溴掺杂聚苯胺的导电性能及机理研究

通过化学氧化法制备聚苯胺,对其进行溴和氢溴酸掺杂,聚苯胺(PANI)的电导率比掺杂前提高了11-13个数量级。采用红外光谱、热重分析、紫外可见光谱和X射线光电子能谱分析研究了溴和氢溴酸对PANI的掺杂作用,探讨了掺杂PANI的导电机理。研究表明,不同于氢溴酸对PANI的质子酸掺杂,溴掺杂过程中产生的溴负离子增加了PANI的载流子浓度并形成电子转移复合物,提高了聚苯胺的电导率。     

衬底加温和后续热退火法形成纳米硅晶粒成核势垒的比较

在真空环境中,采用脉冲激光烧蚀技术,分别在衬底加温和室温条件下沉积制备了纳米Si薄膜.对在室温条件下制备得到的非晶Si薄膜,采用后续热退火实现其晶化.通过扫描电子显微镜、Raman散射仪和X射线衍射仪对制备的薄膜形貌、晶态成分进行表征,得到两种情况下纳米Si晶粒形成的阈值温度分别为700 ℃和850 ℃,通过定量计算比较了两种情况下晶粒成核势垒的大小,并从能量角度对阈值温度的差别进行了理论分析.     

脉冲激光烧蚀制备纳米Si晶粒成核气压阈值及动力学研究

采用脉冲激光烧蚀技术,在室温、低压Ar气条件下通过改变气体压强及靶与衬底间距,对纳米Si晶粒成核的气压阈值进行了研究.根据扫描电子显微镜图像、拉曼散射谱和X射线衍射谱对制备样品的表征结果,确定了在室温、激光能量密度为4 J/cm²、靶与衬底间距为3 cm条件下形成纳米Si晶粒的阈值气压为0.6 Pa.结合流体力学模型和成核分区模型,对纳米晶粒的成核动力学过程进行了分析.通过Monte Carlo数值模拟,表明在气相成核过程中,烧蚀Si原子的温度和过饱和密度共同影响着纳米晶粒的成核. 关键词： 脉冲激光烧蚀 成核 气压阈值 Monte Carlo数值模拟     

脉冲激光沉积纳米硅晶粒流体模型的推广

Yoshida等人提出的惯性流体模型只能解释脉冲激光烧蚀制备纳米硅晶粒平均尺寸随环境气压的变化规律.在此模型基础上,考虑到烧蚀粒子的初始速度分布(Maxwell分布),得到了纳米硅晶粒尺寸分布的解析表达式,数值模拟结果与Yoshida等人在不同环境氦气压下制备样品的晶粒尺寸分布的实验统计数据基本相符.还利用修正后的模型对不同环境气体种类(氦、氖、氩)中制备的纳米Si晶粒尺寸分布进行了模拟,模拟结果与实验数据相符.结论可为实现纳米硅晶粒尺寸的均匀可控提供理论依据. 关键词： 纳米硅晶粒 脉冲激光烧蚀 惯性流体模型 尺寸分布     

Au掺杂硅纳米线的稳定性和电子结构

采用基于密度泛函理论的第一性原理的方法, 对Au掺杂[100]方向氢钝化硅纳米线(SiNWs)不同位置的形成能、能带结构、态密度及磁性进行了计算, 考虑了Au占据硅纳米线的替代、四面体间隙和六角形间隙的不同位置. 结果表明: Au偏爱硅纳米线中心的替代位置. Au掺杂后的硅纳米线引入了杂质能级, 禁带宽度变窄. 对于Au替代掺杂, 杂质能级主要来源于Au的d、p态和Si的p态, 由于Au的d态和Si的p态的耦合, Au掺杂硅纳米线具有铁磁性. 对于间隙掺杂, 杂质能级主要来源于Au的s态, 是非磁性的. 另外, 根据原子轨道和电子填充模型分析了其电子结构和磁性.     

低压下气流对激光沉积纳米硅晶化及尺寸的影响

纳米硅具有明显的光致发光效应和量子尺寸效应,广泛的应用在现代电子工业和太阳能光伏工业中.尺寸影响着纳米硅的实际用途,因此制备尺寸可控的纳米硅晶粒具有很重要的实际意义.本文采用脉冲激光沉积(PLD)技术,在烧蚀点水平方向、距靶2 cm处引入一束流量为5 sccm的氩(Ar)气流,在0.01-0.5 Pa的Ar气压下烧蚀高阻抗单晶硅(Si)靶.在管口正下方1 cm处水平放置衬底来沉积纳米Si薄膜;并用同一装置,在0.08 Pa的Ar气压下分别引入流量为0,2.5,5,7.5,10 sccm的Ar气流沉积纳米Si薄膜.利用原子力显微镜(AFM)、X射线衍射(XRD)、Raman散射对样品表面形貌和微观结构进行分析表征.结果表明:不引入气流时出现纳米Si晶粒的阈值气压是0.1Pa,引入气流后出现纳米Si晶粒的阈值气压为0.05 Pa.晶粒尺寸随着气流流量的增大而减小.     

