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Monitoring chemical processes with optical spectroscopy involves a trade-off between high-frequency sampling to capture process dynamics and good signal-to-noise ratio (SNR). This contribution presents a strategy to maximize sampling frequency while maintaining a high SNR during in-line process monitoring. To achieve this, a novel method to determine the analyte-specific SNR from a single multicomponent spectrum with overlapping peaks based on multivariate Indirect Hard Modeling (IHM) regression is presented. The method is extended to optimize the Raman acquisition time dynamically based on the current SNR. After each measurement, the acquisition time is adjusted to reach the target SNR of a component of interest. The method maximizes the number of individual measurements while sustaining the target SNR despite highly variable signal intensity. The method is applied to monitor polymerization reactions showing that it is suitable for reliably monitoring high-rate reactions of multiple spectrally-similar components. [ABSTRACT FROM AUTHOR]

Poly( N -isopropylacrylamide) microgels have found various uses in fundamental polymer and colloid science as well as in different applications. They are conveniently prepared by precipitation polymerization. In this reaction, radical polymerization and colloidal stabilization interact with each other to produce well-defined thermosensitive particles of narrow size distribution. However, the underlying mechanism of precipitation polymerization has not been fully understood. In particular, the crucial early stages of microgel formation have been poorly investigated so far. In this contribution, we have used small-angle neutron scattering in conjunction with a stopped-flow device to monitor the particle growth during precipitation polymerization in situ. The average particle volume growth is found to follow pseudo-first order kinetics, indicating that the polymerization rate is determined by the availability of the unreacted monomer, as the initiator concentration does not change considerably during the reaction. This is confirmed by calorimetric investigation of the polymerization process. Peroxide initiator-induced self-crosslinking of N -isopropylacrylamide and the use of the bifunctional crosslinker N , N '-methylenebisacrylamide are shown to decrease the particle number density in the batch. The results of the in situ small-angle neutron scattering measurements indicate that the particles form at an early stage in the reaction and their number density remains approximately the same thereafter. The overall reaction rate is found to be sensitive to monomer and initiator concentration in accordance with a radical solution polymerization mechanism, supporting the results from our earlier studies., Competing Interests: The authors declare no competing financial interest.

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., (© The Author(s) 2016.)