乳液聚合中分子量分布特性的耗散粒子动力学模拟研究
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摘要
乳液聚合方法在高分子合成工业领域占有重要地位,其产品广泛应用于橡胶、粘合剂、生物医学等各个领域。另外由于水乳型产品代表了当今发展的方向,其市场潜力巨大、应用广阔,所以世界各国竞相致力于乳液聚合物的研究、开发和应用。作为描述高分子聚合物特性的重要指标,分子量和分子量分布一直是乳液聚合学术领域和工业领域的科学家们关注的重要课题。影响乳液聚合分子量和分子量分布的因素有很多,包括:水相与乳胶粒中的各个反应、单体浓度、表面活性剂、引发剂浓度和活性等等。探索乳液聚合机理以及聚合体系组分对分子量和分子量分布的影响规律,从而达到改变材料性能的目的,对聚合物材料的设计有着十分重要的意义。
涉及微观机理的细节描述,计算机模拟技术是一个很重要的手段,因为计算机模拟方法可以通过改变单一因素对某一性质的变化进行考察,可以直接给出这些物理现象的直观图像,帮助人们从本质上理解和认识其规律。本论文利用耗散粒子动力学模拟(DPD)方法,对乳液聚合进行了细致深入地研究。在DPD方法中,粒子之间的受力包括三部分——保守力、耗散力、和随机力,每一部分相互作用力都是成对出现的,使得体系的动量保持守恒。由于这些力都是非常软的相互作用力,因此DPD中积分步长可以选得很长,模拟的时间尺度可达到毫秒级。也正是因为这种软相互作用势,我们可以把微观尺度上的几个分子甚至是高分子链中的若干个片段粗粒化成DPD模型中的一个粒子,从而使DPD方法可以用来描述在微米级别上的体系。目前,这个方法已经被成功地应用到高聚物共混物、嵌段共聚物微观相分离、两亲性分子自组装成膜,仿生囊泡的形成和分裂等诸多方面,这些成果为DPD方法应用于乳液聚合的研究打下基础。另外,结合MC方法建立的DPD聚合反应模型已经出现,这样通过结合胶束模型与聚合反应模型使得DPD方法研究乳液聚合反应成为可能。
本文利用反应的耗散粒子动力学模拟方法来考察表面活性剂链长、单体浓度、引发剂浓度、以及聚合反应速率对聚合物分子量及其分布的影响,其中DPD反应模型只考虑链增长和链终止反应,链引发过程忽略不计。主要研究内容包括以下几个方面:
1.在模拟的开始阶段,我们首先要考察不同组分间的相互作用参数以控制自组装胶束的结构和稳定性。涉及的相互作用参数分别为水(W)和表面活性剂亲水端A之间的相互作用参数αWA,水和表面活性剂疏水端B的相互作用参数αWB,以及水与单体(M)的相互作用参数αWM。通过对以上三个相互作用参数的考察我们得出结论:表面活性剂的亲水端A在控制胶束尺寸方面起到了决定性的作用,增大表面活性剂疏水端B和单体的疏水性能够改进胶束中单体的包裹性。鉴于这些结论,在乳液聚合模拟中,我们设置αWA= 24,αWB= 30,αWM= 100来同时实现胶束具有好的分散性和胶束中单体粒子具有好的包裹性。
2.研究了单体浓度、引发剂浓度、聚合反应速率、表面活性剂链长四种不同的因素对乳液聚合过程中分子量分布规律的影响。我们发现:
(1)单体浓度的影响:在相同的单体浓度下,多分散指数PDI随单体转化率的增大而增大。而在不同的转化率下,不同的单体浓度下PDI值变化并不十分一致。当转化率非常低时,各个单体浓度下的PDI值差异性不大。随着转化率的增大,PDI值随着单体浓度的增大递增明显。随着单体浓度的增加,生成的长链高分子数量所占的比例越大,分子量分布变宽,在乳液聚合过程中,要想得到高分子量的聚合物,提高单体浓度是有效途径之一。
(2)引发剂浓度的影响:引发剂浓度是控制分子量的一个有效因素,引发剂浓度越高,所得聚合物平均分子量越低,分子量分布越宽。这是由于增大引发剂浓度意味着产生更多的自由基,更多的自由基聚合则会导致最终生成的高分子链数目增加,在相同单体转化率下,随着高分子链数目增加,短链分子数目所占的比例增大,长链分子所占的比例降低,进而数均分子量与重均分子量同时下降。
(3)引发活性的影响:随着引发活性的增加,中链分子数目所占比例增加,分子量分布变窄。变窄的分子量分布是由于中链分子比例增加的直接结果。
(4)表面活性剂链长的影响:在不同的表面活性剂链长下,重均分子量、数均分子量和分子量分布并没有统一的变化规律,但是与胶束尺寸变化一致。这说明表面活性剂的链长直接决定了胶束尺寸,胶束尺寸在这一考察对象中完全依赖于表面活性剂链长和表面活性剂浓度,所以选择有效的表面活性剂从而得到合适的胶束尺寸是必要的。
Emulsion polymerization is a very important industrial method for the synthesis of polymers for a wide variety of applications ranging from coatings and adhesives to biomedical applications. Water-based products via emulsion polymerization are the current direction of development. Countries in the world compete in dedicating to the research, development and application of the polymer latex because of its wide potential market and the applications. As the most important parameters characterizing the polymer products, molecular weight and molecular weight distribution (MWD) are the nevertheless target of emulsion polymerization science and industry. They can be influenced by many factors, such as various reactions in the aqueous and polymer phases, monomer concentrations, surfactants, initiator concentrations and polymerization rate. Exploring the molecular weight and molecular weight distribution dependent on the emulsion polymerization mechanism and the contents is very important for the design of polymer materials to meet the target of improving the materials properties.
Computation simulations may be helpful to tackle the problem by exactly controlling a single factor to probe the detailed information on microscopic level and dynamic process, and it can visualize these physical processes directly to helps us understand and explore the laws in these natural phenomena. In this dissertation, we carry out comprehensive dissipative particle dynamics simulations (DPD) to study the emulsion polymerization. Within the DPD method, all the particles interact with each other through three pairwise forces: a conservative force, a dissipative force, and a random force, and the pair-wise interactions result the momentum of the system being conserved. These forces are very soft, so the integration time steps can be very large, the time scale in DPD simulation can be at milliseconds. It’s also due to the soft repulsions, we can unite some molecules or polymer segments into one DPD bead, thus the DPD model can be used to study the systems at mesoscopic length scale. DPD method has been applied on the study of polymer blends, microphase separation of the block copolymers, self-organizing of amphiphilic molecules into membrane, and the budding and fission of bionic micelles. These studies become the research foundation of the model of micelles formation. In addition, DPD model by incorporating Monte Carlo reaction model has been occurred. A combination of these methods makes it possible to study the emulsion polymerization process.
In this study, the MWD on emulsion polymerization with four different factors has been investigated by DPD simulations, including monomer concentration, initiator concentration, polymerization rate and surfactant chain length. Chain propagation and bimolecular termination were considered. Here we have omitted initiation process. The main results are as follows:
1. In the beginning of the simulations, we need to know the interaction parameters between different species control the structure and stability of self-assembled micelles. Including the interaction parameterαWA between water (W) and the hydrophilicity of the solvable surfactant A block, the interaction parameterαWB between water (W) and the hydrophibicity of the solvable surfactant B block and the interaction parameterαWM between water (W) and the monomer (M). We can conclude that, the hydrophilicity of the solvable surfactant A block (αWA) plays a predominate role in controlling the micelle size, increasing the hydrophibicity of surfactant B and monomer improves the wrapping of monomer beads inside the micelles. Following these criteria, in our simulations of emulsion polymerization, we chooseαWA= 24,αWB= 30,αWM= 100 to obtain the micelles with both good dispersion and stable structure while the monomers are wrapped well by the surfactants.
