Stabilization of Dispersed Oil Droplets in Nanoemulsions by

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Interface Components: Nanoparticles, Colloids, Emulsions, Surfactants, Proteins, Polymers

Stabilization of Dispersed Oil Droplets in Nanoemulsions by Synergistic Effects of Gemini Surfactant, PHPA Polymer and Silica Nanoparticle Nilanjan Pal, Narendra Kumar, and Ajay Mandal Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.8b03364 • Publication Date (Web): 23 Jan 2019 Downloaded from http://pubs.acs.org on January 24, 2019

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Stabilization of Dispersed Oil Droplets in Nanoemulsions by Synergistic Effects of Gemini Surfactant, PHPA Polymer and Silica Nanoparticle Nilanjan Pal, Narendra Kumar, Ajay Mandal* Indian Institute of Technology (Indian School of Mines), Dhanbad-826004, India *Corresponding Author, Email: [email protected] Abstract Nanoemulsion systems comprising n-heptane (oleic component), stabilized by {gemini surfactant (14-6-14 GS) + polymer (PHPA) + silica (SiO2) nanoparticle} shell, and dispersed in aqueous phase, were synthesized by ultrasonication (high-energy method). Influence of ultrasonication time on nanoemulsion kinetics was investigated to predict the saturation droplet diameter. Morphological analysis by cryoTEM imaging showed that oleic phase appears as uniformly dispersed spherical droplets in 14-6-14 GS stabilized nanoemulsion; which on PHPA addition changes into a network structure consisting of larger oil droplets. 14-6-14 + PHPA + SiO2 nanoemulsion systems show more effective packing arrangement with irregular-shaped (non-spherical) droplets. Dynamic light scattering (DLS) studies identified droplet size distribution profiles in the range 4.2-25.4 nm for surfactant-stabilized nanoemulsion; 125.9-358.8 nm for surfactant-polymer nanoemulsion; and 88.4-222.3 nm for surfactant-polymer-nanoparticle based nanoemulsion in optimal dosage(s). Statistical analyses were performed using normal, log-normal and Cauchy-Lorentz distribution functions. A modified form of Hinze theory was employed to model droplet behavior in analysed nanoemulsion systems. Zeta potential values of nanoemulsions were studied at different time intervals to determine kinetic stability as well as corroborate Hinze model findings. In summary, this article aims at investigating nanoemulsion droplet stability by thorough examination of electrostatic repulsive barrier and steric hindrance effects. Keywords Nanoemulsion; High-energy synthesis; Surfactant-Polymer-Nanoparticle; Dynamic light scattering; CryoTEM imaging; Hinze theory; Oil Droplet deformation; Zeta potential INTRODUCTION Nanoemulsions are kinetically stable liquid-phase droplets dispersed in a continuous immiscible phase. Nanoemulsion droplet sizes fall in the range 20-500 nm, depending on the composition and structural arrangement constituting emulsifier particles responsible for stabilization.1,2 Selection of emulsifier(s) is critical for the phenomena of simultaneous droplet

