基于最新势能面的C(1D)+H2反应的环聚合分子动力学研究

本文利用环聚合分子动力学方法对C(¹D)+H₂反应开展了详细的理论研究. 计算中使用了最近构建的Zhang-Ma-Bian(ZMB)从头算势能面,该势能面对锥形交叉附近区域以及范德华区域均有精确的描述. 环聚合分子动力学计算得到的热反应速率常数与最新实验值吻合很好. 与前人计算结果比较,发现在?¹A′电子基态的ZMB-a势能面上获得的反应速率常数远大于前人构建的RKHS势能面上的结果,这是由于ZMB势能面上的范德华鞍具有与之前势能面上的范德华阱完全不同的动态学作用,表明环聚合分子动力学方法能够处理范德华作用引起的势能面拓扑结构所导致的动态学效应. 本文还揭示了b¹A′′电子激发态ZMB-b势能面以及量子效应对反应的重要性.     

间接法测定美斯替仑磷脂复合物的复合率

建立美斯替仑磷脂复合物复合率的间接测定方法。采用紫外分光光度法测定未参与复合的美斯替仑的量,由此计算复合率。结果表明,美斯替仑在269nm处有最大吸收,浓度在10.02—40.09μg·mL^-1时与吸光度呈良好的线性关系,回归方程为:A=9.0176C+0.0065,r=0.9999（n=3）;平均回收率为99.54%,RSD为0.66%。本法操作简便易行,具有良好的重现性和精密度。     

无封胶白光数码管荧光粉转换率的分析

研究无封胶白光数码管衰减机理,利用蓝白比拟法测算无封胶白光数码管荧光粉转换率.对蓝白数码管分3组进行电流老化实验,老化电流分别为20、40mA和60mA.在恒定直流为20mA,老化时间为1000h实验中,通过分析蓝、白数码管光谱曲线的变化,研究了蓝光光输出功率和黄光光输出功率衰减情况,利用实验数据,分析表明了荧光粉转换率的衰减是影响无封胶白光数码管老化的重要因素.同时分析比较了在40mA和60mA不同电流老化实验下得到的荧光粉转换率,发现工作电流越大,荧光粉转化率衰减越快,符合无封胶白光数码管在大电流作用下衰减更快的实际情况,影响使用寿命.对荧光粉转换率的研究将为无封胶白光数码管的应用及进一步研究无封胶白光数码管衰减原因提供参考.     

糠醇辅助的聚合凝胶法制备纳米Li4Ti5O12负极材料

以硝酸锂、钛酸正丁酯和糠醇为反应物,采用糠醇聚合凝胶法制备了纳米Li₄Ti₅O₁₂粉体．利用XRD、SEM和BET比表面测试对产物进行了表征,并研究了纳米Li₄Ti₅O₁₂粉体作为锂离子电池负极材料的电化学性能．在700℃或更高温度烧结时产物为纯相的尖晶石型．通过柠檬酸、聚乙烯吡咯烷酮、十六烷基三甲基溴化铵(CTAB)表面活性剂的加入能够减少产物颗粒的团聚程度,增大粉体的比表面积,提高其电化学性能．加入0.5 g CTAB、700℃烧结12 h的Li₄Ti₅O₁₂粉体展示出最高的比容量和最佳的循环性能,10 C下充电比容量高达156.7 mAh/g．     

双包层光纤激光器的受激拉曼散射与热应力

 建立了高功率掺镱双包层光纤激光器的速率方程模型与热应力模型，对影响受激拉曼散射效应和热应力效应的关键参数（如纤芯半径、光纤长度、泵浦波长、泵浦方式）进行了数值模拟。结果表明:对于较小的纤芯半径，光纤内的斯托克斯光功率较大且增长迅速，因此增加纤芯半径能有效减弱受激拉曼散射效应；减小光纤长度能提高受激拉曼散射的阈值，而纤芯的热应力也增大，因此在不出现热应力引起光纤断裂的情况下，可以减小光纤长度以提高输出功率；采用976 nm波段泵浦源能提高输出功率，降低热应力的影响；两端均匀泵浦方式可以有效降低纤芯热应力，同时维持高功率输出。     

