St/MMA/(mono-BM)三元无皂乳液共聚动力学

在PVA助溶稳定剂的作用下,通过间歇法无皂种子乳液聚合制备了St/MMA/(mono-BM)三元共聚物。研究了单体总浓度[M],引发剂浓度[I],PVA浓度[P],聚合温度对St/MAA/(mono-BM)无皂种子乳液聚合动力学的影响。实验表明:Rp∝[M]2.38,-M w∝[M]0.62;Rp∝[I]0.883,-M w∝[I]-0.71。采用粒度测定仪,凝胶色谱和示差扫描量热法表征了共聚物乳液。粒度分析显示乳液总平均粒径为55 nm;DSC谱图显示三元共聚物只有单个Tg,聚合体系不存在微相分离区;GPC谱表明共聚物分子质量分布仅为1.28,接近单分散体系。     

PS-b-PEA嵌段共聚物的合成与表征

采用“活性”自由基聚合的方法合成了不同分子量的苯乙烯和甲基丙烯酸乙酯[polystyrene-block-poly(ethyl methacrylate),PS-b-PEA]嵌段共聚物。并用凝胶渗透色谱(GPC)和红外光谱(FTIR)对所合成的共聚物进行了表征,实验结果显示:在4-羟基-2,2,6,6-四甲基哌啶-1-氧化物自由基(HTEMPO.)和偶氮二异丁腈(AIBN)存在下,苯乙烯聚合反应所得到的聚苯乙烯分子量分布在1.18~1.2范围,分子量随聚合时间的延长而增大(7 200~69 300 g/mol);将该聚苯乙烯溶于甲基丙烯酸乙酯,在(130±2)℃时可以重新引发甲基丙烯酸乙酯的聚合反应,且甲基丙烯酸乙酯的聚合反应具有“活性”聚合的特征,共聚物的数均分子量及分布分别在57 800~107 800 g/mol和1.22~1.26范围,共聚物由FTIR表征显示:在聚苯乙烯接上聚甲基丙烯酸乙酯后,在1 158 cm-1及1 727 cm-1出现其甲基丙烯酸乙酯的特征吸收峰,说明共聚物为嵌段共聚物。     

高顺式丁二烯-异戊二烯共聚橡胶研究进展

综合评述了国内外高顺式丁二烯-异戊二烯共聚橡胶的研究进展，包括合成方法、结构与性能及其应用；并对其发展趋势进行了评述。     

共聚改性浇铸尼龙的力学性能研究

在绝热条件下通过活化阴离子共聚制备十二内酰胺改性浇铸尼龙，对其力学性能进行了研究。结果显示：改性浇铸尼龙材料拉伸屈服后能够产生很大的塑性形变，不会发生脆性破坏。与未改性浇铸尼龙相比，改性浇铸尼龙缺口冲击强度随十二内酰胺含量的增加而增加；其韧性脆性断裂转变能够有效地向低温方向位移，说明改性浇铸尼龙材料即使在较低温度下仍保持了内在韧性。     

骨胶与表氯醇接枝共聚物的合成及应用性能

研究骨胶与表氯醇的接枝共聚反应。接枝共聚产物在1402cm-1(C—O—C伸缩振动)处的红外光谱验证了接枝共聚反应的发生。探讨了水胶比、碱用量、碱解温度、反应时间等因素对接枝共聚产物的影响。当水胶比为1:1,NaOH用量为胶液量的3%,碱解温度为60℃,表氯醇用量为胶液量的1%,接枝共聚温度为50℃时,合成胶粘剂的初粘力为100%,凝固点为-5℃,与工业骨胶相比,剪切强度大,其应用范围更广,应用性能得到较大的改善。     

共聚改性MC尼龙的制备和DSC分析

以氢氧化钠为催化剂,N 乙酰基己内酰胺为活化剂,催化剂和活化剂物质的量与反应单体物质的量之比均为1∶400,聚合起始温度为130℃,在绝热条件下,通过活化阴离子共聚制得了增韧MC尼龙共聚体。通过示差扫描量热分析测定共聚物熔点和结晶相含量,吸热熔融峰表明改性MC尼龙是无规共聚物,共聚导致了制品结晶度下降,结晶的特点是熔点的宽分布、不规则生长以及高浓度无定形区域对结晶存在干扰。     

氟烯烃共聚合的研究进展

概述了氟烯烃共聚物树脂的发展,总结了氟烯烃与其他单体共聚合的相关研究。大多数情况下,氟烯烃单体的共聚反应得到的是随机共聚物。虽然聚合反应涉及的含氟单体和共聚单体种类很多,但最终聚合物按其性能和应用主要包括热塑性体、弹性体和热塑性弹性体3类。通过筛选一些特定功能性的单体参与聚合反应,可以使得含氟聚合物的某些性能如溶解性、交联固化性、附着力等得到补偿。此外,文中列出了一些氟烯烃单体的Q-e参数,并从理论上讨论了共聚物的结构;简述了吸电子氟烯烃单体与供电子全质子单体间的交替共聚行为,并列举了具体实例。     

Isothermal crystallization of metallocene‐based propylene/α‐olefin copolymers

Isothermal crystallization and subsequent melting behavior of two propylene/hexene‐1 copolymers and two propylene/octene‐1 copolymers prepared with metallocene catalyst were investigated. It is found that γ‐modification is predominant in all copolymers. The Avrami exponent shows a weak dependency on comonomer content and comonomer type. At higher crystallization temperatures (T_c) the crystallization rate constant changes more rapidly with T_c and the crystallization half‐time substantially increases. Double melting peaks were also observed at high T_c, which is attributed to the inhomogeneous distribution of comonomer units along the polymer chains and the existence of crystals with different lamellar thicknesses. The equilibrium melting temperatures (T) of the copolymers were obtained by Hoffman–Weeks extrapolation. It was found that the T decreases with increasing comonomer content, but are independent of comonomer type, implying that comonomer units are excluded from the crystal lattice. Dilation of the crystal lattice was also observed, which depends on crystallization, comonomer content, and comonomer type. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 240–247, 2005     

聚合物纳米复合材料的研究进展

介绍了聚合物基纳米复合材料的种类及制备方法，详细介绍了聚合物／层状硅酸盐纳米复合材料的研究，包括聚合物／层状硅酸盐纳米复合材料的种类、性能及研究的新进展。     

On-line multi-variable predictive control of molar mass and particle size distributions in free-radical emulsion copolymerization

A multi-variable model predictive control (MPC) was formulated to solve control problems associated with a combination of regulation and targeting desired set-points. We investigated the simultaneous control of key polymer properties: the particle size (PSD) and molecular weight distribution (MWD) by manipulating the flow rates of the monomers (styrene, MMA), surfactant, initiator and the temperature of the reactor. A multi-input-multi-output (MIMO) formulation was constructed for the constrained optimal control problem to maximize the width of the PSD (with M_n at a constant set-point), and to maximize the average molar mass. The strategy developed within a gPROMS-API-DCS environment allowed real-time implementation of model-based control of the process. The optimal control problem was implemented via an interface to a dynamic optimization code. Major improvements in process operation and polymer property control resulted on the implementation of our multi-variable MPC algorithm. The manipulation of the four flow rates and the temperature increased the degree of freedom in the system and achieved tighter PSD and MWD control. The on-line performance of MPC for MWD and PSD control was found to be satisfactory.