。在文本相关的声纹识别任务中,对抗学习和分布对齐的方法能协同发挥作用,等错率相对降低11%;在文本无关的任务中,对抗学习效果不稳定,而分布对齐的方法依然有相对8%的性能提升。实验结果证明该方法在领域不匹配且目标领域缺少标注数据时,能有效提取语音中声纹鉴别信息,稳定提升识别性能。     

Fabrication of highly porous starch monoliths and their application as green desiccants

Starch monoliths with oriented porous structures were developed from starches and glycerin as a plasticizer using unidirectional freeze‐drying technology. The morphology of the porous starch monoliths made from different starches was determined by scanning electron microscopy (SEM), and the porosity, mechanical properties and moisture absorption of the porous materials were evaluated. The results show that porous starch monoliths made from oxidized starch with 15% glycerin had good mechanical strength with the best moisture absorption. Increasing the starch concentration significantly improved their strength and increased the moisture absorption rate as well. The moisture absorption of the porous monolith made from oxidized starch is comparable with commonly used desiccants. The porous starch monoliths can successfully remove water from 95% ethanol to produce absolute ethanol due to their selective absorption for water. Copyright © 2011 John Wiley & Sons, Ltd.     

基于衍射波的孔类缺陷超声相控阵定量方法研究

针对常规超声检测方法对小孔型缺陷难以准确定量的问题,利用超声相控阵的声束偏转聚焦和成像技术,基于声波在体积缺陷处的衍射特性,提出采用一次反射回波和衍射回波来确定体积缺陷的直径尺寸。以横孔模拟孔型缺陷,利用动态光弹技术对声波入射横孔的传播规律进行研究,并采用超声相控阵对孔径为Φ1-Φ4mm不同埋深以及相同埋深下Φ0.8-Φ4.8mm的等差横孔进行了试验。研究结果表明利用本文提出的定量方法能快速有效的确定孔型缺陷直径尺寸,实际计算值与理论值误差较小,对相控阵的定量问题研究具有一定的参考价值。     

脯氨酸催化新型3-α,β-不饱和酮取代-4-羟基喹啉-2-酮简便合成及其荧光性质研究

3-乙酰-4-羟基喹啉-2-酮和芳香醛在L-脯氨酸有效催化下发生Aldol反应,生成一系列缩合产物3-α,β-不饱和酮取代-4-羟基喹啉-2-酮.该反应在甲醇溶液中进行,并且产物作为沉淀直接从溶液中析出,简单洗涤可得纯产物.研究表明芳香醛上的取代基性质对产物的产率有显著影响,其结果证实了该缩合反应经历了一个脱水中间体的反应历程.此外,对产物的荧光性质研究表明该类化合物可作为潜在应用价值的有机光学材料.     

采用钯基体改进剂石墨炉原子吸收法测定纺织品中的可萃取铅

纺织品标准中对铅的限量极为严格,但萃取汗液基体成分复杂,背景吸收干扰强烈,为解决纺织品中的可萃取铅检测的困难,建立了一种采用基体改进剂的石墨炉原子吸收光谱法,测定纺织品中的可萃取铅。通过比较两种基体改进剂,优化实验条件,确定了以钯-硝酸镁为基体改进剂、灰化温度1 200℃、原子化温度2 300℃的实验条件。结果表明,以钯-硝酸镁为基体改进剂更能有效降低纺织品可萃取铅的基体干扰,并且具有良好的精密度(2.6%~3.3%)和准确度(加标回收率94.3%~105.6%)。     