2. The influences we focused on are monomer concentration, initiator concentration, polymerization, surfactant chain length on the MWD.
(1) The effect of monomer concentration: The polydispersity index (PDI) increases with increasing monomer conversion at the same monomer concentration. But at the same conversion, PDI varies differently with different monomer concentration. In the early stage of polymerization, the monomer conversion is low, and the difference among the values of PDI for the five different concentrations is not very obvious. The difference between PDI enlarges when the monomer conversion is high. The ratio of long chain increases with increasing monomer concentration and the MWD become wide. We can conclude that monomer concentration plays an important role on obtaining high molecular weight.
(2) The effect of initiation concentration: Initiator concentration is also an efficient way to control the molecular weight distribution. Higher initiator concentration decreases the molecular weight by creating more the polymer chain number with a more widen MWD. That because more initiation concentration can create more radical number, and then create more polymer chain number. With increasing initiator concentration, the ratio of short chain increases and the ratio of long chain decreases at the same conversion. Then we obtain the decrease of average molecular weight and number average molecular weight at the same time.
(3) The effect of initiator activity: The ratio of medium chain increases with increasing initiator activity with a narrowed MWD. The narrowed MWD is contributed to the increase of the ratio of medium chain.
(4) The effect of surfactant chain length: There is not obvious law at the average molecular weight, number average molecular weight and MWD with increasing the surfactant chain length, except for micelle size. That indicate the surfactant chain length determined the micelle size completely. In this part, micelle size completely depends on the surfactant chain length and concentration, so it is necessary to select appropriate surfactant for appropriate micelle size.
涉及微观机理的细节描述,计算机模拟技术是一个很重要的手段,因为计算机模拟方法可以通过改变单一因素对某一性质的变化进行考察,可以直接给出这些物理现象的直观图像,帮助人们从本质上理解和认识其规律。本论文利用耗散粒子动力学模拟(DPD)方法,对乳液聚合进行了细致深入地研究。在DPD方法中,粒子之间的受力包括三部分——保守力、耗散力、和随机力,每一部分相互作用力都是成对出现的,使得体系的动量保持守恒。由于这些力都是非常软的相互作用力,因此DPD中积分步长可以选得很长,模拟的时间尺度可达到毫秒级。也正是因为这种软相互作用势,我们可以把微观尺度上的几个分子甚至是高分子链中的若干个片段粗粒化成DPD模型中的一个粒子,从而使DPD方法可以用来描述在微米级别上的体系。目前,这个方法已经被成功地应用到高聚物共混物、嵌段共聚物微观相分离、两亲性分子自组装成膜,仿生囊泡的形成和分裂等诸多方面,这些成果为DPD方法应用于乳液聚合的研究打下基础。另外,结合MC方法建立的DPD聚合反应模型已经出现,这样通过结合胶束模型与聚合反应模型使得DPD方法研究乳液聚合反应成为可能。
本文利用反应的耗散粒子动力学模拟方法来考察表面活性剂链长、单体浓度、引发剂浓度、以及聚合反应速率对聚合物分子量及其分布的影响,其中DPD反应模型只考虑链增长和链终止反应,链引发过程忽略不计。主要研究内容包括以下几个方面:
1.在模拟的开始阶段,我们首先要考察不同组分间的相互作用参数以控制自组装胶束的结构和稳定性。涉及的相互作用参数分别为水(W)和表面活性剂亲水端A之间的相互作用参数αWA,水和表面活性剂疏水端B的相互作用参数αWB,以及水与单体(M)的相互作用参数αWM。通过对以上三个相互作用参数的考察我们得出结论:表面活性剂的亲水端A在控制胶束尺寸方面起到了决定性的作用,增大表面活性剂疏水端B和单体的疏水性能够改进胶束中单体的包裹性。鉴于这些结论,在乳液聚合模拟中,我们设置αWA= 24,αWB= 30,αWM= 100来同时实现胶束具有好的分散性和胶束中单体粒子具有好的包裹性。
2.研究了单体浓度、引发剂浓度、聚合反应速率、表面活性剂链长四种不同的因素对乳液聚合过程中分子量分布规律的影响。我们发现:
(1)单体浓度的影响:在相同的单体浓度下,多分散指数PDI随单体转化率的增大而增大。而在不同的转化率下,不同的单体浓度下PDI值变化并不十分一致。当转化率非常低时,各个单体浓度下的PDI值差异性不大。随着转化率的增大,PDI值随着单体浓度的增大递增明显。随着单体浓度的增加,生成的长链高分子数量所占的比例越大,分子量分布变宽,在乳液聚合过程中,要想得到高分子量的聚合物,提高单体浓度是有效途径之一。
(2)引发剂浓度的影响:引发剂浓度是控制分子量的一个有效因素,引发剂浓度越高,所得聚合物平均分子量越低,分子量分布越宽。这是由于增大引发剂浓度意味着产生更多的自由基,更多的自由基聚合则会导致最终生成的高分子链数目增加,在相同单体转化率下,随着高分子链数目增加,短链分子数目所占的比例增大,长链分子所占的比例降低,进而数均分子量与重均分子量同时下降。
(3)引发活性的影响:随着引发活性的增加,中链分子数目所占比例增加,分子量分布变窄。变窄的分子量分布是由于中链分子比例增加的直接结果。
(4)表面活性剂链长的影响:在不同的表面活性剂链长下,重均分子量、数均分子量和分子量分布并没有统一的变化规律,但是与胶束尺寸变化一致。这说明表面活性剂的链长直接决定了胶束尺寸,胶束尺寸在这一考察对象中完全依赖于表面活性剂链长和表面活性剂浓度,所以选择有效的表面活性剂从而得到合适的胶束尺寸是必要的。
Emulsion polymerization is a very important industrial method for the synthesis of polymers for a wide variety of applications ranging from coatings and adhesives to biomedical applications. Water-based products via emulsion polymerization are the current direction of development. Countries in the world compete in dedicating to the research, development and application of the polymer latex because of its wide potential market and the applications. As the most important parameters characterizing the polymer products, molecular weight and molecular weight distribution (MWD) are the nevertheless target of emulsion polymerization science and industry. They can be influenced by many factors, such as various reactions in the aqueous and polymer phases, monomer concentrations, surfactants, initiator concentrations and polymerization rate. Exploring the molecular weight and molecular weight distribution dependent on the emulsion polymerization mechanism and the contents is very important for the design of polymer materials to meet the target of improving the materials properties.
Computation simulations may be helpful to tackle the problem by exactly controlling a single factor to probe the detailed information on microscopic level and dynamic process, and it can visualize these physical processes directly to helps us understand and explore the laws in these natural phenomena. In this dissertation, we carry out comprehensive dissipative particle dynamics simulations (DPD) to study the emulsion polymerization. Within the DPD method, all the particles interact with each other through three pairwise forces: a conservative force, a dissipative force, and a random force, and the pair-wise interactions result the momentum of the system being conserved. These forces are very soft, so the integration time steps can be very large, the time scale in DPD simulation can be at milliseconds. It’s also due to the soft repulsions, we can unite some molecules or polymer segments into one DPD bead, thus the DPD model can be used to study the systems at mesoscopic length scale. DPD method has been applied on the study of polymer blends, microphase separation of the block copolymers, self-organizing of amphiphilic molecules into membrane, and the budding and fission of bionic micelles. These studies become the research foundation of the model of micelles formation. In addition, DPD model by incorporating Monte Carlo reaction model has been occurred. A combination of these methods makes it possible to study the emulsion polymerization process.
