Coupled mutual diffusion in solutions of micelles and solubilizates

Everist, Michelle; MacNeil, Jennifer A.; Moulins, Jonathan R.; Leaist, Derek G.

doi:10.1039/b906452d

Cited by 5 publications

(15 citation statements)

References 37 publications

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“…Thus, compared with the pseudobinary prediction, decane transport down its own gradient was dramatically reduced, a phenomenon that has not been observed in recent studies of diffusion in aqueous micellar solutions with more hydrophilic solutes. 3,20 Furthermore, C 12 E 10 diffused up the decane gradient, so that D sa < 0, and decane diffused down the C 12 E 10 gradient, so that D as > 0. The magnitudes of both of the cross diffusivities were significant: the decane cross diffusivity exceeded the decane main diffusivity (D as > D aa ) above a decane concentration of 30 mM, and the surfactant cross diffusivity exceeded the main decane diffusivity in magnitude (|D sa | > D aa ) over the entire decane concentration range.…”

Section: ■ Discussionmentioning

confidence: 99%

“…Here, m 0 is the solute-free aggregation number and α is a constant interpreted as the sensitivity of the aggregation number to solubilizate. In order to determine the local micelle species concentrations C i , the distribution of solubilized decane within C 12 E 10 micelles is assumed to obey a Poisson distribution, 3,4 modified to accommodate the linear increase in aggregation number with solubilized decane:…”

Section: ■ Discussionmentioning

confidence: 99%

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Multicomponent Diffusion in Aqueous Solutions of Nonionic Micelles and Decane

2019

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Taylor dispersion and dynamic light scattering techniques were used to measure the ternary diffusivity matrix [D] and the micelle gradient diffusion coefficient, respectively, in crowded aqueous solutions of decaethylene glycol monododecyl ether (C12E10) and decane. The results indicate that C12E10 diffused down its own gradient with the micelle gradient diffusivity while decane diffused down a decane gradient at a much slower rate. Furthermore, strong diffusion coupling, comprising decane diffusion down a surfactant gradient and surfactant diffusion up a decane gradient, was also observed with cross diffusivities that were on the order of or larger than the main diffusivities. Measurements of the micelle aggregation number, hydration index, and the hydrodynamic radius, obtained using both static and dynamic light scattering methods, indicate that decane-containing micelles interacted as hard spheres and had radii and aggregation numbers that increased linearly with the molar ratio of solute to surfactant. A theoretical model, developed using Batchelor’s theory for gradient diffusion in a polydisperse system of interacting hard spheres, was effectively used to predict [D] with no adjustable parameters. A comparison with the theory indicates that decane diffused down its own gradient by micelle self-diffusion while surfactant diffused down a surfactant gradient by micelle gradient diffusion. It is also shown that intermicellar interactions drove decane diffusion down a C12E10 gradient by a volume exclusion effect while an increase in the micelle aggregation number and hydrodynamic radius with decane was necessary to drive surfactant diffusion up a decane gradient.

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Section: ■ Discussionmentioning

confidence: 99%

Section: ■ Discussionmentioning

confidence: 99%

Multicomponent Diffusion in Aqueous Solutions of Nonionic Micelles and Decane

2019

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“…Actually, this is not true: in the case of a chemical equilibrium between two solutes that are significantly different in size, the diffusion coefficients of the small molecule under the concentration gradient of the larger one should be negative. [68][69][70][71][72] Therefore the experimental trend of D 31 vs. f 3 does not clarify the nature of the predominant HSA-MPD interaction. Based on the general characteristics of similar systems, it could be hypothesized that the trend of D 31 vs. f 3 was due to the sum of the contributions of both binding and excluded volume effect.…”

Section: Diffusion Coefficientsmentioning

confidence: 98%

“…9,20 It was also used in analyzing some protein-simple salt-water systems in large excess of salt when it is possible to ignore the electrostatic protein-salt interactions. 3,19 Regarding the behavior of the ternary diffusion coefficients in the presence of a chemical equilibrium between the solutes the literature is abundant [65][66][67][68][69][70][71][72][73][74][75] and some of us have given a significant contribution to this issue. [70][71][72][73][74][75] In general the diffusion coefficient's behavior has been analyzed for different equilibrium stoichiometries, as a function of the solute concentrations and for systems with different scales of component diffusivity.…”

Section: Theoretical Sectionmentioning

confidence: 99%

Diffusion in ternary aqueous systems containing human serum albumin and precipitants of different classes

Capuano

Paduano

D’Errico

et al. 2011

Phys. Chem. Chem. Phys.

