Characteristics of large-sized ruby crystal grown by temperature gradient technique

Large ruby with the size of circle divide75 x 45 mm was grown by temperature gradient technique for the first time. Absorption spectrum was carried out in the range of 190-800 nm by spectrophotometer, and the concentration spatial distribution of Cr3+ in ruby was calculated from the absorption coefficient that based on the Beer-Lambert's Law. Cr3+ ions gradually increase alone both the growth axis and the radial direction. The shape and ingredient of the inclusions were measured by means of Leitz ride field microscopy and scanning electron microscopy. Lane photos and X-ray omega scan show the good quality of as grown ruby. The optimized growth conditions were pointed out based on the observation. (C) 2004 Elsevier B.V. All rights reserved.

Yb：Ca5（PO4）3F激光晶体的生长缺陷

采用提拉法生长了质量优异的Yb：Ca5（PO4）2F（Yb：FAP）晶体。运用化学腐蚀，光学显微镜、扫描电子显微镜以及能量散射光谱仪观察了该晶体中的生长条纹和包裹物等宏观缺陷，以及晶体的位错腐蚀形貌、位错密度及其分布情况，同时观察了晶体中亚晶界的形态。由晶体中位错的径向变化以及生长条纹可知：晶体在生长过程中为微凸界面生长。高温下CaF2的挥发造成了在晶体生长后期熔体中组分偏离化学计量比，出现组分过冷，形成包裹物。且位错密度显著增加。Yb：FAP晶体的各向异性使得晶体在（10 10）面的位错蚀坑形状、大小以

伦纳德琼斯薄膜材料导热系数的分子动力学研究

提出从微观的角度,借助计算机工具,将薄膜破坏发展的细节展现出来的分子动力学研究的思想。使得实验上难以观察的现象变得形象而便于理解。应用分子动力学理论,使用伦纳德琼斯势函数,采用预校正积分法和虚拟外力约束标定方法,模拟薄膜体系的传热系数受体系的密度、温度的影响,同时结合体系粒子的径向分布函数和长程分布函数分析了相应的系统结构特性。另外,采用不同的模拟尺寸获得了低维材料所特有的“高温尺寸效应”。结果显示,导热系数随密度的增加变大,随温度的上升而变大。这些数据现有测量手段是难以得到的,这类模拟可以为研究提供一些

渐变反射率镜的设计与制备

按照高斯型渐变反射率镜(GRM)的参数要求,采用了中间层厚度渐变的方案对膜系和掩模板形状进行设计.根据薄膜的实际需求和具体的沉积设备,设计了掩模和掩模切换装置.在一次高真空环境下镀制了渐变反射率镜的所有膜系.采用直接测量的方法,测量了高斯型渐变反射率镜反射率的径向分布.测试结果表明,用这种技术制备的样品,与设计要求基本一致.分析得出,掩模板形状与精度对镀制结果有影响.随着设计尺寸减小,掩模板对膜料分子的散射作用增强,使样品中心反射率小于设计要求,边缘出现旁瓣.提出了减小基片与掩模板之间的距离和提高膜厚监控的精度的改善方案.

Transplantable neural progenitor populations derived from Rhesus monkey embryonic stem cells

Cell-based therapies using embryonic stem cells (ESCs) in the treatment of neural disease will require the generation of homogenous donor neural progenitor (NP) populations. Here we describe an efficient culture system containing hepatocyte growth factor (HGF) and G5 supplement for the production of highly enriched (88.3% +/- 8.1%)populations of NPs from rhesus monkey ESCs. Additional purification resulted in NP preparations that were 98% nestin positive. Moreover, NPs, as monolayers or neurospheres, could be maintained for prolonged periods of time in media containing HGF+G5 or G5 alone. In vitro differentiation and in vivo transplantation assays showed that NPs could differentiate into neurons, astrocytes, and oligodendrocytes. The kinds and quantities of differentiated cells derived from NPs were closely correlated with their niches in vivo. Glial differentiation was predominant in periventricular areas, whereas cells migrating into the cortex were mostly neurons. Cell counts showed that 2 months after transplantation, approximately 25% of transplanted NPs survived and 65% - 80% of the surviving transplanted cells migrated along the ventricular wall or in a radial fashion. Subcloning demonstrated that several clonal lines derived from NPs expressed nestin and differentiated into three neural lineages in vitro and in rat brains in vivo. In contrast, some subcloned lines showed restricted differentiation both in vitro and in vivo in rat brains. These observations set the stage for obtaining highly enriched NPs and evaluating the efficacy of NP-based transplantation therapy in the nonhuman primate and will provide a platform for probing the molecular mechanisms that control neural induction.

Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia

The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.

Anomalous photogalvanic effect of circularly polarized light incident on the two-dimensional electron gas in AlxGa1-xN/GaN heterostructures at room temperature

Under normal incidence of circularly polarized light at room temperature, a charge current with swirly distribution has been observed in the two-dimensional electron gas in Al0.25Ga0.75N/GaN heterostructures. We believe that this anomalous charge current is produced by a radial spin current via the reciprocal spin Hall effect. It suggests a new way to research the reciprocal spin Hall effect and spin current on the macroscopic scale and at room temperature.

Quantum-confined Stark effects in a single GaN quantum dot

Using analytical expressions for the polarization field in GaN quantum dot, and an approximation by separating the potential into a radial and an axial, we investigate theoretically the quantum-confined Stark effects. The electron and hole energy levels and optical transition energies are calculated in the presence of an electric field in different directions. The results show that the electron and hole energy levels and the optical transition energies can cause redshifts for the lateral electric field and blueshifts for the vertical field. The rotational direction of electric field can also change the energy shift.