In this study, the MWD on emulsion polymerization with four different factors has been investigated by DPD simulations, including monomer concentration, initiator concentration, polymerization rate and surfactant chain length. Chain propagation and bimolecular termination were considered. Here we have omitted initiation process. The main results are as follows:
1. In the beginning of the simulations, we need to know the interaction parameters between different species control the structure and stability of self-assembled micelles. Including the interaction parameterαWA between water (W) and the hydrophilicity of the solvable surfactant A block, the interaction parameterαWB between water (W) and the hydrophibicity of the solvable surfactant B block and the interaction parameterαWM between water (W) and the monomer (M). We can conclude that, the hydrophilicity of the solvable surfactant A block (αWA) plays a predominate role in controlling the micelle size, increasing the hydrophibicity of surfactant B and monomer improves the wrapping of monomer beads inside the micelles. Following these criteria, in our simulations of emulsion polymerization, we chooseαWA= 24,αWB= 30,αWM= 100 to obtain the micelles with both good dispersion and stable structure while the monomers are wrapped well by the surfactants.
2. The influences we focused on are monomer concentration, initiator concentration, polymerization, surfactant chain length on the MWD.
(1) The effect of monomer concentration: The polydispersity index (PDI) increases with increasing monomer conversion at the same monomer concentration. But at the same conversion, PDI varies differently with different monomer concentration. In the early stage of polymerization, the monomer conversion is low, and the difference among the values of PDI for the five different concentrations is not very obvious. The difference between PDI enlarges when the monomer conversion is high. The ratio of long chain increases with increasing monomer concentration and the MWD become wide. We can conclude that monomer concentration plays an important role on obtaining high molecular weight.
(2) The effect of initiation concentration: Initiator concentration is also an efficient way to control the molecular weight distribution. Higher initiator concentration decreases the molecular weight by creating more the polymer chain number with a more widen MWD. That because more initiation concentration can create more radical number, and then create more polymer chain number. With increasing initiator concentration, the ratio of short chain increases and the ratio of long chain decreases at the same conversion. Then we obtain the decrease of average molecular weight and number average molecular weight at the same time.
(3) The effect of initiator activity: The ratio of medium chain increases with increasing initiator activity with a narrowed MWD. The narrowed MWD is contributed to the increase of the ratio of medium chain.
(4) The effect of surfactant chain length: There is not obvious law at the average molecular weight, number average molecular weight and MWD with increasing the surfactant chain length, except for micelle size. That indicate the surfactant chain length determined the micelle size completely. In this part, micelle size completely depends on the surfactant chain length and concentration, so it is necessary to select appropriate surfactant for appropriate micelle size.
引文
[1]潘祖仁.高分子化学[M].北京:化学工业出版社,2003.
[2] HOFFMAN W. Nitrile Rubber [M]. Akron: ACS, 1967.
[3] BLACKLEY D C. Emulsion polymerization: theory and practice [M]. New York: Applied Science Publishers Ltd, 1975.
[4] LOVELL P, EL-AASSER M S (Eds.). Emulsion polymerization and emulsion polymers [M]. New York: Wiley, 1997.
[5] BLACKLEY D C. Emulsion polymerization [M]. London: Applied Science Publisher Limited, 1975.
[6] HARKINS W D. A general theory of the reaction loci in emulsion polymerization [J]. J. Chem. Phys., 1945, 13: 381-382.
[7] HARKINS W D. A general theory of the reaction loci in emulsion polymerization. II [J]. J. Chem. Phys., 1946, 14: 47-48.
[8] HARKINS W D. A general theory of the mechanism of emulsion polymerization [J]. J. Am. Chem. Soc., 1947, 69: 1428-1444.
[9] SMITH W V, EWART R H. Kinetics of emulsion polymerization [J]. J. Chem. Phys., 1948, 16: 592-599.
[10] SMITH W V. The kinetics of styrene emulsion polymerization [J]. J. Am. Chem. Soc., 1948, 70: 3695-3702.
[11] SMITH W V. Chain initiation in styrene emulsion polymerization [J]. J. Am. Chem. Soc., 1949, 71: 4077-4082.
[12] GARDON J L. Emulsion polymerization. I. Recalculation and extension of the Smith–Ewart theory [J]. J. Polym. Sci. A 1, 1968, 6: 623-641.
[13] GARDON J L. Emulsion polymerization. II. Review of experimental data in the context of the revised Smith–Ewart theory [J]. J. Polym. Sci. A 1, 1968, 6: 643–664.
[14]刘凤崎,汤心颐.高分子物理[M].北京:高等教育出版社,1995.
[15]何曼君,陈维孝,董西侠.高分子物理[M].上海:复旦大学出版社,1990.
[16]杨小震.高分子的计算机模拟研究进展[J].计算机与应用化学,1999,16:321-324.
[17]尤丽艳.嵌段共聚物的非平衡态耗散粒子动力学研究[D].长春:吉林大学理论化学研究所,2009.
[18]何彦东.高分子在受限环境中结构和动力学的介观模拟研究[D].长春:吉林大学理论化学研究所,2009.
[19]杨小震.高分子科学的今天与明天:高分子的计算机模拟[M].北京:化学工业出版社, 1994, 182-193.
[20]赵英.并行高性能耗散粒子动力学程序开发及其应用[D].长春:吉林大学理论化学研究所,2009.
[21]钱虎军.嵌段共聚物微观相分离及高分子表面扩散动力学的耗散粒子动力学研究[D].长春:吉林大学理论化学研究所,2007.
[22] WANG X L, LU Z Y, LI Z S. Molecular dynamics simulation study on controlling the adsorption behavior of polyethylene by fine tuning the surface nanodecoration of graphite [J]. Langmuir, 2007, 23: 802-808.
[23] QIAN H J, LU Z Y, CHEN L J, et al. Computer simulation of cyclic block copolymer microphase separation [M]. Macromolecules, 2005, 38: 1395-1401.
[24] GROOT R D, MADDEN T J. Dynamic simulation of diblock copolymer microphase separation [J]. J. Chem. Phys., 1998, 108: 8713-8724.
[25] REKVIG L, HAFSKJOLD B, SMIT B. Chain length dependencies of the bending modulus of surfactant monolayers [J]. Phys. Rev. Lett., 2004, 92: 116101.
[26] WANG X L, QIAN H J, CHEN L J. Dissipative particle dynamics simulation on the polymer membrane formation by immersion precipitation [J]. J. Membr. Sci., 2008, 311: 251-258.
[27] YAMAMOTO S, HYODO S A. Budding and fission dynamics oftwo-component vesicles [J]. J. Chem. Phys., 2003, 118: 7937-7943.
[28] LARADJI M, SUNIL KUMAR P B. Dynamics of domain growth in self-assembled fluid vesicles [J]. Phys. Rev. Lett., 2004, 93: 198105.
[29] ROE R J. Computer simulation of polymers [M]. New Jersey: Prentice Hall, 1991.
[30] LIU H, XUE Y H, QIAN H J, et al. A practical method to avoid bond crossing in two-dimensional dissipative particle dynamics simulations [J]. J. Chem. Phys., 2008, 129: 024902.
[31] QIAN H J, CHEN L J, LU Z Y, et al. Surface diffusion dynamics of a single polymer chain in dilute solution [J]. Phys. Rev. Lett., 2007, 99: 068301.
[32] LI Z W, LU Z Y, SUN Z Y, et al. Calculating the equation of state parameters and predicting the spinodal curve of isotactic polypropylene/poly (ethylene-co-octene) blend by molecular dynamics simulations combined with sanchez-lacombe lattice fluid theory [J]. J. Phys. Chem. B, 2007, 111: 5934-5940.
[33] HE Y D, QIAN H J, LU Z Y, et al. Polymer translocation through a nanopore in mesoscopic simulations [J]. Polymer, 2007, 48: 3601-3606.
