Abstract

Multicomponent liquid drops in a host liquid are very relevant in various technological applications. Their dissolution or growth dynamics is complex. Differences in solubility between the drop components combined with the solutal Marangoni effect and natural convection contribute to this complexity, which can be even further increased in combination with the ouzo effect, i.e. the spontaneous nucleation of microdroplets due to composition-dependent miscibilities in a ternary system. The quantitative understanding of this combined process is important for applications in industry, particularly for modern liquid–liquid microextraction processes. In this work, as a model system, we experimentally and theoretically explore water–ethanol drops dissolving in anethole oil. During the dissolution, we observed two types of microdroplet nucleation, namely water microdroplet nucleation in the surrounding oil at drop mid-height, and oil microdroplet nucleation in the aqueous drop, again at mid-height. The nucleated oil microdroplets are driven by Marangoni flows inside the aqueous drop and evolve into microdroplet rings. A one-dimensional multiphase and multicomponent diffusion model in combination with thermodynamic equilibrium theory is proposed to predict the behaviour of spontaneous emulsification, i.e. microdroplet nucleation, that is triggered by diffusion. A scale analysis together with experimental investigations of the fluid dynamics of the system reveals that both the solutal Marangoni flow inside the drop and the buoyancy-driven flow in the host liquid influence the diffusion-triggered emulsification process. Our work provides a physical understanding of the microdroplet nucleation by dissolution of a multicomponent drop in a host liquid.

Highlights

  • Multicomponent drops immersed in another liquid occur in a widespread range of engineering applications, such as chemical waste treatment, separation of heavy metals, food processing, diagnostics and so on (Kula, Kroner & Hustedt 1982; Ahuja 2000; Fukumoto, Yoshizawa & Ohno 2005; Chasanis, Brass & Kenig 2010; Lu et al 2017)

  • We have experimentally presented the rich phenomena of water–ethanol drops dissolving in oil as a host liquid, which encompasses the w-in-o emulsification outside the drop, the o-in-w emulsification inside the drop, the buoyancy-driven convection dominating outside the drop, and the prevailing solutal Marangoni convection inside the drop

  • A quantitative understanding and the predictions of the diffusion-induced emulsification were theoretically achieved by developing a one-dimensional multiphase and multicomponent diffusion model, which incorporates thermodynamic equilibrium theory and diffusion path theory

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Summary

Introduction

Multicomponent drops immersed in another liquid occur in a widespread range of engineering applications, such as chemical waste treatment, separation of heavy metals, food processing, diagnostics and so on (Kula, Kroner & Hustedt 1982; Ahuja 2000; Fukumoto, Yoshizawa & Ohno 2005; Chasanis, Brass & Kenig 2010; Lu et al 2017). Molecular dynamics simulations were performed by Maheshwari et al (2017) with the conclusion of the importance of the interaction between the drop constituents and the host liquid during multicomponent drop dissolution These investigations focus on pure diffusion processes. The so-called UNIFAC model, instead of the UNIQUAC model used by Chu & Prosperetti (2016), is applied here for the phase equilibria modelling, because model parameters of the latter are not available for the mixture used in this study Having obtained a good understanding of the fluid dynamics in the system, we acquire a more systematic understanding of the ouzo drop dissolution process and the preferred position where the diffusion-triggered emulsification takes place

Solution and substrate
Micro particle image velocimetry
Characteristic states of dissolving drops
Dissolution of drops with different initial ethanol concentrations
Spontaneous emulsification
Idea of the model
Liquid–liquid equilibrium at the interface
Mass transport
Diffusion coefficients
Concentration profiles
Diffusion path theory and calculated results
Model limitations
Scaling analysis
Flow motions and its influence on spontaneous emulsification
Summary and conclusions
Set-up and image analysis
Particle image velocimetry
Detecting density differences
Findings
Measured viscosity
Full Text
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