A lattice dynamical treatment for the total potential energy of single-walled carbon nanotubes and its applications: relaxed equilibrium structure, elastic properties, and vibrational modes of ultra-narrow tubes

In this paper, we propose a lattice dynamic treatment for the total potential energy of single-walled carbon nanotubes (SWCNTs) which is, apart from a parameter for the nonlinear effects, extracted from the vibrational energy of the planar graphene sheet. The energetics, elasticity and lattice dynamics are treated in terms of the same set of force constants, independently of the tube structures. Based upon this proposal, we have investigated systematically the relaxed lattice configuration for narrow SWCNTs, the strain energy, the Young's modulus and Poisson ratio, and the lattice vibrational properties with respect to the relaxed equilibrium tubule structure. Our calculated results for various physical quantities are nicely in consistency with existing experimental measurements. In particular, we verified that the relaxation effect makes the bond length longer and the frequencies of various optical vibrational modes softer. Our calculation provides evidence that the Young's modulus of an armchair tube exceeds that of the planar graphene sheet, and that the large diameter limits of the Young's modulus and Poisson ratio are in agreement with the experimental values of graphite; the calculated radial breathing modes for ultra-narrow tubes with diameters ranging between 2 and 5 angstrom coincide with the experimental results and the existing ab initio calculations with satisfaction. For narrow tubes with a diameter of 20 angstrom, the calculated frequencies of optical modes in the tubule's tangential plane, as well as those of radial breathing modes, are also in good agreement with the experimental measurements. In addition, our calculation shows that various physical quantities of relaxed SWCNTs can actually be expanded in terms of the chiral angle defined for the corresponding ideal SWCNTs.

Anomalous-circular photogalvanic effect in a GaAs/AlGaAs two-dimensional electron gas

We have studied the circular photogalvanic effect (CPGE) in a GaAs/AlGaAs two-dimensional electron gas excited by near infrared light at room temperature. The anomalous CPGE observed under normal incidence indicates a swirling current which is realized by a radial spin current via the reciprocal spin-Hall effect. The anomalous CPGE exhibits a cubic cosine dependence on the incidence angle, which is discussed in line with the above interpretation.

Nonpolar growth and characterization of InN overlayers on vertically oriented GaN nanorods

Nanostructured hexagonal InN overlayers were heteroepitaxially deposited on vertically oriented c-axis GaN nanorods by metal-organic chemical vapor deposition. InN overlayers grown in radial directions are featured by a nonpolar heteroepitaxial growth mode on GaN nanorods, showing a great difference from the conventional InN growth on (0001) c-plane GaN template. The surface of InN overlayers is mainly composed of several specific facets with lower crystallographic indices. The orientation relationship between InN and GaN lattices is found to be [0001](InN) parallel to [0001](GaN) and [1100](InN)parallel to[1100](GaN). A strong photoluminescence of InN nanostructures is observed. (C) 2009 American Institute of Physics. [DOI 10.1063/1.3177347]

Well-Aligned Zn-Doped InN Nanorods Grown by Metal-Organic Chemical Vapor Deposition and the Dopant Distribution

We report the synthesis and characterization of Zn-doped InN nanorods by metal-organic chemical vapor deposition. Electron microscopy images show that the InN nanorods are single-crystalline structures and vertically well-aligned. Energy-dispersive X-ray spectroscopy analyses suggest that Zn ions are distributed nonhomogenously in InN nanorods. Simulations based on diffusion model show that the doping concentration along the radial direction of InN nanorod is bowl-like from the exterior to the interior, the doping concentration decreases, and Such dopant distribution result in a bimodal EDXS spectrum of Zn across the nanorod. The study of the mechanism of doping effect is useful for the design of InN-based nanometer devices. Also, high-quality Zn-doped InN nanorods will be very attractive as building blocks for nano-optoelectronic devices.'

An optimized design of a fiber optic hydrophone based on a flat diaphragm and multilayer fiber coils: A theoretical approach

In this paper, we present a theoretical approach to optimize the design of a fiber optic hydrophone based on a flat diaphragm and multilayer fiber coils. In this theoretical analysis, both the radial and tangential strain induced fiber length changes are taken into account. The result shows that the position of the fiber coils and the number of the fiber layers have significant effects on the sensitivity, of the hydrophone. By optimizing these parameters, a higher sensitivity can be achieved. Sample hydrophones are fabricated and tested. The experimental result is in good agreement with the theoretical result, which shows this theoretical approach is effective in optimizing the design of the fiber optic hydrophone. (C) 2008 Elsevier Ltd. All rights reserved.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

Geometrical learning, descriptive geometry, and biomimetic pattern recognition

Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by achievements on information geometry. This paper proposes a different geometrical learning from the perspective of high-dimensional descriptive geometry. Geometrical properties of high-dimensional structures underlying a set of samples are learned via successive projections from the higher dimension to the lower dimension until two-dimensional Euclidean plane, under guidance of the established properties and theorems in high-dimensional descriptive geometry. Specifically, we introduce a hyper sausage like geometry shape for learning samples and provides a geometrical learning algorithm for specifying the hyper sausage shapes, which is then applied to biomimetic pattern recognition. Experimental results are presented to show that the proposed approach outperforms three types of support vector machines with either a three degree polynomial kernel or a radial basis function kernel, especially in the cases of high-dimensional samples of a finite size. (c) 2005 Elsevier B.V. All rights reserved.