核壳结构聚合物乳液乳胶粒粒径及重量分布测试

对核壳结构聚合物乳液乳胶粒的粒径及其分布进行了测试,并与同一聚合物体系的其它乳液加以比较、分析,得出其粒径最小、分布最均匀的重要结论.     

2-氨基-5-甲基苯甲酸合成工艺的改进

对甲苯胺与水合氯醛、盐酸羟胺反应制得异亚硝基乙酰对甲苯胺,于浓硫酸中脱水成环得到5-甲基靛红,然后在碱性条件下以双氧水为氧化介质合成2-氨基-5-甲基苯甲酸。通过正交实验得到了一、二步反应的较佳工艺条件,三步反应总收率以对甲苯胺计算达61.7%。通过FTIR和1HNMR对最终产品进行了表征。     

焦炉加热吸力控制模型及应用

依据焦炉正常生产条件下的系统压力分布,利用焦炉内气流流动方程,建立了燃烧系统参数与各压力控制点的模型。通过计算机仿真与实际结果比较,该模型能够较好的反映焦炉内气体流动规律,对吸力控制具有较好的理论指导意义。     

半透明氮化硅陶瓷的放电等离子烧结制备及性能

用放电等离子烧结(spark plasma sintering,SPS)技术,以质量分数(下同)为9%氮化铝(A1N),3%氧化镁(MgO)为烧结助剂,在1850℃烧结5min,成功制备了半透明氮化硅(Si3N4)陶瓷.半透明Si3N4陶瓷在中红外波段表现出良好的透过率,最大透过率为66.4%.SPS的快速致密化过程保证了烧结体具有良好的晶体结构,有利于提高透过率.SPS快速的烧结过程和A1N和MgO的加入能够有效抑制烧结过程中Si3N4陶瓷由α相向β相的转变,是制备光学性能良好的Si3N4陶瓷的关键.报道了半透明Si3N4陶瓷的其他性能.光学性能与其他性能的结合,势必大大拓宽Si3N4陶瓷的应用领域.     

甲基丙烯酸三氟乙酯/甲基丙烯酸缩水甘油酯共聚物乳液的制备

以甲基丙烯酸三氟乙酯、丙烯酸丁酯、甲基丙烯酸甲酯、甲基丙烯酸缩水甘油酯和苯乙烯为基本原料,用半连续滴加的方法制备了共聚物乳液。FT-IR证明了所用单体甲基丙烯酸三氟乙酯参与了共聚反应。讨论了氟单体用量对转化率的影响。讨论了乳胶膜的吸水率的影响因素。     

邻硝基苯甲醚液相加氢制邻氨基苯甲醚的研究

对邻硝基苯甲醚液相加氢还原制邻氨基苯甲醚的工艺进行了研究。试验结果表明,加氢还原工艺在产品质量、收率及经济效益方面都优于传统还原工艺。     

降压新药西尼地平杂质Ⅰ的制备

以西尼地平为起始原料,活化MnO2为氧化剂,一步法制备了2,6-二甲基-4-(3-硝基苯基)吡啶-3,5-二甲酸3-(2-甲氧基)乙酯5-(3-苯基)-2(E)-丙烯酯(记为西尼地平杂质Ⅰ).经实验优化,制备西尼地平杂质Ⅰ的最适宜条件为:m(西尼地平):m(MnO2)=1.00:1.24,甲苯为溶剂,105℃下反应1 h.反应得到西尼地平杂质Ⅰ收率为92.3％,经高效液相色谱面积归一化法检测后,纯度达到99.2％.产物结构经HRMS,1HNMR、13CNMR以及FTIR进行确认.该反应实现了西尼地平绿色高效直接转化西尼地平杂质Ⅰ.     

载锰氮化碳催化臭氧法降解水中草酸研究

草酸是废水中多种有机物氧化降解的中间产物,与臭氧反应速率很低,难以直接氧化去除。本研究通过热解法煅烧不同摩尔比例的三聚氰胺与醋酸锰(Ⅲ)二水合物,制备出不同负载量的载锰氮化碳催化剂,并研究了不同条件下载锰氮化碳催化臭氧法对草酸的降解效果,考察三聚氰胺与醋酸锰摩尔比、催化剂投加量、p H值、臭氧浓度等因素以及叔丁醇对草酸去除效果的影响。实验结果表明:相对于单独臭氧氧化而言,载锰氮化碳催化臭氧法可以极大的提高草酸的去除率,在投加0.15 g/L三聚氰胺与醋酸锰20∶1的催化剂后,草酸在60 min的TOC去除率可达到89.1%。叔丁醇加入对草酸的去除率影响较小,表明·OH在载锰氮化碳催化氧化草酸的过程中起作用但不是唯一因素。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

石墨烯基气凝胶的制备及油吸附性能研究进展

海上漏油的频繁发生以及采油废水、工业含油污水的大量排放造成水资源大片污染和生态系统平衡的严重破坏。目前，从水体中分离油品和有机污染物已受到越来越多的商业和学术的关注。石墨烯基气凝胶是由二维石墨烯片层组装成的三维宏观材料，因其孔隙率高、比表面积大、密度低、机械性能强等特点在油水分离领域具有广阔的应用前景，已成为当今的研究热点之一。本文结合最新研究进展系统地总结了石墨烯基气凝胶的结构设计、组装及干燥方法，归纳了近年来其在油水分离中的应用进展，并对石墨烯基气凝胶在油水分离领域的研究现状和未来研究方向做了简要评述，以期为该领域的深入探索提供新的视角。