Ruckebusch C, Vilmin F, Coste N, Huvenne J P
Laboratoire de Spectrochimie Infrarouge et Raman, UMR CNRS 8516, Université des Sciences et Technologies de Lille, bât C5, 59655 Villeneuve d'Ascq, France.
Appl Spectrosc. 2008 Jul;62(7):791-7. doi: 10.1366/000370208784909562.
We evaluate the contribution made by multivariate curve resolution-alternating least squares (MCR-ALS) for resolving gel permeation chromatography-Fourier transform infrared (GPC-FT-IR) data collected on butadiene rubber (BR) and styrene butadiene rubber (SBR) blends in order to access in-depth knowledge of polymers along the molecular weight distribution (MWD). In the BR-SBR case, individual polymers differ in chemical composition but share almost the same MWD. Principal component analysis (PCA) gives a general overview of the data structure and attests to the feasibility of modeling blends as a binary system. MCR-ALS is then performed. It allows resolving the chromatographic coelution and validates the chosen methodology. For SBR-SBR blends, the problem is more challenging since the individual elastomers present the same chemical composition. Rank deficiency is detected from the PCA data structure analysis. MCR-ALS is thus performed on column-wise augmented matrices. It brings very useful insight into the composition of the analyzed blends. In particular, a weak change in the composition of individual SBR in the MWD's lowest mass region is revealed.
我们评估了多元曲线分辨-交替最小二乘法（MCR-ALS）在解析丁二烯橡胶（BR）和丁苯橡胶（SBR）共混物的凝胶渗透色谱-傅里叶变换红外光谱（GPC-FT-IR）数据方面所做的贡献，以便深入了解聚合物沿分子量分布（MWD）的情况。在BR-SBR共混物的情况下，各聚合物的化学组成不同，但分子量分布几乎相同。主成分分析（PCA）给出了数据结构的总体概况，并证明了将共混物建模为二元体系的可行性。然后进行MCR-ALS。它能够解析色谱共洗脱现象，并验证所选方法的有效性。对于SBR-SBR共混物，问题更具挑战性，因为各弹性体的化学组成相同。从PCA数据结构分析中检测到秩亏缺。因此，对按列增强的矩阵进行MCR-ALS。它为所分析共混物的组成提供了非常有用的见解。特别是，揭示了在MWD最低质量区域中各SBR组成的微弱变化。
