Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam, XH 1098, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands.
BASF SE, Carl-Bosch-Strasse 38, Ludwigshafen am Rhein 67056, Germany.
J Chromatogr A. 2022 Aug 30;1679:463386. doi: 10.1016/j.chroma.2022.463386. Epub 2022 Jul 28.
Synthetic polymers typically show dispersity in molecular weight and potentially in chemical composition. For the analysis of the chemical-composition distribution (CCD) gradient liquid chromatography may be used. The CCD obtained using this method is often convoluted with an underlying molecular-weight distribution (MWD). In this paper we demonstrate that the influence of the MWD can be reduced using very steep gradients and that such gradients are best realized utilizing recycling gradient liquid chromatography (LC↻LC). This method allows for a more-accurate determination of the CCD and the assessment of (approximate) critical conditions (if these exist), even when high-molecular-weight standards of narrow dispersity are not readily available. The performance and usefulness of the approach is demonstrated for several polystyrene standards, and for the separation of statistical copolymers consisting of styrene/methyl methacrylate and methyl methacrylate/butyl methacrylate. For the latter case, approximate critical compositions of the copolymers were calculated from the critical compositions of two homopolymers and one copolymer of known chemical composition, allowing for a determination of the CCD of unknown samples. Using this approach it is shown that the copolymers elute significantly closer to the predicted critical compositions after recycling of the gradient. This is most clear for the lowest-molecular-weight copolymer (M = 4.2 kDa), for which the difference between measured and predicted elution composition decreases from 7.9% without recycling to 1.4% after recycling.
合成聚合物通常在分子量和潜在的化学组成上表现出分散性。对于化学组成分布(CCD)的分析,可以使用梯度液相色谱法。使用这种方法获得的 CCD 通常与潜在的分子量分布(MWD)卷积。在本文中,我们证明了可以使用非常陡峭的梯度来降低 MWD 的影响,并且这种梯度最好通过循环梯度液相色谱(LC↻LC)来实现。该方法允许更准确地确定 CCD,并评估(近似)临界条件(如果存在),即使没有容易获得的具有窄分散性的高分子量标准品。该方法在几种聚苯乙烯标准品的分离以及苯乙烯/甲基丙烯酸甲酯和甲基丙烯酸甲酯/正丁基甲基丙烯酸酯的统计共聚物的分离中得到了验证。对于后一种情况,从两个均聚物和一个已知化学组成的共聚物的临界组成计算出共聚物的近似临界组成,从而可以确定未知样品的 CCD。使用这种方法可以表明,在梯度循环后,共聚物的洗脱更接近预测的临界组成。对于分子量最低的共聚物(M = 4.2 kDa)最为明显,在没有循环的情况下,测量和预测的洗脱组成之间的差异从 7.9%减小到循环后的 1.4%。