A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation

Abstract

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.

Neat Linear Polysiloxane-Based Ionic Polymers: Insights into Structure-Based Property Modifications and Applications

“…The X-ray scattering data in Figure 4C show three unique peaks for this ionomer that can be attributed to the structural features in Figure 4B: the polysiloxane backbone (peak II), the oligo-ethylene oxide connecting groups that act as a plasticizer (peaks I and II), and the small quantity of phosphonium halide pendant groups that cluster (peak III). An interesting FT-IR method for following the progress of polysiloxane syntheses from non-ionic monomers has been reported recently [57,58]. It should be adaptable easily to follow the syntheses of ionic polymers reported here.…”

The Influence of Zinc Waste Filler on the Tribological and Mechanical Properties of Silicone-Based Composites

“…Choosing the right filler can increase the mechanical strength, can reduce abrasion, or can protect against the growth of organisms on the surface. Precise process control is of great importance to ensure reproducible products and to control the properties of materials [7]. In turn, improving mechanical properties enables the use of such materials in modern, innovative, and unusual solutions [8][9][10].…”

Prediction of Residual Curing Capacity of Melamine-Formaldehyde Resins at an Early Stage of Synthesis by In-Line FTIR Spectroscopy

“…The conception of a low-cost process analyzer, as an alternative to an expensive MIR spectrometer, requires a model of comparable quality with reduced input data (which is based on only a comparatively small number of wavenumbers (x-variables)) [ 37 ].…”

“…Earlier stages of synthesis, however, seem to be not suitable since at before the point of clarification not yet all chemicals are present in solution and therefore, cannot be detected by FTIR. The conception of a low-cost process analyzer, as an alternative to an expensive MIR spectrometer, requires a model of comparable quality with reduced input data (which is based on only a comparatively small number of wavenumbers (x-variables)) [37].…”