Emulsions are metastable systems typically formed in the presence of surfactant molecules, amphiphilic polymers, or solid particles, as a mixture of two mutually immiscible liquids, one of which is dispersed as very small droplets in the other. These dispersions are unwanted occurrences in some areas, like those formed during crude oil production, but are also put into many other useful applications in the oil and gas industry, food industry, and construction industry, among others. These emulsions form when two immiscible liquids come together in the presence of an emulsifying agent and sufficient agitation strong enough to disperse one of the liquids in the other. Thermodynamically, these emulsions are unstable and thus would separate into their individual phases when left alone. To be stabilized, surface-active agents (surfactants) or solids (that act in so many ways like surfactants) ought to be used. Like many commercially available products, several pharmaceutical products are usually supplied in the form of emulsions that must be stabilized before they are being administered. Pharmaceutical emulsions used for oral administration either as medications themselves or as carriers come in form of stable emulsions. Either water-in-oil (w/o) or oil-in-water (o/w), these emulsions after formulation must be classified, majorly as stable or unstable. Only formulations that give stable emulsions are used, and the unstable ones reformulated or discarded. Classifying such emulsions using results obtained by visual observation in most cases can be very tedious and inaccurate. This necessitates the use of a more scientific and intelligent method of classification. The objective of this study is to employ support vector machine (SVM) as a new technique to classify synthetic emulsions. The study will assess the effects of nonionic surfactant (sodium monooleate) and Laponite clay (LC) on the stability of synthetic emulsions prepared using a response surface methodology (RSM) based on a Box-Behnken design. The stability of the emulsions was measured using batch test and TurbiScan, and the SVM was used to classify the emulsions into stable, moderately stable and unstable emulsions. The study showed that an increase in surfactant concentration in the presence of moderate to high concentrations of LC can provide a stable emulsion. Also, a clear classification of the emulsion samples was provided by the SVM, with high accuracy and reduced misclassifications due to human error. A higher accuracy in classification would reduce the risk of using the wrong formulation for any pharmaceutical product.
Part of the book: Science and Technology Behind Nanoemulsions
In this experimental work, the adsorption of partially hydrophilic silica nanoparticles, SiO2 has been investigated to determine the degree of silica nanoparticle aggregation in the porous media. An integrated quantitative and qualitative method was used by flowing silica nanoparticles into Buff Berea cores and glass micromodel. Water wet Buff Berea cores were flooded with 5 pore volumes of 0.05% silica nanoparticles solution followed by 10 pore volumes of brine post flush subjected to 30 and 60°C. The pressure drops increased rapidly at the initial stage of silica nanoparticles injection indicated the adsorption had taken place. Pressure drops reached the maximum value of ~3.1 psi and between 26.6–82.6 psi at 30 and 60°C respectively. Pressure drops gradually declined and stabilized in between ~0.4 and ~0.7 psi after couple of pore volumes of brine post flush, suggesting complete reversible and irreversible adsorption. Micromodel test provide qualitative information where the straining or log-jamming observed in the form of gelled-like suspension when silica nanoparticles in contact with brine. The adsorption is considered reversible when the suspension decreased after post flooded with brine. Silica nanoparticles used in this experimental work shows minimal aggregation that can be beneficial as improved oil recovery agent.
Part of the book: Nano- and Microencapsulation