激光能量对反钙钛矿GaCMn_3薄膜结构与物性的影响

本文采用脉冲激光沉积方法在LaAlO3(001)单晶衬底上制备了反钙钛矿GaCMn3薄膜,通过控制制备过程中脉冲激光的能量,研究了不同激光能量条件对GaCMn3薄膜结构与物理性能的影响.分别利用X射线衍射仪、原子力显微镜、超导量子干涉仪和物理性能测试系统,对所制备的薄膜的晶体结构、表面形貌和磁性、电输运性质进行了研究.结果表明,制备的样品均为具有多个晶面取向的反钙钛矿薄膜,且薄膜结构和物性明显随制备激光能量的变化而变化.当激光能量为450mJ时,制备的薄膜多晶面取向性最弱,结晶性和表面形貌最优良.实验所得的薄膜均表现出顺磁-铁磁-反铁磁相转变,然而转变过程比块材较平缓,同时薄膜的电阻率并未表现出块材中的突变特征,我们推测该现象很可能是由衬底的应力及衬底的晶格膨胀对薄膜反常晶格变化的抑制作用造成的.     

词汇策略的使用与词汇知识水平的相关探究

旨在探讨二语学习者的词汇策略倾向与其词汇知识之间的关系。对非英语专业大学二年级学生的词汇水平测试和词汇学习策略问卷调查结果表明：（1）学习者词汇策略的认知倾向对词汇知识水平有预测作用：词汇水平较高的同学更多地使用认知策略和元认知策略，而词汇水平相对较差的同学较多地依赖机械记忆；（2）二语学习者还没有摆脱母语知识系统的束缚，还需要进一步努力构建二语词汇知识系统。     

等离子体噻吩聚合膜的研究及I+注入的掺杂效应

以噻吩为单体,在等离子体环境中聚合成致密的有机膜。利用Rutherford背散射(RB2S)和质子弹性反冲(ERD)、傅里叶红外谱(FTIR)测定了膜的成分和结构;从近红外-可见-紫外光的反射和透射谱,运用传递矩阵方法研究了膜的光学性质;此外,还探讨了24keV的I⁺离子束的注入对等离子体噻吩聚合膜的掺杂效应。结果表明,注入层内电荷载流子的输运机理可用Mott的可变自由程跳跃(VRH)理论给以解释。 关键词：     

单晶光纤损耗谱测量装置

本文描述了一套用以测量单晶光纤损耗谱的装置。本装置由卤钨灯,单色仪,积分球,AgORbCs光电倍增管以及数据采集,处理和控制系统组成。可测量300～1150nm的光谱范围。利用本装置可获得单晶光纤的吸收光谱,透过率谱,散射光谱以及散射位置谱。     

秦巴山区西洋参栽培的基本经验

根据秦巴山区的生态特点，对影响西洋参栽培中的质量和产量等几个基本问题进行了总结。     

充分挖掘高校档案资源更好地为地方经济服务

为使高校档案工作能更好地为地方经济服务，必须提高认识，拓宽思路，提升服务的自觉性；内练素质，外树形象，不断提高挖掘和利用的水平；切实解决好标准化建设、与地方政府的沟通、经费投入、复合型人才培养、数据库建设等问题。     

Triterpenoid Constituents of Huperzia miyoshiana

Thirteen triterpenoids, including three new ones, miyoshianois A (1), B (2) and C (3), were isolated from Huperzia miyoshiana. The structures of these new compounds were established as 3-O-dihydroferuloyltohogenol (1), 16-oxo-3β,21β-dihydrox-y-serrat-14-en-24-ferulate (2) and 16-oxo-3a, 21β-dihydroxy-serrat-14-en-24-ferulate (3), respectively, on the basis of their spectroscopic analysis.     

临近铁路深基坑开挖防护技术

考虑到周边建筑物安全及在动荷载下施工人员和机械设备的安全,选择了防护桩方案并进行了设计及计算,为临近铁路深基坑施工提供了参考。     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.