Dynamic rheological behavior of flocculated fumed silica suspensions
作者:
Saad A. Khan,
Nancy J. Zoeller,
期刊:
Journal of Rheology
(AIP Available online 1993)
卷期:
Volume 37,
issue 6
页码: 1225-1235
ISSN:0148-6055
年代: 1993
DOI:10.1122/1.550378
出版商: The Society of Rheology
关键词: Suspensions;Particle interactions;Sol‐gel transitions;Colloidal suspensions;Light scattering, of suspensions;Dynamic rheological behavior;SUSPENSIONS;SILICA;RHEOLOGY;SOL−GEL PROCESS;LIGHT SCATTERING;COLLOIDS;FLOCCULATION;POLYPROPYLENE;GLYCOLS;OILS
数据来源: AIP
摘要:
Fumed silica suspensions in low molecular weight solvents are used in many photonic and microelectronic applications. The rheology of these thixotropic systems plays a major role in the effectiveness of their usage. In this study, we use dynamic rheological measurements to examine the particle–particle and particle–solvent interactions of fumed silica with hydrophilic and hydrophobic surface groups dispersed in both polar and nonpolar solvents, polypropylene glycol and mineral oil, respectively. We find the mineral oil‐based suspensions to have a frequency‐independent elastic modulus (G’) for all solids concentration, whereas the polypropylene glycol‐based systems exhibit a ‘‘sol–gel’’ transition to a frequency‐independentG’at high concentrations. The results are explained in terms of different solvent particle mechanisms present in the two systems. The behavior of the mineral oil suspensions are dominated by particle–particle interactions through hydrogen bonds, resulting in a gel structure. The polypropylene glycol systems, on the other hand, are dominated by the interactions of the polar solvent with the fumed silica thereby preventing the formation of a 3D gel network. Static light‐scattering experiments are used to probe the microstructure of both suspensions. We find the presence of a gel‐like network in mineral oil but not in polypropylene glycol, corroborating the rheological results. In addition, both rheology and light‐scattering data for the mineral oil suspensions are consistent with the prediction of a diffusion‐limited cluster–cluster aggregation model.
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