半胱氨酸亚金铵的合成、理化性质及电镀金应用

以半胱氨酸为配体合成一种新型亚金配合物NH4Au(Cys)2,对该配合物进行元素分析、红外光谱、紫外光谱、热失重分析和导电性测量等理化性质研究;以该亚金配合物为金源开展相关的电镀金工艺探索,并通过四因素三水平的正交试验获得其最佳条件参数;采用扫描电子显微镜(SEM)和X线衍射(XRD)对镀金层的表面质量进行探讨。研究结果表明:该目标产物的分子式为NH4Au(Cys)2·2H2O,该配合物中以半胱氨酸的巯基和金配位为成健特征,在170℃以下热稳定性较好,该亚金配合物是一个典型的离子化合物。在电流密度为200~300 A/m2,p H为10.5~12.0,温度为35~45℃,金质量浓度为15~25 g/L的电镀工艺条件下,得到粒度为0.5~1.0μm的单质金,且主要沿着(111)面进行生长。     

Improved Classification for Compositional Data Using the <Emphasis Type="Italic">α</Emphasis>-transformation

In compositional data analysis, an observation is a vector containing nonnegative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centers on the idea of using the α-transformation to transform the data and then to classify the transformed data via regularized discriminant analysis and the k-nearest neighbors algorithm. Using the α-transformation generalizes two rival approaches in compositional data analysis, one (when α=1) that treats the data as though they were Euclidean, ignoring the compositional constraint, and another (when α = 0) that employs Aitchison’s centered log-ratio transformation. A numerical study with several real datasets shows that whether using α = 1 or α = 0 gives better classification performance depends on the dataset, and moreover that using an intermediate value of α can sometimes give better performance than using either 1 or 0.     

随机纳米碳管网络及其渗流性质

数值模拟了实验上构造纳米碳管网络的溶液沉积方法.与一般的随机网络模型不同,将碳管的长度计算在内,而且考虑了不同的空间相交位形.数值模拟发现网络的度分布为高斯分布,平均集聚系数约为0.11.当网络中碳管平均面密度取值在σ0=179 200根/cm2附近时,网络系综达到渗流.在临界点附近,网络的连通概率p、两极之间电导G、...