Analytically predicting the viscosity of hard sphere suspensions from the particle size distribution

Shewan, Heather M.; Stokes, Jason R.

doi:10.1016/j.jnnfm.2014.09.002

Cited by 68 publications

(43 citation statements)

References 51 publications

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“…The bidisperse data lie below the correlation line, indicating that the jamming point is increased with particle bidispersity. This behavior for the bidisperse system is in good agreement with most previous experiments [Farris, 1968;Shapiro and Probstein, 1992;Gondret and Petit, 1997;He and Ekere, 2001;Shewan and Stokes, 2015]. Simulations of bidisperse suspensions by Chang and Powell (1993) suggest that the reduction of viscosity is due to a disruption of clustering that is seen in monodisperse suspensions.…”

Section: A Viscositysupporting

confidence: 90%

Shear and normal stress measurements in non-Brownian monodisperse and bidisperse suspensions

Gamonpilas¹,

Morris²,

Denn³

2016

Journal of Rheology

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There was an error in data reduction, resulting in incorrect values for the normal stress differences N 1 and N 2 shown in Figs. 7-10, and the corrected figures are shown here. In particular, the algebraic sign of N 1 is changed, as are the relative magnitudes of N 1 and N 2 . The negative values of N 1 for these non-shear-thickening suspensions are larger in magnitude than those reported by other workers, but both N 1 and N 2 are in general agreement with the accelerated Stokesian Dynamics calculations of Sierou and Brady [1].FIG. 7. Normal stress differences N 1 and N 2 of monodisperse suspensions at volume fractions of 0.4 and 0.5 for both 10 and 52.6 µm particles as functions of (a) shear rate and (b) shear stress (Insets are the same data on a semilog scale). SYNOPSISWe have measured the viscometric functions of mono-and bimodal non-colloidal suspensions of PMMA spheres in a density-matched aqueous Newtonian suspending fluid using parallelplate and cone-and-plate rheometry for particle volume fractions in the range 0.20 to 0.50.Cone-and-plate normal stress measurements employed the method of Marsh and Pearson, in which there is a finite gap between the cone tip and the plate. The monodisperse suspensionsshowed an unexpected particle size dependence of the viscometric functions, with the viscosity increasing with decreasing particle size. Normal stresses were very small in magnitude and difficult to measure at volume fractions below 0.30. At higher concentrations, N 2 was negative and much larger in magnitude than N 1 , for which the algebraic sign was positive over most of the shear rate range for the monodisperse suspensions but indeterminate and possibly negative for the bimodal suspensions. The normal stresses were insensitive to bidispersity when plotted as functions of the shear stress at each volume fraction.

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Section: A Viscositysupporting

confidence: 90%

Shear and normal stress measurements in non-Brownian monodisperse and bidisperse suspensions

Gamonpilas¹,

Morris²,

Denn³

2016

Journal of Rheology

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“…The outcomes are expected to provide insights that are equally applicable to colloidal-scale microgels. Having previously shown that a single theoretical model is applicable to both colloidal and non-colloidal hard spheres [15], we demonstrate here its applicability to non-colloidal soft spheres that do not contain long range interactive forces; these include emulsion droplets and microgel spheres. In our recent study [15], we show that Eq.…”

Section: Introductionmentioning

confidence: 73%

“…We recently showed that / m for many colloidal and non-colloidal hard sphere suspensions, and particularly those with some polydispersity, is reasonably predicted by the packing fraction of randomly closepacked spheres, / rcp [15]. For suspensions of soft spheres, the critical / 0 value (/ 0m ) is not so well-defined as it increases with decreasing particle modulus [16] and cross-link density [17]; we note that values of / 0 > 1 and / 0m > 1 are commonly reported [3].…”

Section: Introductionmentioning

confidence: 99%

Viscosity of soft spherical micro-hydrogel suspensions

Shewan

Stokes

2015

Journal of Colloid and Interface Science

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“…Recently, there has been renewed interest in modeling higher order effects of polydispersity on suspension viscosity (Qi and Tanner, 2012;Dörr et al, 2013;Farr, 2014;Faroughi and Huber, 2014;Shewan and Stokes, 2015;and Mwasame et al, 2016). In a previous publication (Mwasame et al, 2016), we described a modification and extension of the approach of Farris (1968) to develop a model for the viscosity of binary and polydisperse non-colloidal suspensions.…”

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confidence: 99%

Modeling the viscosity of polydisperse suspensions: Improvements in prediction of limiting behavior

2016

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The present study develops a fully consistent extension of the approach pioneered by Farris [“Prediction of the viscosity of multimodal suspensions from unimodal viscosity data,” Trans. Soc. Rheol. 12, 281–301 (1968)] to describe the viscosity of polydisperse suspensions significantly improving upon our previous model [P. M. Mwasame, N. J. Wagner, and A. N. Beris, “Modeling the effects of polydispersity on the viscosity of noncolloidal hard sphere suspensions,” J. Rheol. 60, 225-240 (2016)]. The new model captures the Farris limit of large size differences between consecutive particle size classes in a suspension. Moreover, the new model includes a further generalization that enables its application to real, complex suspensions that deviate from ideal non-colloidal suspension behavior. The capability of the new model to predict the viscosity of complex suspensions is illustrated by comparison against experimental data.

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Analytically predicting the viscosity of hard sphere suspensions from the particle size distribution

Cited by 68 publications

References 51 publications

Shear and normal stress measurements in non-Brownian monodisperse and bidisperse suspensions

Shear and normal stress measurements in non-Brownian monodisperse and bidisperse suspensions

Viscosity of soft spherical micro-hydrogel suspensions

Modeling the viscosity of polydisperse suspensions: Improvements in prediction of limiting behavior

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