微乳液聚合制备聚丙烯酰胺及聚N-异丙基丙烯酰胺超细微粒

研究了琥珀酸双（２－乙基己酯）磺酸钠（ＡＯＴ）、甲苯（Ｔ）、乙酰胺（ＥＭ）、丙烯酰胺（ＡＭ）、Ｎ－异丙基丙烯酰胺（ＮＩＰＡＭ）、Ｎ,Ｎ－亚甲基双丙烯酰胺（ＢＩＳ）、水组成的六种微乳液体系,以电导法和目视法测定了其相区临界点电导率,并绘制了各自的拟三元相图;以偶氮二异丁腈（ＡＩＢＮ）为引发剂,在微乳液中制备了聚丙烯酰胺及聚Ｎ－异丙基丙烯酰胺超细微粒．用ＩＲ谱对其进行了验证,以扫描电镜观察了其外部结构特征,测得微粒平均粒径为０．０５～０．１０μｍ．实验表明,可在微乳液中制备ＰＡＭ及ＰＮＩＰＡＭ超细微粒．     

双筒中心圆管振动磨

双筒中心圆管振动磨用于超细粉碎,利用研磨介质在做高频振动的筒体内对物料进行冲击、摩擦、剪切等作用而使物料粉碎。该设计在原有振动磨的基础上,在筒体中心安装中心圆管,以消除磨筒中央产生的惰性区,使磨介在一个振动周期内受到一次以上的冲击,增大了磨介对被磨材料的冲击能量。     

微乳液聚合聚N-异丙基丙烯酰胺超细微粒的结构表征

以偶氮二异丁腈为引发剂，在琥珀酸双（２乙基己酯）磺酸钠、甲苯、Ｎ异丙基丙烯酰胺、水组成的微乳液中，通过微乳液聚合制备了未交联及交联的聚Ｎ异丙基丙烯酰胺超细微粒．用扫描电镜、迎头色谱法及粘度法对其结构进行了表征．该超细微粒的颗粒平均粒径约０．１μｍ，未交联聚Ｎ异丙基丙烯酰胺粘均分子量为１．３８×１０６ｋｇ／ｍｏｌ，交联聚Ｎ异丙基丙烯酰胺超细微粒的内比表面积为４８．０４８ｍ２／ｇ，相对孔径分布为１～１０ｎｍ，气体最大吸附量为１７８４．８ｃｍ３／ｇ．     

乳浊液法制备超细Fe_2O_3粉体

FeSo_4·7H_2O为原料，用乳浊液法制备了平均粒径在12－18nm的超细球形Fe_2o)_3粉体，运用X射线衍射、电感耦合等离子体光谱、透射电镜等分析手段对粉体进行了表征。实验表明：用该法制备的产品中SO~(2-)_4含量小于0．01％，产品在有机溶剂中具有良好的分散性和透明性。     

半导体超微粒子气敏膜的研究

用气相法成功地研制出了 SnO_2超微粒子气敏膜。研究了0.03乇到0.1乇范围内不同氧分压下制得的超微粒子薄膜的酒敏特性和湿敏特性与成膜工艺、工作温度等关系。     

Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models

Real-time monitoring is necessary for nanoparticle exposure assessment to characterize the exposure profile, but the data produced are autocorrelated. This study was conducted to compare three statistical methods used to analyze data, which constitute autocorrelated time series, and to investigate the effect of averaging time on the reduction of the autocorrelation using field data. First-order autoregressive (AR(1)) and autoregressive-integrated moving average (ARIMA) models are alternative methods that remove autocorrelation. The classical regression method was compared with AR(1) and ARIMA. Three data sets were used. Scanning mobility particle sizer data were used. We compared the results of regression, AR(1), and ARIMA with averaging times of 1, 5, and 10?min. AR(1) and ARIMA models had similar capacities to adjust autocorrelation of real-time data. Because of the non-stationary of real-time monitoring data, the ARIMA was more appropriate. When using the AR(1), transformation into stationary data was necessary. There was no difference with a longer averaging time. This study suggests that the ARIMA model could be used to process real-time monitoring data especially for non-stationary data, and averaging time setting is flexible depending on the data interval required to capture the effects of processes for occupational and environmental nano measurements.     

桃皮绒织物服用性能研究

本文就目前国内现有的涤纶超细、微细、细旦、涤与棉交织、纯棉桃皮绒织物进行了较为全面的服用性能研究，并在性能分析的基础上结合服装成形的要求对涤纶桃皮绒织物进行了灰色聚类判别分析，为桃皮绒织物的设计提供参考依据。