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.
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.
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