Scaling of self and Fickian diffusion coefficients in the critical region

De, Santanu; Shapir, Yonathan; Chimowitz, Eldred H.

doi:10.1016/s0009-2509(01)00164-6

Cited by 22 publications

(26 citation statements)

References 13 publications

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“…The fact that the parameter, a, which fits our data is 0.64, is the same as that reported by Wu et al (1988) for the temperature dependence of the diffusion coefficient in the same system, and is also well within the range of about two thirds suggested by theory (De et al, 2001) De et al, 2001), fits our data very well at temperatures close to the consolute temperature (within about 2 1C), but diverges from the experimental results as the temperature difference increases, and the activity coefficient Table 3 Values of tracer diffusivity for nitrobenzene and n-hexane measured by PFG-NMR: (a) as a function of temperature at various compositions: (i) nitrobenzene and (ii) n-hexane; (b) as a function of composition at 21 1C.…”

Section: Discussionsupporting

confidence: 90%

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Prediction of binary diffusion coefficients in non-ideal mixtures from NMR data: Hexane–nitrobenzene near its consolute point

D’Agostino

Mantle

Gladden

et al. 2011

Chemical Engineering Science

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Section: Discussionsupporting

confidence: 90%

“…where T c is the consolute temperature, D 0 is a temperatureindependent constant and a is a parameter, expected from dynamic scaling theory (De et al, 2001) to be around two thirds. Clark and Rowley (1986) give a as 0.685 for hexane/methanol and Wu et al (1988) report a to be 0.64 for hexane/nitrobenzene.…”

Section: Introductionmentioning

confidence: 99%

Prediction of binary diffusion coefficients in non-ideal mixtures from NMR data: Hexane–nitrobenzene near its consolute point

D’Agostino

Mantle

Gladden

et al. 2011

Chemical Engineering Science

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“…11 The equal-volume criterion was tested in order to ensure that the samples were critical. 20 Although the precision in the temperature measurements is only ±0.2 K, it must be taken into account that the self-diffusion coefficient does not show critical anomaly for mixtures, 21 thus the control of T is not as crucial as for the light-scattering data. The PG-NMR experiments were carried out in a Bruker AVANCE AV-5000 apparatus, with a BPP-LED ͑LED͒light-emitting diode pulse sequence.…”

Section: Methodsmentioning

confidence: 99%

Collective and self-diffusion coefficients in an ionic critical mixture: 3-methylpyridine+water+NaBr

Hernández

Monroy

Ortega

et al. 2005

The Journal of Chemical Physics

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The dynamics of concentration fluctuations of three critical samples of the 3-methylpyridine (3MP)+water+NaBr system have been measured by photon correlation spectroscopy. The collective-diffusion coefficient D shows the usual Ising behavior near the critical temperature T(c). However, as |T-T(c)| increases, the dynamic correlation length calculated from D, xi, takes values higher than the correlation length of the critical fluctuations calculated from static light scattering, xi(s). At the largest |T-T(c)| measured, xi approaches the value, xi(0,d) approximately equal to 1.13 nm, while the amplitude of xi(s) is xi(0,s)=0.38 nm. Pulsed-gradient NMR spectroscopy points out the existence of two dynamic contributions. One of them is consistent with the existence of molecular entities of hydrodynamic radius 0.31 nm, while the other one indicates the existence of aggregates rich in 3MP of radius 1.16 nm. The existence of the aggregates may explain the apparent anomalous behavior of the dynamic light scattering experiments for this system far from the critical point.

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“…where T c is the consolute temperature, D 0 is a temperature independent constant and α is a parameter from dynamic scaling theory (De et al, 2001), usually taken to be to be around two-thirds (Cussler, 2009;Clark and Rowley 1986;Wu et al, 1988). D' Agostino et al (2011) used the temperature dependence in Eq.…”

Section: Introductionmentioning

confidence: 99%

A local composition model for the prediction of mutual diffusion coefficients in binary liquid mixtures from tracer diffusion coefficients

Zhu

Moggridge

D’Agostino

2015

Chemical Engineering Science

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Recently a simple model was shown to predict mutual diffusivities in non-ideal binary mixtures. The analysis was extended to cases where strong molecular association was hypothesised to occur. In this previous model, a priori knowledge on cluster formation is needed. A new method to predict mutual diffusivity is proposed, based on local composition. The method is shown to accurately predict mutual diffusivity without any a priori assumption. a b s t r a c tIn a recent publication (Moggridge, 2012a. Chem. Eng. Sci. 71, 226-238), a simple equation was shown to accurately predict the mutual diffusion coefficients for a wide range of non-ideal binary mixtures from the tracer diffusion coefficients and thermodynamic correction factor, on the physical basis that the dynamic concentration fluctuations in the liquid mixture result in a reduction of the mean thermodynamic correction factor relative to the hypothetical case in which such fluctuations do not occur. The analysis was extended to cases where strong molecular association was hypothesised to occur in the form of dimerization of a polar species in mixtures with a non-polar one. This required modification of the average molecular mobility in the form of doubling the tracer diffusivity of the dimerized species (Moggridge, 2012b. Chem. Eng. Sci. 76,[199][200][201][202][203][204][205]. Predictions were found to show good accuracy for the mixtures investigated. One of the difficulties with this approach is that it is an a posteriori correction: there is no a priori way of knowing whether strong cluster formation influences the observed molecular mobility, or what the appropriate size of the cluster is.In this work, a modification is made to the average molecular mobility in the original equation by replacing the bulk mole fraction with local mole fraction calculated using the NRTL (non-random two liquid) model, to take account of strong molecular association that results in highly correlated movement during diffusion. The new equation enables an accurate description of mutual diffusion coefficients in mixtures of one strongly selfassociating species and one non-polar species, as well as in non-ideal, non-associating mixtures. This result is significant because in this way there is no need of any prior knowledge on the degree of molecular association in the mixture for the prediction of mutual diffusion coefficients from tracer diffusivities.

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Scaling of self and Fickian diffusion coefficients in the critical region

Cited by 22 publications

References 13 publications

Prediction of binary diffusion coefficients in non-ideal mixtures from NMR data: Hexane–nitrobenzene near its consolute point

Prediction of binary diffusion coefficients in non-ideal mixtures from NMR data: Hexane–nitrobenzene near its consolute point

Collective and self-diffusion coefficients in an ionic critical mixture: 3-methylpyridine+water+NaBr

A local composition model for the prediction of mutual diffusion coefficients in binary liquid mixtures from tracer diffusion coefficients

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