不同树脂对瓜果腐霉代谢产物中除草活性成分的吸附分析

利用HPD400、HPD500、HPD600、HPD700、HPD850、ADS-17、D101、DM130大孔吸附树脂对瓜果腐霉培养滤液中除草活性成分的吸附情况进行了研究,通过对树脂吸附后的流出液和洗脱液的浓缩物进行HPLC检测分析,确定了其除草活性成分。实验发现,在所选择的8种树脂中以HPD500、HPD850、HPD600、ADS-17对除草活性成分的吸附能力最强,其中以HPD500和HPD600树脂的10%的乙醇洗脱液中除草活性成分含量最高。综合结果表明,以HPD500树脂作为吸附瓜果腐霉培养滤液中除草活性成分的适宜树脂。研究发现,HPD500树脂对除草活性成分吸附和解吸附的最佳条件是:吸附最适温度为20℃,树脂吸附饱和度为33.75mL/g,较适宜吸附流速为2BV/h,较适宜脱附流速为1BV/h,洗脱剂浓度为10%乙醇,洗脱剂用量为2.5BV。     

Flame retardant and mechanism of vinyl ester resin modified by octaphenyl polyhedral oligomeric silsesquioxane

Flamability is one of the major issues for utilization of bisphenol A–type vinyl ester resin (VER) polymeric composites in practical applications. The thermal stability and mechanical property of VER composites containing octaphenyl polyhedral oligomeric silsesquioxane (OPS) were investigated and discussed in detail. With increasing the mass ratio of OPS, the residues yield at 800°C and bending strength at break of the VER/OPS composites were enhanced, accompanied with the gradually decreased values of the peak heat release rate, total heat release, and total smoke release due to the formation of dense carbon/silica protective layers that acted as a barrier to heat and mass transfer. In addition, the flame‐retardant mechanisms of condensed phase and gas phase were analyzed by XPS, TGA‐FTIR, and GC‐MS. The results showed that when OPS was incorporated to the resin matrix, the characteristic peaks intensity of the gaseous products was reduced obviously due to some of the characteristic groups still retained in condensed phase. Therefore, the significance of this work is providing an optional method to fabricate flame‐retarding VER composites with excellent mechanical properties.     

Mechanical and thermal properties and flame retardancy of phosphorus-containing polyhedral oligomeric silsesquioxane (DOPO-POSS)/polycarbonate composites

A novel polyhedral oligomeric silsesquioxane containing 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO-POSS) has been incorporated into polycarbonate (PC) composites in order to study its effect on mechanical and thermal properties and flame retardancy. The mechanical and thermal properties of the DOPO-POSS/PC composites have been investigated by tensile and flexural testing, DSC, and DMA. Slight enhancements of yield stress, and flexural strength and modulus, and obvious decreases of fracture strength and strain of the DOPO-POSS/PC composites were observed with an increase in DOPO-POSS loading. The glass transition temperatures (T_g) of the composites were reduced with increasing DOPO-POSS loading. The morphology of the PC composites was evaluated by SEM, which indicated that the DOPO-POSS was dispersed with a particle size of 100-250 nm in the PC matrix. The thermal degradation behaviour and flame retardancies of PC composites with different DOPO-POSS loadings were investigated by TGA, LOI, UL-94 standard, and cone calorimetry. The composite had an LOI value of 30.5 and a UL-94 rating V-0 when the content of DOPO-POSS was 4%.     

An Appraisal of Incremental Learning Methods

As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data. It is expected to have the ability of memorization and it is regarded as one of the ultimate goals of artificial intelligence technology. However, incremental learning remains a long term challenge. Modern deep neural network models achieve outstanding performance on stationary data distributions with batch training. This restriction leads to catastrophic forgetting for incremental learning scenarios since the distribution of incoming data is unknown and has a highly different probability from the old data. Therefore, a model must be both plastic to acquire new knowledge and stable to consolidate existing knowledge. This review aims to draw a systematic review of the state of the art of incremental learning methods. Published reports are selected from Web of Science, IEEEXplore, and DBLP databases up to May 2020. Each paper is reviewed according to the types: architectural strategy, regularization strategy and rehearsal and pseudo-rehearsal strategy. We compare and discuss different methods. Moreover, the development trend and research focus are given. It is concluded that incremental learning is still a hot research area and will be for a long period. More attention should be paid to the exploration of both biological systems and computational models.