矩形多环芳烃的合成

本文用3,4-二苯基-2,5-二(3,5-二溴苯基)环戊二烯酮(4a)与二苯乙炔(5a)通过Diels-Alder环加成反应得到1,2,4,5-四苯基-3,6-二(3,5-二溴苯基)苯(6a)。化合物6a通过经典的Suzuki偶联反应得到1,2,4,5-四苯基-3,6-二(3,5-二(4-十二烷基噻吩))苯基苯(8a),再利用Fe Cl3作为氧化剂发生Scholl氧化脱氢关环反应,得到目标化合物1a。采用类似合成方法,得到目标化合物1b。化合物的结构均通过1H NMR和MALDI-TOF MS表征,并对其光谱特征、热性能及电学性能进行了初步研究。     

光响应超分子聚合物

超分子聚合物是超分子化学、高分子化学和材料化学领域的研究热点.将光响应的功能基团以非共价作用构筑到超分子聚合物体系中,得到光响应型超分子聚合物,从而能够将超分子聚合物的独特性质与光化学反应的优势有效地结合起来,从而构筑新型的光功能材料.本文总结了近年来本课题组有关光响应超分子聚合物方面的研究工作:介绍了主链型的光响应超分子聚合物的光调控组装和解离,超分子聚合物和共价聚合物的光控可逆切换和光调控组装形貌;另外还举例介绍了具有自修复和室温磷光发射等功能的侧链型光响应超分子聚合物,并对刺激-响应的超分子聚合物领域的发展做了展望.     

异相成核和拉伸诱导对生物基PHBV复合纤维结晶结构与力学性能的影响

采用熔融纺丝法制备了聚(3-羟基丁酸酯-co-3-羟基戊酸酯)(PHBV)/二硫化钨(WS_2)复合纤维.利用示差扫描量热仪(DSC)、热台偏光显微镜、二维广角射线衍射仪(2D-WXRD)、纤维强力仪研究了WS_2异相成核作用和牵伸诱导作用对纤维的结晶结构和力学性能的影响.研究表明,WS_2显著提高了PHBV的结晶温度,当使用2 wt%WS_2时,复合材料的结晶温度提高到115~130oC,比纯PHBV(99~105oC)提高了约25oC.WS_2不仅没有影响PHBV球晶的径向生长速率,且明显提高了PHBV/WS_2复合材料的晶核密度,熔体成核活性Φ由1.0降低为0.49.随着牵伸倍率和WS_2用量的增加,纤维的拉伸强度呈现出先增加后减小的趋势.当添加1 wt%WS_2并采用单向牵伸3.8倍时,纤维中的晶体取向产生了β晶结构,使复合纤维的拉伸强度由纯PHBV的37 MPa提高至155 MPa,断裂伸长率由2.4%增加至45%.     

硫化天然橡胶的本征自修复与固相回收加工

通过小分子模拟试验和应力松弛,发现甲基丙烯酸铜(MA-Cu)比氯化铜(CuCl_2)更适合用作硫化天然橡胶中二硫键交换反应催化剂,并且硫化天然橡胶中的二硫键交换反应需要在高于120°C的温度下才能高效进行,这保障了相关制品在较低温工作时的结构与性能稳定.进一步的实验结果表明,硫化天然橡胶在MA-Cu辅助作用下获得了多次自修复与固相回收加工性能,损伤自修复后的硫化天然橡胶与固相回收加工的再生硫化天然橡胶,其拉伸强度均可恢复到原始材料强度的80%左右.     

4′-{2-[5-(4-羧基苯基)呋喃基]}-2,2′:6′,2″-三联吡啶的合成——推荐一个大学有机化学实验

介绍一个面向大学高年级本科生的综合性有机化学实验——4′-{2-[5-(4-羧基苯基)呋喃基]}-2,2′:6′,2″-三联吡啶的合成与结构表征。以糠醛、对氨基苯甲酸为原料经低温条件下的偶联反应制备5-(4-羧基苯基)呋喃-2-甲醛,并以其和2-乙酰基吡啶为原料,在碱性条件下以氨水为氮源合成目标产物。通过熔点测定、红外光谱和核磁共振氢谱对其结构进行表征。该实验合成路线成熟,包括芳环偶联、α,β-不饱和酮的合成、Michael加成等内容,能达到多方面对学生进行锻炼之目的,是一个值得推荐的有机化学实验。     

基于个体仿真的机场安检系统优化研究

建立了安检流程的网络流模型,模型中包含了不稳定的乘客到达情形、嫌疑乘客以及反馈机制。分析了安检过程中的瓶颈所在,并给出了相应的优化方案,包括整个安检过程的通行规则,A区和B区内部预检节点与普通节点的适当比例等。仿真结果显示,为了充分利用安检资源并兼顾预检乘客的利益,通行规则应采用连通方案。依照统计数据中计算出的各种参数,在A区预检节点和普通节点比例设置为2∶2,B区预检节点和普通节点比例设置为3∶5的情况下,旅客平均等待时间最短,等待时间的标准差也最小,同时高峰期的通行量也相对较优。进一步分析了不同的文化背景及机场情况。对于美国人和瑞士人,通过改变预检乘客的选择概率来满足他们不同的行为偏好。仿真结果说明,预检乘客选择哪类节点进行安检对平均等待时间、等待时间的标准差和高峰期通行量的影响不大。分析了插队情形,插队对平均等待时间和高峰期通行量几乎没有影响,但当插队比例非常大时,旅客等待时间标准差会增加,影响乘客到达的准确性。最后,针对具体情况提出了一些合理的建议,并给出了进一步的研究计划。     

基于改进的模糊综合评价法的低渗油藏CO_2驱筛选体系的建立及应用

以某低渗透油藏为对象,首先在数据挖掘的基础上制定了低渗透油藏注CO_2驱筛选标准.基于模糊综合评价法,建立了适用于低渗透油藏CO_2驱适宜度评价的模糊综合评价模型,并运用该模型对目标油藏注CO_2的适宜度进行了评价.计算结果表明目标油藏注气驱开发适应性较好.评价过程有效地降低了直接进行注气开发的风险,为之后低渗透油藏注气开发提供了技术和经济上的指导.     

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.     

Semiparametric Bayesian inference for mean-covariance regression models

In this paper, we propose a Bayesian semiparametric mean-covariance regression model with known covariance structures. A mixture model is used to describe the potential non-normal distribution of the regression errors. Moreover, an empirical likelihood adjusted mixture of Dirichlet process model is constructed to produce distributions with given mean and variance constraints. We illustrate through simulation studies that the proposed method provides better estimations in some non-normal cases. We also demonstrate the implementation of our method by analyzing the data set from a sleep deprivation study.     

New algebraic structures from Hermitian one-matrix model

Virasoro constraint is the operator algebra version of one-loop equation for a Hermitian one-matrix model, and it plays an important role in solving the model. We construct the realization of the Virasoro constraint from the Conformal Field Theory (CFT) method. From multi-loop equations of the one-matrix model, we get a more general constraint. It can be expressed in terms of the operator algebras, which is the Virasoro subalgebra with extra parameters. In this sense, we named as generalized Virasoro constraint. We enlarge this algebra with central extension, this is a new kind of algebra, and the usual Virasoro algebra is its subalgebra. And we give a bosonic realization of its subalgebra.