Molecular weight prediction in polystyrene blends. Unprecedented use of a genetic algorithm in pulse field gradient spin echo (PGSE) NMR,Soft Matter

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Molecular weight prediction in polystyrene blends. Unprecedented use of a genetic algorithm in pulse field gradient spin echo (PGSE) NMR
Soft Matter ( IF 2.9 ) Pub Date : 2017-09-18 00:00:00 , DOI: 10.1039/c7sm01569k
Francisco M. Arrabal-Campos _{1,

2,

3,

4,

5} , José D. Álvarez _{3,

4,

5,

6,

7} , Amador García-Sancho _{8,

9,

10,

11,

12} , Ignacio Fernández _{1,

2,

3,

4,

5}

Affiliation

A genetic algorithm that uses boxcar functions (diffGA) has been applied for the first time in PGSE NMR. It reconstructs accurate diffusion coefficients for all the components of the mixture, and therefore predicts correct weight-average molecular weights for all of them. The results reported herein complement those obtained with established methods such as ITAMeD, CONTIN and TRAIn algorithms, and provide a detailed solution picture. Its robustness and limits have been stretched in order to ascertain the minimum separation within diffusion coefficients or relative proportion between components. In addition, the new genetic algorithm has been also applied to a mixture of small molecules, providing excellent results at very low computational times.

中文翻译：

聚苯乙烯共混物中的分子量预测。在脉冲场梯度自旋回波（PGSE）NMR中前所未有地使用遗传算法

在PGSE NMR中首次应用了使用boxcar函数（diffGA）的遗传算法。它可以为混合物的所有组分重建准确的扩散系数，因此可以预测所有组分的正确重均分子量。本文报告的结果补充了通过已建立方法（例如ITAMeD，CONTIN和TRAIn算法）获得的结果，并提供了详细的解决方案图。为了确定扩散系数内的最小间距或组件之间的相对比例，已对其鲁棒性和极限进行了扩展。此外，新的遗传算法也已应用于小分子的混合物，在极短的计算时间内即可提供出色的结果。

更新日期：2017-09-18

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