数字显微全息中记录参数对颗粒测量影响的数值模拟

数字显微全息技术由于具有三维、非接触和实时测量微小空间内流场的能力, 已引起了国内外学者的广泛关注. 利用数字显微全息方法测量微通道流场时, 记录距离、颗粒尺寸、颗粒浓度、入射光波长、CCD分辨率等参数会对颗粒重建结果产生重要影响. 为了评估颗粒浓度和样本空间深度对重建结果的影响, 本文开展了数值模拟研究. 采用基于洛伦兹-米散射理论的程序产生不同浓度的颗粒全息图, 用小波变换重建算法对其进行重建. 结果表明: 在样本空间深度为24 μm 时, 颗粒浓度n_s在3.44×10⁵ mm^-3–13.77×10⁵ mm^-3 范围内时, 颗粒重建率E_p随着颗粒浓度n_s 的增大而迅速减小, 在13.77×10⁵ mm^-3–55.08×10⁵ mm^-3范围内时, 颗粒重建率E_p 随颗粒浓度n_s增大而缓慢减少. 在颗粒浓度n_s (13.77×10⁵ mm^-3) 保持不变时, 颗粒重建率E_p与样本空间深度满足单调递减的线性关系. 当阴影密度不变时, 重建率的变化呈现一定的规律性:当深度L较小时, 样本空间深度对颗粒重建的影响要比颗粒浓度的影响大; 当深度L较大时, 颗粒浓度对颗粒重建的影响较大. 关键词： 数字显微全息 颗粒浓度 粒径误差 位置误差     

Augmented Lagrangian Methods for $p$-Harmonic Flows with the Generalized Penalization Terms and Application to Image Processing

In this paper, we propose a generalized penalization technique and a convex constraint minimization approach for the $p$-harmonic flow problem following the ideas in [Kang & March, IEEE T. Image Process., 16 (2007), 2251-2261]. We use fast algorithms to solve the subproblems, such as the dual projection methods, primal-dual methods and augmented Lagrangian methods. With a special penalization term, some special algorithms are presented. Numerical experiments are given to demonstrate the performance of the proposed methods. We successfully show that our algorithms are effective and efficient due to two reasons: the solver for subproblem is fast in essence and there is no need to solve the subproblem accurately (even 2 inner iterations of the subproblem are enough). It is also observed that better PSNR values are produced using the new algorithms.     

Efficient Convex Optimization Approaches to Variational Image Fusion

Image fusion is an imaging technique to visualize information from multiple imaging sources in one single image, which is widely used in remote sensing, medical imaging etc. In this work, we study two variational approaches to image fusion which are closely related to the standard TV-$L_2$ and TV-$L_1$ image approximation methods. We investigate their convex optimization formulations, under the perspective of primal and dual, and propose their associated new image decomposition models. In addition, we consider the TV-$L_1$ based image fusion approach and study the specified problem of fusing two discrete-constrained images $f_1(x) ∈\mathcal{L}_1$ and $f_2(x) ∈\mathcal{L}_2$, where$\mathcal{L}_1$ and$\mathcal{L}_2$ are the sets of linearly-ordered discrete values. We prove that the TV-$L_1$ based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discrete-valued set $u(x) ∈\mathcal{L}_1 ∪\mathcal{L}_2$. This extends the results for the global optimization of the discrete-constrained TV-$L_1$ image approximation [8, 36] to the case of image fusion. As a big numerical advantage of the two proposed dual models, we show both of them directly lead to new fast and reliable algorithms, based on modern convex optimization techniques. Experiments with medical images, remote sensing images and multi-focus images visibly show the qualitative differences between the two studied variational models of image fusion. We also apply the new variational approaches to fusing 3D medical images.     

A robust nonconforming -element

Finite element methods for some elliptic fourth order singular perturbation problems are discussed. We show that if such problems are discretized by the nonconforming Morley method, in a regime close to second order elliptic equations, then the error deteriorates. In fact, a counterexample is given to show that the Morley method diverges for the reduced second order equation. As an alternative to the Morley element we propose to use a nonconforming -element which is -conforming. We show that the new finite element method converges in the energy norm uniformly in the perturbation parameter.