Elucidating the influence of freezing stochasticity on drying kinetics in lyophilization through experiments and modeling
Year: 2025
Type: article
Abstract: Freeze-drying, a common technique in the food and pharmaceutical industries, is a complex process, primarily due to the occurrence of two phase changes: the ice crystals formed during the freezing stage lead to a porous network that significantly influences the sublimation kinetics during drying. In this study, the effect of the freezing conditions, their variability among vials due to the stochastic nature of ice nucleation, and their impact on drying kinetics are investigated through a combination of theory on flow through porous media and freeze-drying experiments. A recently developed infrared thermography setup is used to monitor product temperature and is combined with gravimetric analysis to accurately assess the sublimation rates for each vial in the whole batch (typically 60 vials per batch). Experiments with sucrose-mannitol formulation reveal a clear correlation between the freezing conditions, characterized by nucleation time, nucleation temperature, and solidification time of the vials, and their drying kinetics. Specifically, vials that nucleate earlier exhibit slower solidification and significantly faster drying. In contrast, late-nucleating vials nucleate at lower temperatures, solidify faster, and exhibit slower drying. To further investigate the role of the porous network in drying, a control experiment with pure water was performed, where no correlation between freezing conditions and sublimation rates was observed due to the absence of a porous structure that governs the drying performance. Overall, the results improve the understanding of how freezing conditions affect drying kinetics and offer an accurate, reproducible, and statistically reliable gravimetric method for quantifying the effect of freezing conditions on drying kinetics.
Institutions: ETH Zurich, Institute of Energy and Process Engineering
Language: English
Topic: Food Drying and Modeling
Subfield: Food Science
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,620
OpenAlex ID: W4414540237