Fitting molecular weight distributions using a log-normal distribution model
Fitting molecular weight distributions using a log-normal distribution model
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DOI：
10.1016/j.eurpolymj.2015.01.009
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发表时间：
2015-04
影响因子：
6
通讯作者：
M. Monteiro
中科院分区：
文献类型：
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作者：
M. Monteiro
“活的”自由基聚合为生产复杂的聚合物结构开辟了道路，包括环状聚合物、树突状聚合物、星形聚合物和许多其他结构。聚合物科学领域的一个主要问题是这些聚合物产品的表征，特别是当制成高分子量时，远远超出NMR或MALDI的准确性。由于副产物的数量（例如通过终止或循环纯度的死聚合物）而产生纯度问题。在这里，我们使用对数正态分布模型来拟合所有产品的分布与总体实验分布。所有病例的吻合度都非常好。对数正态分布模型实现简单，只依赖于双参数拟合，其中标准差和中位数这两个参数是由分子质量分布的数平均分子量和多分散性获得的。这种类型的分析可以在阐明副产品分布方面发挥作用，从而导致新的或修改的机制见解。
‘Living’ radical polymerization has opened the way to producing complex polymer architectures, including cyclic polymers, dendrimers, stars and many other structures. A major issue in the field of polymer science is the characterization of these polymer products, especially when made to high molecular weights, well beyond the accuracy of NMR or MALDI. Questions of purity arise due to the amount of side products (e.g. dead polymer through termination or cyclic purity). Here, we use the log-normal distribution model to fit distributions of all products to the overall experimental distribution. The fits in all cases were excellent. The log-normal distribution model was simple to implement as it only relies on a two-parameter fit, in which these two parameters, the standard deviation and median, were obtained from the number-average molecular weight and the polydispersity of the molecular weight distribution. This type of analysis can play a role in elucidating the side product distributions, leading to new or modified mechanistic insights.