[34] QIAN H J, LU Z Y, CHEN L J, LI Z S, SUN C C. Dissipative particle dynamics study on the interfaces in incompatible A/B homopolymer blends and with their block copolymers [J]. J. Chem. Phys., 2005, 122:184907.
[35] CHEN S, GUO C, HU G H, LIU H Z, LIANG X F, WANG J, MA J H, ZHENG L. Dissipative particle dynamics simulation of gold nanoparticles stabilization by PEO-PPO-PEO block copolymer micelles [J]. Colloid Polym. Sci., 2007, 285: 1543-1552.
[36] ZHAO Y, LIU Y T, LU Z Y, SUN C C. Effect of molecular architecture on the morphology diversity of the multicompartment micelles: A dissipative particle dynamics simulation study [J]. Polymer, 2008, 49: 4899-4909.
[37] YOU L Y, CHEN L J, QIAN H J. LU Z Y. CHEN L J. Microphase transitionsof perforated lamellae of cyclic diblock copolymers under steady shear [J]. Macromolecules, 2007, 40: 5222-5227.
[38] QIAN H J, CHEN L J, LU Z Y, et al. Surface diffusion dynamics of a single polymer chain in dilute solution [J]. Phys. Rev. Lett., 2007, 99: 068301.
[39]陈梨俊.以耗散粒子动力学为纽带的多尺度贯通模拟方法[D].长春:吉林大学理论化学研究所,2007.
[40]刘鸿.高分子体系活性聚合反应的计算机模拟研究[D].长春:吉林大学理论化学研究所,2010.
[41]刘英涛.梳型嵌段共聚物聚集态结构的耗散粒子动力学模拟[D].长春:吉林大学理论化学研究所,2010.
[42]王晓琳.表面吸附受限高分子体系结构及动力学性质的计算机模拟研究[D].长春:吉林大学理论化学研究所,2008.
[43] YOU L Y, HE Y D, ZHAO Y, et al. The complex influence of the oscillatory shear on the melt of linear diblock copolymers [J]. J. Chem. Phys., 2008, 129: 204901.
[44] ALLEN M P, TILDESLEY D J. Computer simulation of liquids [M]. Oxford: Clarendon, 1987.
[45] FRENKEL D, SMIT B. Understanding molecular simulation: from algorithms to applications [M]. San Diego: Academic Press, 1996.
[46] RAPPAPORT D C. The art of molecular dynamics simulation [M]. Cambridge: Cambridge Univ Press, 1995.
[47] SARMAN S S, EVANS D J, CUMMINGS P T. Recent developments in non-Newtonian molecular dynamics [J]. Phys. Rep., 1998, 305: 1-92.
[48] FLORY P J. Statistical mechanics of chain molecules [M]. Munich: Hanser, 1989.
[49] LEACH A R. Molecular modeling principles and applications [M]. Essex: Longman, 1996.
[50]杨小震.分子模拟与高分子材料[M].北京:科学出版社,2002.
[51] MSI MANUAL. Forcefield-based simulations general theory & methodology [M]. 1997.
[52] SWOPE W C, ANDERSEN H C, BERENS P H, WILSON K R. A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters [J]. J. Chem. Phys., 1982, 76: 637-649.
[53]刘光恒,戴树珊.化学应用统计力学[M].北京:科学出版社,2001.
[54]李如生.平衡和非平衡统计力学[M].北京:清华大学出版社,1995.
[55] HUR J S, SHAQFEH E S G, LARSON R G. Brownian dynamics simulation of single DNA molecules in shear flow [J]. J. Rheol., 2000, 44: 713- 742.
[56] LIU T W. Flexible polymer chain dynamics and rheological properties in steady flows [J]. J. Chem. Phys., 1989, 90: 5826-5842.
[57] ZYLKA W, ?TTINGER H C. A comparison between simulations and various approximations for Hookean dumbbells with hydrodynamic interaction [J]. J. Chem. Phys., 1989, 90: 474-480.
[58] GRASSIA P, HINCH E J F. Computer simulations of polymer chain relaxation via Brownian motion [J]. J. Fluid Mech., 1996, 308: 255-288.
[59] DOYLE P S, SHAQFEH E S G, GAST A P. Dynamic simulation of freely draining flexible polymers in steady linear flows [J]. J. Fluid Mech., 1997, 334: 251-291.
[60] BINDER K. Monte carlo methods in condensed matter physics [M]. Berlin: Springer-Verlag, 1992.
[61] BINDER K. Applications of the Monte Carlo method in statistical physics [M]. Berlin: Springer-Verlag, 1984.
[62] LIU Y, YANG X Z, YANG M J, et al. Mesoscale simulation on the shape evolution of polymer drop and initial geometry influence [J]. Ploymer, 2004, 45: 6985-6991.
[63] CLARK A T, LAL M, RUDDOCK J N, et al. Mesoscopic simulation of dropsin gravitational and shear fields [J]. Langmuir, 2000, 16: 6342-6350.
[64] JONES J L, LAL M, RUDDOCK J N, et al. Dynamics of a drop at a liquid/solid interface in simple shear fields: a mesoscopic simulation study [J]. Faraday Disc., 1999, 112: 129-142.
[65] ESPA?OL P, SERRANO M, ZUNIGA I. Coarse graining of a fluids and its relation with dissipative particle dynamics and smoothed particle dynamics [J]. Int. J. Mod. Phys. C, 1997, 8: 899- 908.
[66]李泽生,吕中元.计算机模拟方法及其在物理化学中的应用,新世纪的物理化学学科前沿与展望(国家自然科学基金委员会化学科学部组编,梁文平、杨俊林、陈拥军、李灿烂主编)[M].北京:科学出版社,2004.
[67] BOGHOSIAN B M, ALEXANDER F J, COVENEY P V. Guest editors′preface-discrete models of complex fluid dynamics [J]. Int. J. Mod. Phys., 1997, 8: 637- 640.
[68] HOOGERBRUGGE P J, KOELMAN J M V A. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics [J]. Europhys. Lett., 1992, 19: 155-160.
[69] ESPA?OL P, WARREN P B. Statistical mechanics of dissipative particle dynamics [J]. Europhys. Lett., 1995, 30: 191-196.
[70] GROOT R D, WARREN P B. Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation [J]. J. Chem. Phys., 1997, 107: 4423-4435.
[71] CHEN S, DOOLEN G D. Lattice Boltzmann method for fluid flows [J]. Annu. Rev. Fluid. Mech., 1998, 30: 329-364.
[72] BENZI R, SUCCI S, VERGSSOLA M. The Lattice Boltzmann equation: theory and applications [J]. Phys. Rep., 1992, 222: 145-197.
[73] GONNELLA G, ORLANDINI E, YEOMANS J M. Phase separation in two-dimensional fluids: The role of noise [J]. Phys. Rev. E, 1999, 59: R4741-R4744.
[74] WAGNER A J, YEOMANS J M. Phase separation under shear in two-dimensional binary fluids [J]. Phys. Rev. E, 1999, 59: 4366-4373.
[75] ORLANDINI E, GONNELLA G, YEOMANS J M. Lattice Boltzmann study of spinodal decomposition in structured fluids [J]. Physical A, 1997, 240: 277-285.
[76] GONNELLA G., ORLANDINI E, YEOMANS J M. Spinodal decomposition to a lamellar phase: Effects of hydrodynamic flow [J]. Phys. Rev. Lett., 1997, 78: 1695-1698.
[77] FRISCH U, HASSLACHER B, POMEAU Y. Lattice-gas automata for the Navier-Stokes equations [J]. Phys. Rev. Lett., 1986, 56: 1505-1508.
