A computational and modeling approach is used to highlight the key factors that affect polymer nanoparticles (NP) formation in flash nano-precipitation (FNP), when the good solvent, e.g., acetone, is replaced by acetonitrile, tetrahydrofuran and tert-butanol. A population balance model is coupled with computational fluid dynamics to study the kinetics effects on FNP. The mean NP size is predicted in terms of mean radius of gyration via the Flory law of real polymers. The effect of different good solvents is modeled in terms of solute–solvent interactions, using the Flory–Huggins theory and Hansen solubility parameters. Promising results show how the proposed methodology is able to investigate the role played by different good solvents, analyzing single factors at the time. A deep insight into both the dynamics of mixing and the dynamics of aggregation is therefore reached and the main mechanisms involved are pointed out, showing a good agreement with experimental data.

Effect of different good solvents in flash nano-precipitation via multi-scale population balance modeling-CFD coupling approach / Lavino, Alessio D.; Ferrari, Marco; Barresi, Antonello A.; Marchisio, Daniele. - In: CHEMICAL ENGINEERING SCIENCE. - ISSN 0009-2509. - STAMPA. - 245:(2021), p. 116833. [10.1016/j.ces.2021.116833]

Effect of different good solvents in flash nano-precipitation via multi-scale population balance modeling-CFD coupling approach

Lavino, Alessio D.;Ferrari, Marco;Barresi, Antonello A.;Marchisio, Daniele
2021

Abstract

A computational and modeling approach is used to highlight the key factors that affect polymer nanoparticles (NP) formation in flash nano-precipitation (FNP), when the good solvent, e.g., acetone, is replaced by acetonitrile, tetrahydrofuran and tert-butanol. A population balance model is coupled with computational fluid dynamics to study the kinetics effects on FNP. The mean NP size is predicted in terms of mean radius of gyration via the Flory law of real polymers. The effect of different good solvents is modeled in terms of solute–solvent interactions, using the Flory–Huggins theory and Hansen solubility parameters. Promising results show how the proposed methodology is able to investigate the role played by different good solvents, analyzing single factors at the time. A deep insight into both the dynamics of mixing and the dynamics of aggregation is therefore reached and the main mechanisms involved are pointed out, showing a good agreement with experimental data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2910552