Many applications of responsive microgels rely on the fast adaptation of the polymer network. However, the underlying dynamics of the de-/swelling process of the gels have not been fully understood. In the present work, we focus on the collapse kinetics of poly-N-isopropylacrylamide (pNIPAM) microgels due to cononsolvency. Cononsolvency means that either of the pure solvents, e.g., pure water or pure methanol, act as a so-called good solvent, leading to a swollen state of the polymer network. However, in mixtures of water and methanol, the previously swollen network undergoes a drastic volume loss. To further elucidate the cononsolvency transition, pNIPAM microgels with diameters between 20 and 110 μm were synthesized by microfluidics. To follow the dynamics, pure water was suddenly exchanged with an unfavorable mixture of 20 mol% methanol (solvent-jump) within a microfluidic channel. The dynamic response of the microgels was investigated by optical and fluorescence microscopy and Raman microspectroscopy. The experimental data provide unique and detailed insight into the size-dependent kinetics of the volume phase transition due to cononsolvency. The change in the microgel’s diameter over time points to a two-step process of the microgel collapse with a biexponential behavior. Furthermore, the dependence between the two time constants from this biexponential behavior and the microgel’s diameter in the collapsed state deviates from the square-power law proposed by Tanaka and Fillmore [J. Chem. Phys.19797012141218]. The deviation is discussed considering the adhesion-induced deformation of the gels and the physical processes underlying the collapse.

From Raman, we determined an enrichment of methanol inside the polymer in the cononsolvency-induced collapse and donor-type hydrogen-bonding of methanol with pNIPAM.

To increase the process productivity and product quality of bioprocesses, the in‐line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in‐situ and non‐invasively. However, evaluating bioprocess Raman spectra with a robust state‐of‐the‐art statistical model requires effortful model calibration. In the present study, we in‐line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics‐based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.

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In bioprocesses, the pH value is a critical process parameter that requires monitoring and control. For pH monitoring, potentiometric methods such as pH electrodes are state-of-the-art. Nevertheless, they are invasive and show measurement value drift. Spectroscopic pH monitoring is a non-invasive alternative to potentiometric methods avoiding this measurement value drift. In this study, we developed the Good pH probe, which is an approach for spectroscopic pH monitoring in bioprocesses with an effective working range between pH 6 and pH 8 that does not require the estimation of activity coefficients. The Good pH probe combines the Good buffer 3-( N-morpholino)propanesulfonic acid (MOPS) as pH indicator with Raman spectroscopy as spectroscopic technique, and Indirect Hard Modeling (IHM) for the spectral evaluation. During a detailed characterization, we proved that the Good pH probe is reversible, exhibits no temperature dependence between 15 and 40 °C, has low sensitivity to the ionic strength up to 1100 mM, and is applicable in more complex systems, in which other components significantly superimpose the spectral features of MOPS. Finally, the Good pH probe was successfully used for non-invasive pH in-line monitoring during an industrially relevant enzyme-catalyzed reaction with a root mean square error of prediction (RMSEP) of 0.04 pH levels.