[78] AHLRICHS P, DüNWEG B. Simulation of a single polymer chain in solution by combining lattice Boltzmann and molecular dynamics [J]. J. Chem. Phys., 1999, 111: 8225-8239.
[79] DENNISTON C, ORLANDINI E, YEOMANS J M. Phase ordering in nematic liquid crystals [J]. Phys. Rev. E, 2001, 64: 021701.
[80] GUSEV A A. Representative volume element size for elastic composites: A numerical study [J]. J. Mech. Phys. Solids., 1997, 45: 1449-1459.
[81] KASHIYAMA K, SAITOH K, BEHR M, TEZDUYAR T. Parallel finite element methods for large scale computation of storm surges and tidal flows [J]. Int. J. Numerical methods in fluids, 1997, 24: 1371-1380.
[82] GEORGE P L. Automatic mesh generation: Application to finite element methods [M]. New York: Wiley, 1991.
[83] KOELMAN J M V A, HOOGERBRUGGE P J. Dynamic simulations of hard-sphere suspensions under steady shear [J]. Europhys. Lett., 1993, 21: 363-368.
[84] KONG Y, MANKE C W, MADDEN W G, et al. Simulation of a confined polymer in solution using the dissipative particle dynamics method [J]. Int. J. Themophys., 1994, 15: 1093-1101.
[85] SCHLIJPER A G, HOOGERBRUGGE P G, MANKE C W. Computer simulation of dilute polymer solutions with the dissipative particle dynamics method [J]. J. Rheol, 1995, 39: 567-579.
[86] DüNWEG B, PAUL W. Brownian dynamics simulations without Gaussian random numbers [J]. Int. J. Mod. Phys. C, 1991, 2: 817-827.
[87] TUCKERMAN M E, MARTYNA G J. Understanding modern molecular dynamics: Techniques and applications [J]. J. Phys. Chem. B, 2000, 104: 159-178.
[88] NOVIK K E, COVENEY P V. Finite-difference methods for simulation models incorporating nonconservative forces [J]. J. Chem. Phys., 1998, 109: 7667-7677.
[89] PAGONABARRAGA I, HAGEN M H J, FRENKEL D. Self-consistent dissipative particle dynamics algorithm [J]. Europhys. Lett., 1998, 42: 377-382.
[90] GIBSON J B, CHEN K, CHYNOWETH S. The equilibrium of a velocity-Verlet type algorithm for DPD with finite time steps [J]. Int. J. Mod. Phys. C, 1999, 10: 241-261.
[91] VATTULAINEN I, KARTTUNEN M, BESOLD G, et al. Integration schemes for dissipative particle dynamics simulations: From softly interacting systems towards hybrid models [J]. J. Chem. Phys., 2002, 116: 3967-3979.
[92] NIKUNEN P, KARTTUNEN M, VATTULAINEN I. How would you integrate the equations of motion in dissipative particle dynamics simulations? [J]. Comp. Phys. Comm., 2003, 153: 407-423.
[93] LOWE C P. An alternative approach to dissipative particle dynamics [J]. Europhys. Lett., 1999, 47: 145-151.
[94] ANDERSEN H C. Molecular dynamics simulations at constant pressure and/or temperature [J]. J. Chem. Phys., 1980, 72: 2384-2393.
[95] DüNWEG B. Advanced Simulations for Hydrodynamic Problems: LatticeBoltzmann and Dissipative Particle Dynamics[R], In Attig N, Binder K, Grubmüller H, K. Kremer, editors, Computational Soft Matter: From Synthetic Polymers to Proteins, Lecture Notes[I]. Jülich, John von Neumann Institute for Computing, NIC Series, 2004, Vol. 23, ISBN 3-00-012641-4: pp. 61-82.
[96] SCHNEIDER T, STOLL E. Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions [J]. Phys. Rev. B, 1978, 17: 1302-1322.
[97] CHANDRASEKHAR S. Stochastic problems in physics and astronomy [J]. Rev. Mod. Phys., 1943, 15: 1-89.
[98] RISKEN H. The Fokker-Planck equation [M]. Berlin: Springer-Verlag, 1984.
[99] DüNWEG B. Langevin methods, In Dünweg B, Landau D P, Milchev A I, editors. Computer simulation of surfaces and interfaces [M]. Dordrecht: Kluwer, 2003.
[100] GARDINER C W. Handbook of stochastic methods [M]. Berlin: Springer-Verlag, 1983.
[101] GROOT R D, RABONE K L. Mesoscopic simulation of cell membrane damage, morphology change and rupture by nonionic surfactants [J]. Biophysical Journal, 81: 725-736.
[102] SUN H. Ab initio characterizations of molecular structures, conformation energies, and hydrogen-bonding properties for polyurethane hard segments [J]. Macromolecules, 1994, 26: 5924-5936.
[103] ANTONIETTI M, TAUER K. 90 years of polymer latexes and heterophase polymerization: More vital than ever [J]. Macromol. Chem. Phys., 2003, 204: 207-219.
[104] HERNANDEZ H F, TAUER K. Radical desorption kinetics in emulsion polymerization. 1. theory and simulation [J]. Ind. Eng. Chem. Res., 2008, 47: 9795-9811.
[105] ARZAMENDI G, LEIZA J R. Molecular weight distribution (soluble and insoluble fraction) in emulsion polymerization of acrylate monomers by Monte Carlo simulations [J]. Ind. Eng. Chem. Res., 2008, 47: 5934-5947.
[106] TOBITA H. RAFT miniemulsion polymerization kinetics, 2-molecular weight distribution [J]. Macromol. Theory Simul., 2009, 18: 120-126.
[107] VENTUROLI, M, SMIT B. Simulating the self-assembly of model membranes [J]. Phys. Chem. Comm., 1999, 10: 1-5.
[108] KRANENBURG M, VENTUROLI M, SMIT B. Phase behavior and induced interdigitation in bilayers studied with dissipative particle dynamics [J]. J. Phys. Chem. B, 2003, 107: 11491-11501.
[109] LARADJI M, SUNIL KUMAR P B. Dynamics of domain growth in self-assembled fluid vesicles [J]. Phys. Rev. Lett., 2004, 93: 198105.
[110] SHILLCOCK J C, LIPOWSKY R. Equilibrium structure and lateral stress distribution of amphiphilic bilayers from dissipative particle dynamics simulations [J]. J. Chem. Phys., 2002, 117: 5048-5061.
[111] SHILLCOCK J C, LIPOWSKY R. Tension-induced fusion of bilayer membranes and vesicles [J]. Nat. Mater., 2005, 4: 225-228.
[112] YAMAMOTO S, MARUYAMA Y, HYODO S. Dissipative particle dynamics study of spontaneous vesicle formation of amphiphilic molecules [J]. J. Chem., 2002, 116: 5842-5849.
[113] LIU Y T, ZHAO Y, LIU H, LIU Y H, LU Z Y. Spontaneous fusion between the vesicles formed by A (2n) (B-2) (n) type comb-like block copolymers with a semiflexible hydrophobic backbone [J]. J. Phys. Chem. B, 2009, 113: 15256-15262.
[114] ORTIZ V, NIELSEN S O, DISCHER D E, KLEIN M L, LIPOWSKY R, SHILLCOCK J. Dissipative particle dynamics simulations of polymersomes [J]. J. Phys. Chem. B, 2005, 109: 17708-17714.
[115] ZHONG C L, LIU D H. Understanding multicompartment micelles usingdissipative particle dynamics simulation [J]. Macromolecular Theory, 2007, 16: 141-157.
[116] REKVIG L, HAFSKJOLD B, SMIT B. Molecular simulations of surface forces and film rupture in oil/water/surfactant systems [J]. Langmuir, 2004, 20: 11583-11593.
