低铂Pt/SiC制备及对氧还原反应的催化性能表征

采用乙二醇回流法成功制备了理论含铂量为14;的Pt/SiC阴极催化剂.利用X射线衍射(XRD)、扫描电镜(SEM)、透射电镜(TEM)、X射线光电子能谱(XPS)对催化剂晶型结构、表面形貌和表面电子结构进行了表征.应用循环伏安法和旋转圆盘电极进行电化学测试,研究了Pt/SiC催化剂对氧还原反应催化活性.结果表明,在180℃条件下合成的Pt/SiC催化剂的平均粒径为3.4 nm,Pt是面心立方结构.Pt在SiC表面以0价为主,有利于O2在其表面的吸附和解离.Pt/SiC-180催化剂具有与商业Pt/C(20;)催化剂相近的氧还原催化性能.Pt/SiC-180催化剂的起始还原电位为0.95 V,氧还原过程以4-电子路径为主,且稳定性和抗甲醇中毒性能明显优于商业Pt/C催化剂.优良的催化性能和较低的含铂量使其有望成为低铂燃料电池阴极催化潜在材料.     

Entropy of field interacting with two two-qubit atoms

We use quantum field entropy to measure the degree of entanglement for a coherent state light field interacting with two atoms that are initially in an arbitrary two-qubit state. The influence of different mean photon number of the coherent field on the entropy of the field is discussed in detail when the two atoms are initially in one superposition state of the Bell states. The results show that the mean photon number of the light field can regulate the quantum entanglement between the atoms and light field.     

粘弹性减阻流动的雷诺应力模型研究

针对由Giesekus本构方程描述的粘弹性槽道减阻流动,建立了低Re数雷诺应力模型.通过与DNS数据的比较,该模型对平均速度、平均变形、减阻率和应力的预测较准确,而对时均速度剖面和脉动速度强度的预测精度有待提高.另外,对雷诺应力再分配项(压强变形率相关项)的模化有待改进.     

Comments on <Emphasis Type="Italic">Three-point explicit compact difference scheme with arbitrary order of accuracy and its application in CFD</Emphasis>

The explicit compact difference scheme, proposed in Three-point explicit compact difference scheme with arbitrary order of accuracy and its application in CFD by Lin et al., published in Applied Mathematics and Mechanics （English Edition）, 2007, 28（7）, 943-953, has the same performance as the conventional finite difference schemes. It is just another expression of the conventional finite difference schemes. The proposed expression does not have the advantages of a compact difference scheme. Nonetheless, we can more easily obtain and implement compared with the conventional expression in which the coefficients can only be obtained by solving equations, especially for higher accurate schemes.     

Enhancement of next-nearest-neighbouring entanglement in quantum Ising spin chain

Using the method of the Jordan--Wigner transformation for solving different spin--spin correlation functions, we have investigated the generation of next-nearest-neighbouring entanglement in a one-dimensional quantum Ising spin chain with the Gaussian distribution impurities of exchange couplings and external magnetic fields taken into account. The maximal value of entanglement between the next-nearest-neighbouring qubits in the transverse Ising model was analysed in detail by varying the effectively controlled parameters such as interchange coupling, magnetic field and the system impurity. For such systems, where both exchange couplings and external magnetic field disorder appear, we show that it is possible to achieve next-nearest-neighbouring entanglement better than the previously discussed pure Ising spin chain case. We also show that the Gaussian distribution impurity can induce next-nearest-neighbouring entanglement, which can be used as a means to characterize quantum phase transition.     

具有Dzyaloshinskii-Moriya相互作用的一维随机量子XY模型中的纠缠特性

研究了一维随机量子XY自旋链中中心两量子位的纠缠特性,在该系统中引入了自旋间的交换耦合杂质、磁杂质和Dzyaloshinskii-Moriya相互作用,并且杂质满足高斯分布关系.通过数值计算,求出了自旋的关联函数和平均磁化强度,给出了Concurrence的解析表达式.结果表明：高斯分布和Dzyaloshinskii-Moriya相互作用对两量子位的纠缠有重要的影响,选择合适的交换耦合、外界磁场和Dzyaloshinskii-Moriya相互作用参数,可以控制和提高中心两量子位的纠缠. 关键词： 纠缠 XY模型')" href="#">随机量子XY模型 高斯分布 Dzyaloshinskii-Moriya相互作用     

基于差分进化算法的核磁共振T2谱多指数反演

提出了一种基于差分进化(DE)算法的核磁共振弛豫信号多指数反演新方法. 将核磁共振T₂谱反演问题转化为带非负约束的非线性优化问题,不需要预先给定横向弛豫时间T_{2分布,直接利用差分进化算法进行反演计算. 在测量信号低信噪比的情况下,计算机模拟和实验数据反演都表明了该方法在分析处理NMR弛豫信号中的有效性. 关键词： 核磁共振 多指数反演 差分进化 岩心分析}     

基于差分进化算法的核磁共振T2谱多指数反演

提出了一种基于差分进化(DE)算法的核磁共振弛豫信号多指数反演新方法.将核磁共振T2谱反演问题转化为带非负约束的非线性优化问题,不需要预先给定横向弛豫时间T2分布,直接利用差分进化算法进行反演计算.在测量信号低信噪比的情况下,计算机模拟和实验数据反演鄙表明了该方法在分析处理NMR弛豫信号中的有效性.     

基于有效性的集值映射最优化的广义共轭对偶理论

讨论在锥控意义下，建立有效性上的集值映射最优化问题的广义共地偶理论，证明了弱，强对偶定理和鞍点定理。     

不易发现的错解

现行高中《平面解析几何》课本中有这样一个命题[1]:“点M与点F(4,0)的距离比它到直线x+5=0的距离小1,求点M的轨迹方程,”对基础较好的学生来说会联想到用抛物线的定义去解非常简单,它等价于命题“点M与点F(4,0)的距离等于它列直线x=-4的距离,求点M的轨迹方程。”很明显答案为y~2=16x,解法很巧妙!