Ag+掺杂的立方相Y2O3:Eu纳米晶体粉末发光强度研究

采用化学自燃烧法制备了不同Ag⁺掺杂浓度的Y₂O₃:Eu纳米晶体粉末样品(［Y³⁺］∶［Eu³⁺］∶［Ag⁺］=99∶1∶X，X=0—3.5×10^-2)，以及通过退火处理得到了相应的体材料.根据X射线衍射谱确定所得纳米和体材料样品均为纯立方相.实验表明在纳米尺寸样品中随着Ag离子浓度的增加，荧光发射强度随之增加，当X=2×10^-2时达到最大值，其发光强度比X=0时提高了近50%.当Ag离子浓度继续增加，样品发光强度保持不变.在相应的体材料样品中则没有观察到此现象.通过对各样品的发射光谱，激发光谱，X射线衍射图谱，透射电镜(TEM)照片和荧光衰减曲线的研究，分析了引起纳米样品荧光强度变化的原因是由于Ag离子与表面悬键氧结合，从而使这一无辐射通道阻断，使发光中心Eu³⁺的量子效率提高；Ag⁺的引入所带来的另一个效应是使激发更为有效.这两方面原因使发光效率得到了提高.     

Yb3+掺杂KY(WO4)2激光晶体生长、结构与光谱分析

采用顶部籽晶提拉法(TSSG)生长出Yb:KY(WO₄)₂(Yb:KYW)激光晶体.对预烧后的原料及晶体进行了XRD分析，结果表明，分别在920℃和600℃预烧8h后的熔质和助熔剂基本上形成一相，抑止了实验中的挥发问题；所生长的晶体为β-Yb:KYW，计算其晶格常数为a=1.063nm，b=1.034nm，c=0.755nm，β=130.75°.测得不同厚度样品的吸收光谱，结果表明样品在933nm和981nm有较强的吸收峰，计算出主峰981nm的吸收截面σ关键词： Yb:KYW TSSG法 晶体结构 光谱参数     

Experimental intensity patterns obtained from a 2D shearing interferometer with adaptable sensitivity

The interferometric intensity patterns from a 2D shearing interferometer are shown and discussed. The intensity patterns can be obtained in two different approaches incorporating differential and extended wavefront controlled displacements. The reliable directional sensitivity of this interferometer allows the optimization of the measurement parameters to estimate the wavefront of the intensity patterns by regularization techniques.     

Angle theorems for the Lagrangian mean curvature flow

We prove that symplectic maps between Riemann surfaces L, M of constant, nonpositive and equal curvature converge to minimal symplectic maps, if the Lagrangian angle for the corresponding Lagrangian submanifold in the cross product space satisfies . If one considers a 4-dimensional K?hler-Einstein manifold of nonpositive scalar curvature that admits two complex structures J, K which commute and assumes that is a compact oriented Lagrangian submanifold w.r.t. J such that the K?hler form w.r.t.K restricted to L is positive and , then L converges under the mean curvature flow to a minimal Lagrangian submanifold which is calibrated w.r.t. . Received: 11 April 2001 / Published online: 29 April 2002     

Multicriteria decision making under uncertainty

Formalization for problems of multicriteria decision making under uncertainty is constructed in terms of guaranteed and weak estimates. A relevant definition of the vector maximinimax value is given. Parameterization and approximation of maximum, minimax, and maximinimax values based on the inverse logical convolution are suggested. An application for multicommodity networks is considered. Received: December 13, 2000 / Accepted: August 21, 2001?Published online May 8, 2002     

Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices

In this paper, the influence functions and limiting distributions of the canonical correlations and coefficients based on affine equivariant scatter matrices are developed for elliptically symmetric distributions. General formulas for limiting variances and covariances of the canonical correlations and canonical vectors based on scatter matrices are obtained. Also the use of the so-called shape matrices in canonical analysis is investigated. The scatter and shape matrices based on the affine equivariant Sign Covariance Matrix as well as the Tyler's shape matrix serve as examples. Their finite sample and limiting efficiencies are compared to those of the Minimum Covariance Determinant estimators and S-estimator through theoretical and simulation studies. The theory is illustrated by an example.     

On the realization of Riemannian symmetric spaces in Lie groups

In this paper we give a realization of some symmetric space G/K as a closed submanifold P of G. We also give several equivalent representations of the submanifold P. Some properties of the set gK∩P are also discussed, where gK is a coset space in G.     

Conditional independence models for seemingly unrelated regressions with incomplete data

We consider normal ≡ Gaussian seemingly unrelated regressions (SUR) with incomplete data (ID). Imposing a natural minimal set of conditional independence constraints, we find a restricted SUR/ID model whose likelihood function and parameter space factor into the product of the likelihood functions and the parameter spaces of standard complete data multivariate analysis of variance models. Hence, the restricted model has a unimodal likelihood and permits explicit likelihood inference. In the development of our methodology, we review and extend existing results for complete data SUR models and the multivariate ID problem.