[117] JURY S, BLADON P, CATES M, KRISHNA S, HAGEN M, RUDDOCK N, WARREN P. Simulation of amphiphilic mesophases using dissipative particle dynamics [J]. Phys. Chem. Chem. Phys., 1999, 1: 2051-2056.
[118] SCHULZ S G, KUHN H, SCHMID G, MUND C, VENZMER J. Phase behavior of amphiphilic polymers: A dissipative particles dynamics study [J]. Colloid Polym. Sci., 2004, 283: 284-290.
[119] MALFREYT P, TILDESLEY D J. Dissipative particle dynamics simulations of grafted polymer chains between two walls [J]. Langmuir, 2000, 16: 4732-4740.
[120] CLARK A T, LAL M, RUDDOCK J N, WARREN P B. Mesoscopic simulation of drops in gravitational and shear fields [J]. Langmuir, 2000, 16: 6342-6350.
[121] LIU H, QIAN H J, ZHAO Y, LU Z Y. Dissipative particle dynamics simulation study on the binary mixture phase separation coupled with polymerization [J]. J. Chem. Phys., 2007, 127:144903.
[122] LIU H, LI M, LU Z Y, ZHANG Z G, SUN C C. Influence of surface-initiated polymerization rate and initiator density on the properties of polymer brushes [J]. Macromolecules, 2009, 42: 2863-2872.
[2] HOFFMAN W. Nitrile Rubber [M]. Akron: ACS, 1967.
[3] BLACKLEY D C. Emulsion polymerization: theory and practice [M]. New York: Applied Science Publishers Ltd, 1975.
[4] LOVELL P, EL-AASSER M S (Eds.). Emulsion polymerization and emulsion polymers [M]. New York: Wiley, 1997.
[5] BLACKLEY D C. Emulsion polymerization [M]. London: Applied Science Publisher Limited, 1975.
[6] HARKINS W D. A general theory of the reaction loci in emulsion polymerization [J]. J. Chem. Phys., 1945, 13: 381-382.
[7] HARKINS W D. A general theory of the reaction loci in emulsion polymerization. II [J]. J. Chem. Phys., 1946, 14: 47-48.
[8] HARKINS W D. A general theory of the mechanism of emulsion polymerization [J]. J. Am. Chem. Soc., 1947, 69: 1428-1444.
[9] SMITH W V, EWART R H. Kinetics of emulsion polymerization [J]. J. Chem. Phys., 1948, 16: 592-599.
[10] SMITH W V. The kinetics of styrene emulsion polymerization [J]. J. Am. Chem. Soc., 1948, 70: 3695-3702.
[11] SMITH W V. Chain initiation in styrene emulsion polymerization [J]. J. Am. Chem. Soc., 1949, 71: 4077-4082.
[12] GARDON J L. Emulsion polymerization. I. Recalculation and extension of the Smith–Ewart theory [J]. J. Polym. Sci. A 1, 1968, 6: 623-641.
[13] GARDON J L. Emulsion polymerization. II. Review of experimental data in the context of the revised Smith–Ewart theory [J]. J. Polym. Sci. A 1, 1968, 6: 643–664.
[14]刘凤崎,汤心颐.高分子物理[M].北京:高等教育出版社,1995.
[15]何曼君,陈维孝,董西侠.高分子物理[M].上海:复旦大学出版社,1990.
[16]杨小震.高分子的计算机模拟研究进展[J].计算机与应用化学,1999,16:321-324.
[17]尤丽艳.嵌段共聚物的非平衡态耗散粒子动力学研究[D].长春:吉林大学理论化学研究所,2009.
[18]何彦东.高分子在受限环境中结构和动力学的介观模拟研究[D].长春:吉林大学理论化学研究所,2009.
[19]杨小震.高分子科学的今天与明天:高分子的计算机模拟[M].北京:化学工业出版社, 1994, 182-193.
[20]赵英.并行高性能耗散粒子动力学程序开发及其应用[D].长春:吉林大学理论化学研究所,2009.
[21]钱虎军.嵌段共聚物微观相分离及高分子表面扩散动力学的耗散粒子动力学研究[D].长春:吉林大学理论化学研究所,2007.
[22] WANG X L, LU Z Y, LI Z S. Molecular dynamics simulation study on controlling the adsorption behavior of polyethylene by fine tuning the surface nanodecoration of graphite [J]. Langmuir, 2007, 23: 802-808.
[23] QIAN H J, LU Z Y, CHEN L J, et al. Computer simulation of cyclic block copolymer microphase separation [M]. Macromolecules, 2005, 38: 1395-1401.
[24] GROOT R D, MADDEN T J. Dynamic simulation of diblock copolymer microphase separation [J]. J. Chem. Phys., 1998, 108: 8713-8724.
[25] REKVIG L, HAFSKJOLD B, SMIT B. Chain length dependencies of the bending modulus of surfactant monolayers [J]. Phys. Rev. Lett., 2004, 92: 116101.
[26] WANG X L, QIAN H J, CHEN L J. Dissipative particle dynamics simulation on the polymer membrane formation by immersion precipitation [J]. J. Membr. Sci., 2008, 311: 251-258.
[27] YAMAMOTO S, HYODO S A. Budding and fission dynamics oftwo-component vesicles [J]. J. Chem. Phys., 2003, 118: 7937-7943.
[28] LARADJI M, SUNIL KUMAR P B. Dynamics of domain growth in self-assembled fluid vesicles [J]. Phys. Rev. Lett., 2004, 93: 198105.
[29] ROE R J. Computer simulation of polymers [M]. New Jersey: Prentice Hall, 1991.
[30] LIU H, XUE Y H, QIAN H J, et al. A practical method to avoid bond crossing in two-dimensional dissipative particle dynamics simulations [J]. J. Chem. Phys., 2008, 129: 024902.
[31] QIAN H J, CHEN L J, LU Z Y, et al. Surface diffusion dynamics of a single polymer chain in dilute solution [J]. Phys. Rev. Lett., 2007, 99: 068301.
[32] LI Z W, LU Z Y, SUN Z Y, et al. Calculating the equation of state parameters and predicting the spinodal curve of isotactic polypropylene/poly (ethylene-co-octene) blend by molecular dynamics simulations combined with sanchez-lacombe lattice fluid theory [J]. J. Phys. Chem. B, 2007, 111: 5934-5940.
[33] HE Y D, QIAN H J, LU Z Y, et al. Polymer translocation through a nanopore in mesoscopic simulations [J]. Polymer, 2007, 48: 3601-3606.
[34] QIAN H J, LU Z Y, CHEN L J, LI Z S, SUN C C. Dissipative particle dynamics study on the interfaces in incompatible A/B homopolymer blends and with their block copolymers [J]. J. Chem. Phys., 2005, 122:184907.
[35] CHEN S, GUO C, HU G H, LIU H Z, LIANG X F, WANG J, MA J H, ZHENG L. Dissipative particle dynamics simulation of gold nanoparticles stabilization by PEO-PPO-PEO block copolymer micelles [J]. Colloid Polym. Sci., 2007, 285: 1543-1552.
[36] ZHAO Y, LIU Y T, LU Z Y, SUN C C. Effect of molecular architecture on the morphology diversity of the multicompartment micelles: A dissipative particle dynamics simulation study [J]. Polymer, 2008, 49: 4899-4909.
[37] YOU L Y, CHEN L J, QIAN H J. LU Z Y. CHEN L J. Microphase transitionsof perforated lamellae of cyclic diblock copolymers under steady shear [J]. Macromolecules, 2007, 40: 5222-5227.
[38] QIAN H J, CHEN L J, LU Z Y, et al. Surface diffusion dynamics of a single polymer chain in dilute solution [J]. Phys. Rev. Lett., 2007, 99: 068301.