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The mutual diffusion coefficients for two aqueous ternary systems, both containing a protein, human serum albumin (HSA, component 1), were measured. The first system contained a neutral polymer, polyethylene glycol (PEG, component 2), and the second an "organic solvent", 2-methyl-2,4-pentanediol (MPD, component 3). Both PEG and MPD are used as co-precipitants in HSA crystallization protocols. Measurements were performed at constant protein concentration, with increasing precipitant content. The results obtained for the two systems were discussed and compared. In both cases, the two main diffusion coefficients, relative to the motion of the protein and of the precipitant under their own concentration gradient, can be interpreted in terms of non-specific volume interactions between the solutes. Particularly, it was showed that any possible direct HSA-MPD interaction may not have a significant effect on the values of these two diffusion coefficients. Differences arise between the cross precipitant's diffusion coefficients, relative to the motion of the precipitant under the protein concentration gradient, D(i1) with i = 2, 3. In the case of PEG, the D(21) trend vs. c(2) can be simply interpreted in terms of an "exclude volume" effect. In contrast, in the case of MPD, the D(31)vs. c(3) trend seems to indicate a more complex mechanism of transport. Because the cross precipitant's diffusion coefficient plays an important role in the crystallization process, the implication of the observed difference on the crystallization procedure was also discussed.

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“…Leaist and his group have extensively studied the diffusion of different systems using a variety of techniques, including the Taylor dispersion techniques, dynamic light scattering, thermogravitation cells, and computer simulations [25,26,27,28,29,30,31,32]. The results have provided new information about both molecular motions and interactions, in order to understand the rates of chemical and physical processes of practical significance, such as diffusion-limited reactions, carrier-mediated transport, solubilization, gas absorption, crystal growth, chemical waves and oscillations, and diffusion driven by temperature gradients (i.e., the Soret effect).…”

Section: Introductionmentioning

confidence: 99%

Transport Properties for Pharmaceutical Controlled-Release Systems: A Brief Review of the Importance of Their Study in Biological Systems

Ribeiro

Esteso

2018

Biomolecules

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The goal of this work was to comprehensive study the transport properties of controlled-release systems for the safe and reliable delivery of drugs. Special emphasis has been placed on the measurement of the diffusion of drugs, alone or in combination with carrier molecules for enhanced solubility and facilitated transport. These studies have provided detailed comprehensive information—both kinetic and thermodynamic—for the design and operation of systems for the controlled release and delivery of drugs. Cyclodextrins are among the most important carriers used in these systems. The basis for their popularity is the ability of these materials to solubilize poorly soluble drugs, generally resulting in striking increases in their water solubilities. The techniques used in these investigations include pulse voltammetry, nuclear magnetic resonance (NMR) and Raman spectroscopy, ultrasonic relaxation, and dissolution kinetics. Transport in these systems is a mutual diffusion process involving coupled fluxes of drugs and carrier molecules driven by concentration gradients. Owing to a strong association in these multicomponent systems, it is not uncommon for a diffusing solute to drive substantial coupled fluxes of other solutes, mixed electrolytes, or polymers. Thus, diffusion data, including cross-diffusion coefficients for coupled transport, are essential in order to understand the rates of many processes involving mass transport driven by chemical concentration gradients, as crystal growth and dissolution, solubilization, membrane transport, and diffusion-limited chemical reactions are all relevant to the design of controlled-release systems. While numerous studies have been carried out on these systems, few have considered the transport behavior for controlled-release systems. To remedy this situation, we decided to measure mutual diffusion coefficients for coupled diffusion in a variety of drug–carrier solutions. In summary, the main objective of the present work was to understand the physical chemistry of carrier-mediated transport phenomena in systems of controlled drug release.

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Coupled mutual diffusion in solutions of micelles and solubilizates

Cited by 5 publications

References 37 publications

Multicomponent Diffusion in Aqueous Solutions of Nonionic Micelles and Decane

Multicomponent Diffusion in Aqueous Solutions of Nonionic Micelles and Decane

Diffusion in ternary aqueous systems containing human serum albumin and precipitants of different classes

Transport Properties for Pharmaceutical Controlled-Release Systems: A Brief Review of the Importance of Their Study in Biological Systems

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