YNbO4：Tm3+/Yb3+上转换发光特性研究

按照50Nb₂O₅-（46-x）Y₂O₃-4Yb₂O₃-xTm₂O₃（x=0.1,0.2,0.5,1,2）的配比方式,采用高温固相法制备出了掺杂Tm³⁺/Yb³⁺的YNbO₄晶体粉末。在980 nm红外光激发下,观测到波长为478,645,707 nm的上转换荧光,分别对应于Tm³⁺离子的¹G₄→³H₆、¹G₄→³F₄、³F₃→³H₆能级跃迁过程。利用上转换发射功率与980 nm激光器工作电流关系估算出跃迁过程吸收光子数目为2.72,2.69,2.01,从而确定出前两者为三光子吸收过程,最后一个对应于双光子吸收过程。运用Judd-Ofelt理论研究样品光谱特性,根据样品的吸收谱得到样品的谱线强度参数Ω_t（t=2,4,6）,进而得出理论振子强度及实验振子强度,二者均方根偏差δ_rms=1.299×10^-7。计算了Tm³⁺离子向下能级跃迁的跃迁几率、跃迁分支比等参数。最后得出结论：（1）³F₄能级寿命较长,适合作为上转换中间能级;（2）³H₅能级寿命较长,且³H₅→³H₆跃迁分支比（96.46%）接近100%,可用于产生1 216 nm激光。     

改进的用于AMOLED的移位寄存器的设计与仿真

p-type技术能够简化低温多晶硅TFT的制作过程。基于这个原因,本文提出了一种改进的利用p-type多晶硅TFT构成的移位寄存器结构,它可以作为AMOLED或者AMLCD外围驱动电路的一部分被集成到显示屏的衬底上。改进的移位寄存器由6个时钟控制,每个单元包含6个p-type多晶硅TFT。本文利用HSPICE进行仿真,验证了电路功能的正确性。利用韩方在多晶硅制作方面的优势,已经利用以上技术完成了96×3×128的AMOLED显示屏的制作。     

由一个特勒根定理例题的求解而引发的思考

本文从参考文献[2]中一实例受到启发,将未知网络转换成一具体例题证明了内部含受控源时利用特勒根第二定理解题会出错,由此推断出不能冒然用特勒根定理。所以我们在命题时,如果笼统说成是电阻性网络是不够明确的,而应明确告知网络内部仅由电阻组成。     

三元无规接枝共聚物的合成及其自组装性能的研究

通过甲基丙烯酸羟乙酯(HEMA)引发ε-己内酯(ε-CL)开环聚合得到带有双键的大分子预聚体甲基丙烯酸羟乙酯-聚己内酯(HEMA-PCL), 该预聚体与N-异丙基丙烯酰胺(NIPAAm)及丙烯酸(AAc)自由基聚合得到一系列含有不同比例组分的三元无规接枝共聚物. 研究了该聚合物的自组装性能. 通过1H NMR, FTIR, 凝胶渗透色谱(GPC)对聚合物进行结构和分子量的表征. 通过TEM, DLS与表面张力等方法表征其纳米粒子情况.     

光纤Fabry—Perot干涉式温度测量

一种新型的光纤温度传感器，基于Fabry-Peror(F-P)白光干涉原理，提出可对温度进行绝对测量，并具有稳定性高，抗干扰能力强的特点，分辨率达到0.01℃。     

艾儒棣教授辨治湿疹用药经验*

摘 要:艾儒棣教授师从于蜀中疡医大师文琢之教授，从事中医外科学临床工作40余年，累积了极为丰富的临床经验。艾儒棣教授在临床治疗湿疹中常内外并治、肝脾并治，用药常配伍皮类和芳香类中药，临床疗效确切。现总结艾儒棣教授治疗湿疹辨治用药经验，并附验案一则。     

A Multi-Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition

Density peak clustering (DPC) can identify cluster centers quickly,without any prior knowledge.It is supposed that the cluster centers have a high density and large distance.However,some real datasets have a hierarchical structure,which will result in local cluster centers having a high density but a smaller distance.DPC is a flat clustering algorithm that searches for cluster centers globally,without considering local differences.To address this issue,a Multi-granularity DPC (MG-DPC) algorithm based on Variational mode decomposition (VMD) is proposed.MG-DPC can find global cluster centers in the coarse-grained space,as well as local cluster centers in the fine-grained space.In addition,the density is difficult to calculate when the dataset has a high dimension.Neighborhood preserving embedding (NPE) algorithm can maintain the neighborhood relationship between samples while reducing the dimensionality.Moreover,DPC requires human experience in selecting cluster centers.This paper proposes a method for automatically selecting cluster centers based on Chebyshev's inequality.MG-DPC is implemented on the dataset of load-data to realize load classification.The clustering performance is evaluated using five validity indices compared with four typical clustering methods.The experimental results demonstrate that MG-DPC outperforms other comparison methods.