[39]陈梨俊.以耗散粒子动力学为纽带的多尺度贯通模拟方法[D].长春:吉林大学理论化学研究所,2007.
[40]刘鸿.高分子体系活性聚合反应的计算机模拟研究[D].长春:吉林大学理论化学研究所,2010.
[41]刘英涛.梳型嵌段共聚物聚集态结构的耗散粒子动力学模拟[D].长春:吉林大学理论化学研究所,2010.
[42]王晓琳.表面吸附受限高分子体系结构及动力学性质的计算机模拟研究[D].长春:吉林大学理论化学研究所,2008.
[43] YOU L Y, HE Y D, ZHAO Y, et al. The complex influence of the oscillatory shear on the melt of linear diblock copolymers [J]. J. Chem. Phys., 2008, 129: 204901.
[44] ALLEN M P, TILDESLEY D J. Computer simulation of liquids [M]. Oxford: Clarendon, 1987.
[45] FRENKEL D, SMIT B. Understanding molecular simulation: from algorithms to applications [M]. San Diego: Academic Press, 1996.
[46] RAPPAPORT D C. The art of molecular dynamics simulation [M]. Cambridge: Cambridge Univ Press, 1995.
[47] SARMAN S S, EVANS D J, CUMMINGS P T. Recent developments in non-Newtonian molecular dynamics [J]. Phys. Rep., 1998, 305: 1-92.
[48] FLORY P J. Statistical mechanics of chain molecules [M]. Munich: Hanser, 1989.
[49] LEACH A R. Molecular modeling principles and applications [M]. Essex: Longman, 1996.
[50]杨小震.分子模拟与高分子材料[M].北京:科学出版社,2002.
[51] MSI MANUAL. Forcefield-based simulations general theory & methodology [M]. 1997.
[52] SWOPE W C, ANDERSEN H C, BERENS P H, WILSON K R. A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters [J]. J. Chem. Phys., 1982, 76: 637-649.
[53]刘光恒,戴树珊.化学应用统计力学[M].北京:科学出版社,2001.
[54]李如生.平衡和非平衡统计力学[M].北京:清华大学出版社,1995.
[55] HUR J S, SHAQFEH E S G, LARSON R G. Brownian dynamics simulation of single DNA molecules in shear flow [J]. J. Rheol., 2000, 44: 713- 742.
[56] LIU T W. Flexible polymer chain dynamics and rheological properties in steady flows [J]. J. Chem. Phys., 1989, 90: 5826-5842.
[57] ZYLKA W, ?TTINGER H C. A comparison between simulations and various approximations for Hookean dumbbells with hydrodynamic interaction [J]. J. Chem. Phys., 1989, 90: 474-480.
[58] GRASSIA P, HINCH E J F. Computer simulations of polymer chain relaxation via Brownian motion [J]. J. Fluid Mech., 1996, 308: 255-288.
[59] DOYLE P S, SHAQFEH E S G, GAST A P. Dynamic simulation of freely draining flexible polymers in steady linear flows [J]. J. Fluid Mech., 1997, 334: 251-291.
[60] BINDER K. Monte carlo methods in condensed matter physics [M]. Berlin: Springer-Verlag, 1992.
[61] BINDER K. Applications of the Monte Carlo method in statistical physics [M]. Berlin: Springer-Verlag, 1984.
[62] LIU Y, YANG X Z, YANG M J, et al. Mesoscale simulation on the shape evolution of polymer drop and initial geometry influence [J]. Ploymer, 2004, 45: 6985-6991.
[63] CLARK A T, LAL M, RUDDOCK J N, et al. Mesoscopic simulation of dropsin gravitational and shear fields [J]. Langmuir, 2000, 16: 6342-6350.
[64] JONES J L, LAL M, RUDDOCK J N, et al. Dynamics of a drop at a liquid/solid interface in simple shear fields: a mesoscopic simulation study [J]. Faraday Disc., 1999, 112: 129-142.
[65] ESPA?OL P, SERRANO M, ZUNIGA I. Coarse graining of a fluids and its relation with dissipative particle dynamics and smoothed particle dynamics [J]. Int. J. Mod. Phys. C, 1997, 8: 899- 908.
[66]李泽生,吕中元.计算机模拟方法及其在物理化学中的应用,新世纪的物理化学学科前沿与展望(国家自然科学基金委员会化学科学部组编,梁文平、杨俊林、陈拥军、李灿烂主编)[M].北京:科学出版社,2004.
[67] BOGHOSIAN B M, ALEXANDER F J, COVENEY P V. Guest editors′preface-discrete models of complex fluid dynamics [J]. Int. J. Mod. Phys., 1997, 8: 637- 640.
[68] HOOGERBRUGGE P J, KOELMAN J M V A. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics [J]. Europhys. Lett., 1992, 19: 155-160.
[69] ESPA?OL P, WARREN P B. Statistical mechanics of dissipative particle dynamics [J]. Europhys. Lett., 1995, 30: 191-196.
[70] GROOT R D, WARREN P B. Dissipative particle dynamics: Bridging the gap between atomistic and mesoscopic simulation [J]. J. Chem. Phys., 1997, 107: 4423-4435.
[71] CHEN S, DOOLEN G D. Lattice Boltzmann method for fluid flows [J]. Annu. Rev. Fluid. Mech., 1998, 30: 329-364.
[72] BENZI R, SUCCI S, VERGSSOLA M. The Lattice Boltzmann equation: theory and applications [J]. Phys. Rep., 1992, 222: 145-197.
[73] GONNELLA G, ORLANDINI E, YEOMANS J M. Phase separation in two-dimensional fluids: The role of noise [J]. Phys. Rev. E, 1999, 59: R4741-R4744.
[74] WAGNER A J, YEOMANS J M. Phase separation under shear in two-dimensional binary fluids [J]. Phys. Rev. E, 1999, 59: 4366-4373.
[75] ORLANDINI E, GONNELLA G, YEOMANS J M. Lattice Boltzmann study of spinodal decomposition in structured fluids [J]. Physical A, 1997, 240: 277-285.
[76] GONNELLA G., ORLANDINI E, YEOMANS J M. Spinodal decomposition to a lamellar phase: Effects of hydrodynamic flow [J]. Phys. Rev. Lett., 1997, 78: 1695-1698.
[77] FRISCH U, HASSLACHER B, POMEAU Y. Lattice-gas automata for the Navier-Stokes equations [J]. Phys. Rev. Lett., 1986, 56: 1505-1508.
[78] AHLRICHS P, DüNWEG B. Simulation of a single polymer chain in solution by combining lattice Boltzmann and molecular dynamics [J]. J. Chem. Phys., 1999, 111: 8225-8239.
[79] DENNISTON C, ORLANDINI E, YEOMANS J M. Phase ordering in nematic liquid crystals [J]. Phys. Rev. E, 2001, 64: 021701.
[80] GUSEV A A. Representative volume element size for elastic composites: A numerical study [J]. J. Mech. Phys. Solids., 1997, 45: 1449-1459.
[81] KASHIYAMA K, SAITOH K, BEHR M, TEZDUYAR T. Parallel finite element methods for large scale computation of storm surges and tidal flows [J]. Int. J. Numerical methods in fluids, 1997, 24: 1371-1380.
[82] GEORGE P L. Automatic mesh generation: Application to finite element methods [M]. New York: Wiley, 1991.
[83] KOELMAN J M V A, HOOGERBRUGGE P J. Dynamic simulations of hard-sphere suspensions under steady shear [J]. Europhys. Lett., 1993, 21: 363-368.
[84] KONG Y, MANKE C W, MADDEN W G, et al. Simulation of a confined polymer in solution using the dissipative particle dynamics method [J]. Int. J. Themophys., 1994, 15: 1093-1101.
