One Step Closer to Coatings Applications Utilizing Self-Stratification: Effect of Rheology Modifiers.
ACS Appl Polym Mater
; 5(8): 6672-6684, 2023 Aug 11.
Article
em En
| MEDLINE
| ID: mdl-37588086
Self-stratification of model blends of colloidal spheres has recently been demonstrated as a method to form multifunctional coatings in a single pass. However, practical coating formulations are complex fluids with upward of 15 components. Here, we investigate the influence of three different rheology modifiers (RMs) on the stratification of a 10 wt % 7:3 w:w blend of 270 and 96 nm anionic latex particles that do not stratify without RM. However, addition of a high molar mass polysaccharide thickener, xanthan gum, raises the viscosity and corresponding Péclet number enough to achieve small-on-top stratification as demonstrated by atomic force microscopy (AFM) measurements. Importantly, this was possible due to minimal particle-rheology modifier interactions, as demonstrated by the bulk rheology. In contrast, Carbopol 940, a microgel-based RM, was unable to achieve small-on-top stratification despite a comparable increase in viscosity. Instead, pH-dependent interactions with latex particles lead to either laterally segregated structures at pH 3 or a surface enrichment of large particles at pH 8. Strong RM-particle interactions are also observed when the triblock associative RM HEUR10kC12 is used. Here, small-on-top, large-enhanced, and randomly mixed structures were observed at respectively 0.01, 0.1, and 1 wt % HEUR10kC12. Combining rheology, dynamic light scattering, and AFM results allows the mechanisms behind the nonmonotonic stratification in the presence of associative RMs to be elucidated. Our results highlight that stratification can be predicted and controlled for RMs with weak particle interactions, while a strong RM-particle interaction may afford a wider range of stratified structures. This takes a step toward successfully harnessing stratification in coatings formulations.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
ACS Appl Polym Mater
Ano de publicação:
2023
Tipo de documento:
Article