Solving symmetric mixed-model multi-level just-in-time scheduling problems

The generation of leveled production schedules is of high importance for mixed-model assembly lines whose parts and materials are supplied just-in-time by multi-level production processes. The Output Rate Variation problem is the standard mathematical representation of this complex level scheduling problem and has been extensively studied by research thus far. This work identifies novel symmetries in solution sequences of this problem class and shows how these insights can be used to improve exact solution procedures presented in the literature. The effectiveness of the modifications is evaluated by a computational study.     

The implications of costs, capacity, and competition on product line selection

We consider a situation in which a manufacturer has to select the product(s) to sell as well as the selling price and production quantity of each selected product. There are two substitutable products in the consideration set, where product 2 has a higher quality and reservation price than that of product 1. By considering the cannibalization effect that depends on the selling price of each product, the manufacturer needs to evaluate the profit function associated with three different product line options: sell both products or only one of the 2 products. In order to examine the impact of costs, capacity, and competition on the optimal product line selection, optimal price, and optimal production quantity analytically, we present a stylized model in this paper so that we can determine the conditions under which a particular option is optimal.     

A stochastic programming approach for planning horizons of infinite horizon capacity planning problems

Planning horizon is a key issue in production planning. Different from previous approaches based on Markov Decision Processes, we study the planning horizon of capacity planning problems within the framework of stochastic programming. We first consider an infinite horizon stochastic capacity planning model involving a single resource, linear cost structure, and discrete distributions for general stochastic cost and demand data (non-Markovian and non-stationary). We give sufficient conditions for the existence of an optimal solution. Furthermore, we study the monotonicity property of the finite horizon approximation of the original problem. We show that, the optimal objective value and solution of the finite horizon approximation problem will converge to the optimal objective value and solution of the infinite horizon problem, when the time horizon goes to infinity. These convergence results, together with the integrality of decision variables, imply the existence of a planning horizon. We also develop a useful formula to calculate an upper bound on the planning horizon. Then by decomposition, we show the existence of a planning horizon for a class of very general stochastic capacity planning problems, which have complicated decision structure.     

The design of robust value-creating supply chain networks: A critical review

This paper discusses Supply Chain Network (SCN) design problem under uncertainty, and presents a critical review of the optimization models proposed in the literature. Some drawbacks and missing aspects in the literature are pointed out, thus motivating the development of a comprehensive SCN design methodology. Through an analysis of supply chains uncertainty sources and risk exposures, the paper reviews key random environmental factors and discusses the nature of major disruptive events threatening SCN. It also discusses relevant strategic SCN design evaluation criteria, and it reviews their use in existing models. We argue for the assessment of SCN robustness as a necessary condition to ensure sustainable value creation. Several definitions of robustness, responsiveness and resilience are reviewed, and the importance of these concepts for SCN design is discussed. This paper contributes to framing the foundations for a robust SCN design methodology.     

Supply capacity acquisition and allocation with uncertain customer demands

We study a class of capacity acquisition and assignment problems with stochastic customer demands often found in operations planning contexts. In this setting, a supplier utilizes a set of distinct facilities to satisfy the demands of different customers or markets. Our model simultaneously assigns customers to each facility and determines the best capacity level to operate or install at each facility. We propose a branch-and-price solution approach for this new class of stochastic assignment and capacity planning problems. For problem instances in which capacity levels must fall between some pre-specified limits, we offer a tailored solution approach that reduces solution time by nearly 80% over an alternative approach using a combination of commercial nonlinear optimization solvers. We have also developed a heuristic solution approach that consistently provides optimal or near-optimal solutions, where solutions within 0.01% of optimality are found on average without requiring a nonlinear optimization solver.