[85] SCHLIJPER A G, HOOGERBRUGGE P G, MANKE C W. Computer simulation of dilute polymer solutions with the dissipative particle dynamics method [J]. J. Rheol, 1995, 39: 567-579.
[86] DüNWEG B, PAUL W. Brownian dynamics simulations without Gaussian random numbers [J]. Int. J. Mod. Phys. C, 1991, 2: 817-827.
[87] TUCKERMAN M E, MARTYNA G J. Understanding modern molecular dynamics: Techniques and applications [J]. J. Phys. Chem. B, 2000, 104: 159-178.
[88] NOVIK K E, COVENEY P V. Finite-difference methods for simulation models incorporating nonconservative forces [J]. J. Chem. Phys., 1998, 109: 7667-7677.
[89] PAGONABARRAGA I, HAGEN M H J, FRENKEL D. Self-consistent dissipative particle dynamics algorithm [J]. Europhys. Lett., 1998, 42: 377-382.
[90] GIBSON J B, CHEN K, CHYNOWETH S. The equilibrium of a velocity-Verlet type algorithm for DPD with finite time steps [J]. Int. J. Mod. Phys. C, 1999, 10: 241-261.
[91] VATTULAINEN I, KARTTUNEN M, BESOLD G, et al. Integration schemes for dissipative particle dynamics simulations: From softly interacting systems towards hybrid models [J]. J. Chem. Phys., 2002, 116: 3967-3979.
[92] NIKUNEN P, KARTTUNEN M, VATTULAINEN I. How would you integrate the equations of motion in dissipative particle dynamics simulations? [J]. Comp. Phys. Comm., 2003, 153: 407-423.
[93] LOWE C P. An alternative approach to dissipative particle dynamics [J]. Europhys. Lett., 1999, 47: 145-151.
[94] ANDERSEN H C. Molecular dynamics simulations at constant pressure and/or temperature [J]. J. Chem. Phys., 1980, 72: 2384-2393.
[95] DüNWEG B. Advanced Simulations for Hydrodynamic Problems: LatticeBoltzmann and Dissipative Particle Dynamics[R], In Attig N, Binder K, Grubmüller H, K. Kremer, editors, Computational Soft Matter: From Synthetic Polymers to Proteins, Lecture Notes[I]. Jülich, John von Neumann Institute for Computing, NIC Series, 2004, Vol. 23, ISBN 3-00-012641-4: pp. 61-82.
[96] SCHNEIDER T, STOLL E. Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions [J]. Phys. Rev. B, 1978, 17: 1302-1322.
[97] CHANDRASEKHAR S. Stochastic problems in physics and astronomy [J]. Rev. Mod. Phys., 1943, 15: 1-89.
[98] RISKEN H. The Fokker-Planck equation [M]. Berlin: Springer-Verlag, 1984.
[99] DüNWEG B. Langevin methods, In Dünweg B, Landau D P, Milchev A I, editors. Computer simulation of surfaces and interfaces [M]. Dordrecht: Kluwer, 2003.
[100] GARDINER C W. Handbook of stochastic methods [M]. Berlin: Springer-Verlag, 1983.
[101] GROOT R D, RABONE K L. Mesoscopic simulation of cell membrane damage, morphology change and rupture by nonionic surfactants [J]. Biophysical Journal, 81: 725-736.
[102] SUN H. Ab initio characterizations of molecular structures, conformation energies, and hydrogen-bonding properties for polyurethane hard segments [J]. Macromolecules, 1994, 26: 5924-5936.
[103] ANTONIETTI M, TAUER K. 90 years of polymer latexes and heterophase polymerization: More vital than ever [J]. Macromol. Chem. Phys., 2003, 204: 207-219.
[104] HERNANDEZ H F, TAUER K. Radical desorption kinetics in emulsion polymerization. 1. theory and simulation [J]. Ind. Eng. Chem. Res., 2008, 47: 9795-9811.
[105] ARZAMENDI G, LEIZA J R. Molecular weight distribution (soluble and insoluble fraction) in emulsion polymerization of acrylate monomers by Monte Carlo simulations [J]. Ind. Eng. Chem. Res., 2008, 47: 5934-5947.
[106] TOBITA H. RAFT miniemulsion polymerization kinetics, 2-molecular weight distribution [J]. Macromol. Theory Simul., 2009, 18: 120-126.
[107] VENTUROLI, M, SMIT B. Simulating the self-assembly of model membranes [J]. Phys. Chem. Comm., 1999, 10: 1-5.
[108] KRANENBURG M, VENTUROLI M, SMIT B. Phase behavior and induced interdigitation in bilayers studied with dissipative particle dynamics [J]. J. Phys. Chem. B, 2003, 107: 11491-11501.
[109] LARADJI M, SUNIL KUMAR P B. Dynamics of domain growth in self-assembled fluid vesicles [J]. Phys. Rev. Lett., 2004, 93: 198105.
[110] SHILLCOCK J C, LIPOWSKY R. Equilibrium structure and lateral stress distribution of amphiphilic bilayers from dissipative particle dynamics simulations [J]. J. Chem. Phys., 2002, 117: 5048-5061.
[111] SHILLCOCK J C, LIPOWSKY R. Tension-induced fusion of bilayer membranes and vesicles [J]. Nat. Mater., 2005, 4: 225-228.
[112] YAMAMOTO S, MARUYAMA Y, HYODO S. Dissipative particle dynamics study of spontaneous vesicle formation of amphiphilic molecules [J]. J. Chem., 2002, 116: 5842-5849.
[113] LIU Y T, ZHAO Y, LIU H, LIU Y H, LU Z Y. Spontaneous fusion between the vesicles formed by A (2n) (B-2) (n) type comb-like block copolymers with a semiflexible hydrophobic backbone [J]. J. Phys. Chem. B, 2009, 113: 15256-15262.
[114] ORTIZ V, NIELSEN S O, DISCHER D E, KLEIN M L, LIPOWSKY R, SHILLCOCK J. Dissipative particle dynamics simulations of polymersomes [J]. J. Phys. Chem. B, 2005, 109: 17708-17714.
[115] ZHONG C L, LIU D H. Understanding multicompartment micelles usingdissipative particle dynamics simulation [J]. Macromolecular Theory, 2007, 16: 141-157.
[116] REKVIG L, HAFSKJOLD B, SMIT B. Molecular simulations of surface forces and film rupture in oil/water/surfactant systems [J]. Langmuir, 2004, 20: 11583-11593.
[117] JURY S, BLADON P, CATES M, KRISHNA S, HAGEN M, RUDDOCK N, WARREN P. Simulation of amphiphilic mesophases using dissipative particle dynamics [J]. Phys. Chem. Chem. Phys., 1999, 1: 2051-2056.
[118] SCHULZ S G, KUHN H, SCHMID G, MUND C, VENZMER J. Phase behavior of amphiphilic polymers: A dissipative particles dynamics study [J]. Colloid Polym. Sci., 2004, 283: 284-290.
[119] MALFREYT P, TILDESLEY D J. Dissipative particle dynamics simulations of grafted polymer chains between two walls [J]. Langmuir, 2000, 16: 4732-4740.
[120] CLARK A T, LAL M, RUDDOCK J N, WARREN P B. Mesoscopic simulation of drops in gravitational and shear fields [J]. Langmuir, 2000, 16: 6342-6350.
[121] LIU H, QIAN H J, ZHAO Y, LU Z Y. Dissipative particle dynamics simulation study on the binary mixture phase separation coupled with polymerization [J]. J. Chem. Phys., 2007, 127:144903.
[122] LIU H, LI M, LU Z Y, ZHANG Z G, SUN C C. Influence of surface-initiated polymerization rate and initiator density on the properties of polymer brushes [J]. Macromolecules, 2009, 42: 2863-2872.
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