This contribution presents in-line monitoring of microgel synthesis by precipitation polymerization based on Raman spectroscopy. The spectra are evaluated via multivariate Indirect Hard Modeling (IHM) regression. Therefore, mechanistic models of the pure component spectra for solvent, monomer, and microgel are created by a sum of adaptable parameterized peak functions (Gaussian-Lorentzian). Instead of individual calibrations for each analyte, one comprehensive model is calibrated to predict both the monomer and microgel fraction while ensuring a consistent mass balance. As a novelty, this leads to an in-line microgel quantification based on an interactive spectral model. The results show cross-validation errors (RMSECV) of monomer and microgel fractions as low as 0.028 wt % and 0.084 wt %, respectively. The ability of IHM to account for non-linear spectral changes was found to reduce the microgel RMSECV by a factor of two compared to linear CLS regression. The calibration model allows simultaneous observation of the decrease in monomer content and the formation of microgels. Long as well as short focus immersion optics reveal characteristic vibrations of the turbid microgel suspension, although long focus optics are influenced by scattering particles to a greater extent. Precise examination of the model proves that the prediction is robust against changes in microgel particle size or temperature, which opens up the application of Raman spectroscopy as a comprehensive process analytical technology in microgel synthesis.
Keywords: In-line monitoring; Indirect Hard Modeling; Microgel; Polymerization; Raman spectroscopy.
© The Author(s) 2016.