The Impact of the Feed Rate and the Binder Concentration on the Morphology of Spray-Dried Alumina–Polymer Nanocomposites
Year: 2025
Type: article
Abstract: Spray-drying is a flexible method for creating fine porous composites with controlled size and morphology. This study investigates how the morphology and porosity of the spray-dried powder of nano-alumina and polyvinylpyrrolidone (PVP-30) granules are affected by both the feed rate and the binder concentration. Droplet size and velocity distributions, measured with a HiWatch system, showed that higher feed rates produce larger droplets with faster velocities, therefore affecting the final morphology of the dried product. The morphology of the dried granules was analyzed using inline SOPAT imaging. While mercury intrusion porosimetry quantified the nano-pore volume and nano-pore size of the granules, offline scanning electron microscopy (SEM) was also used to characterize the morphology of the dried product. The findings show that, while raising the binder concentration produces a more compact morphology with a lower nano-pore volume, higher feed rates produce larger granules with a larger nano-pore volume. This study offers fundamental insights that can support the future development of control strategies for optimizing the production of spray-dried porous alumina–polymer nanocomposites by means of knowledge about the relationship between these process parameters and product qualities.
Source: Processes
DOI: 10.3390/pr13061643
Authors: Rahul Mitra, Lukas Fuchs, Orkun Furat, Yannik Sinnwell, Sergiy Antonyuk, Volker Schmidt, Urs Alexander Peuker
Language: English
Subfield: Food Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: Autonome und selbstadaptierende, hochauflösende 3D additive Fertigung mittels hochenergetischer Partikelstöße, Modellbasierte Regelung der Sprühtrocknung von strukturierten Granulaten mit spezifizierten Eigenschaften durch Transferfunktionen, hergeleitet mittels multivariater stochastischer Modelle und maschineller Lernverfahren
OpenAlex ID: W4410628644
