Steinbach Julia C, Schneider Markus, Hauler Otto, Lorenz Günter, Rebner Karsten, Kandelbauer Andreas
School of Applied Chemistry, Reutlingen University, 72762 Reutlingen, Germany.
Reutlingen Research Institute, 72762 Reutlingen, Germany.
Polymers (Basel). 2020 Oct 25;12(11):2473. doi: 10.3390/polym12112473.
The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real-time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. "Bad" (or abnormal) batches can quickly be distinguished from "normal" ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.
由单体原料化学合成聚硅氧烷涉及一系列水解、缩合和改性反应，反应混合物包含复杂的单体和低聚物。对合成过程进行实时监测和精确的过程控制对于确保可重复的中间体和产品非常重要，并且可以通过光谱学轻松实现。然而，在涉及快速且同时发生的官能团转化和复杂反应混合物的化学反应中，由于谱带重叠、峰位移动和基线变化，光谱信号往往模糊不清。因此，对单个吸光度信号进行单变量分析通常用处有限。相比之下，基于对反应光谱导出的主成分（PC）时间进程进行多变量分析的批次建模，为实时监测提供了更有效的工具。在批次建模中，不仅使用单个吸光度谱带，还从不断演变的光谱指纹中提取宽波长范围内的信息用于分析。由此，过程控制可以基于合成过程中发生的众多化学和形态变化。通过实时比较各自的反应轨迹，可以快速将“不良”（或异常）批次与“正常”批次区分开来。在这项工作中，傅里叶变换红外光谱（FTIR）与多变量数据分析相结合，用于聚硅氧烷形成过程的在线表征和批次建模。在不同的起始条件下使用各种反应物浓度进行合成。使用化学计量学（主成分分析，PCA）评估复杂的光谱信息。将反应不同阶段的特定光谱特征与相应的反应步骤相关联。基于使用宽波长范围的批次建模得出反应轨迹。随后，再次将复杂性简化为最相关的吸光度信号，以便得出一种低成本过程光谱设置的概念，该设置可用于实时过程监测和反应控制。