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breakdown and droplet coalescence, which is a function of the interfacial energy barrier per unit area between the dispersed oil droplet and continuous aqueous phase. Controlling emulsion droplet stability and behavior plays a primary role in the development of many interesting properties, such as robust stability, low interfacial tension (IFT) and high surface area.1-3 Nanoemulsions essentially permit solubilization and transport of hydrophobic materials within a water-based medium. This ability is useful in pharmaceutical applications and drug delivery research as carriers of nutrients, contrast agents and drugs to targeted organs and tissues for treatment/imaging purposes.4,5 In addition, nanoemulsions aid in crystallization of active pharmaceutical ingredients (APIs) to form drug nanocrystals.6,7 Nanoemulsions are also suitable in manufacturing smart cosmetic products to target external tissues.8 The food and agriculture industry has recently focussed on nanoemulsion-based delivery of proteins, functional foods and minerals to achieve increased productivity and sustenance of plant and animal tissues.9,10 A recent scope for the use of nanoemulsion systems involves the field of enhanced oil recovery (EOR) in the petroleum sector, wherein specially designed nanoemulsions are employed as chemical injecting fluids to displace (residual) crude oil trapped within mature reservoir pores.11,12 A rational design for nanoemulsion formulation is, therefore, pivotal for use in various applications. Main components of nanoemulsion systems comprise oil, water and emulsifier(s) that function via electrostatic interactions and/or steric hindrance.13,14 Typical emulsifiers consist of surfactant, polymer and/or nanoparticle with different mechanisms to obtain a common effect i.e. droplet stabilization.13,14 Techniques for nanoemulsion synthesis are broadly classified as low-energy and high-energy methods.15-17 Low-energy methods employ the IFT reduction ability of nanoemulsions to achieve suitable phase transitions without using excess shear.15,16 During high-energy or dispersion methods, such as in ultrasonication, a significant energy input (~108 W/kg) is required to cause droplet breakdown under high shear conditions.16,17 Ultrasound technology has attracted rapid interest in the field of research due to formation of more homogenous systems in comparison to conventional low-energy mechanical processes; and better control during synthesis in comparison to other high-energy methods (such as highpressure homogenization, micro-fluidization and jet dispersion).17,18 However, this method still requires a comprehensive theoretical approach to investigate droplet behavior and distribution due to numerous gaps existing in existing literature, especially wherein the nature of emulsifier varies and different emulsifier components are involved in a single system. Through this paper,


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the authors aim at addressing this specific issue by correlating experimental studies with a modified theoretical approach. An ability to understand oil droplet behavior in nanoemulsions is critical from the mechanistic point of view. Previously, researchers used the classical theory of Taylor to predict forces acting on an isolated droplet in a continuous phase, considering laminar flow behavior.19,20 In his report, Taylor stated that droplet breakdown occurs when the applied stress overcomes the interfacial stress that holds the droplet together. This model was further improved by Hinze by incorporating the turbulence effect during nanoemulsion formation.21,22 As per Hinze’s correlation, an adequate inertial stress is required to dominate over the oil-aqueous interfacial stresses as well as viscous stresses.21-23 This model can be effectively applied during ultrasonication process, wherein turbulent flow property exists. Another drawback of Taylor’s theory is that the droplet sizes are assumed independent of the viscosity of dispersed (hydrophobic) phase.19,20 This assumption is flawed and the theoretical background of the study requires a much more refined approach, as in Hinze’s model. Besides Weber number (elucidating inertial stress effect on droplet behavior), a dimensionless group known as Ohnesorge number (Oh) is needed to describe the effect of droplet viscosity on experiment size distribution.22-24 The major difference between classical microemulsions and nanoemulsions lies in the type of stability.25,26 Unlike thermodynamically stable microemulsions, nanoemulsions exhibit kinetic stability, i.e. the ability to remain stable for considerable time periods. In addition, nanoemulsion systems are generally insensitive to variations in composition and temperature.26 Though zeta potential values provide an idea about emulsion stability, it has been previously established that a higher zeta potential does not essentially mean improves stability. This is because zeta potential measurements fail to take the effect of shear hindrance of encapsulating material into account.27 Therefore, incorporation of theoretical model discussions to study experimental data can prove to be very useful to insightful in understanding results and their underlying influences that are otherwise not predictable in nanoemulsion science. Nanoemulsion droplet stability is the result of relative influences of surface tension reduction, electrostatic repulsion, stearic hindrance and viscosity modification.28 As per the interfacial tension theory, emulsifier molecules reduces IFT between two immiscible phases (oil, water) and diminishes the attraction between oil droplets.28,29 The repulsion/ steric hindrance effects cause the formation of a film containing globules onto oil droplet surfaces, which tend to remain suspended in dispersion medium.28-30 Incorporation of emulsifiers (particularly PHPA


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polymer, SiO2 nanoparticles) contribute toward increasing fluid viscosity and miscible viscous suspension of oil droplets/glubes within the continuous nanoemulsion phase.31 This paper primarily deals with the integration of experimental data and theoretical approach employed in nanoemulsion formulation. Initially, the preparation of oil-in-water nanoemulsions and droplet kinetics with varying ultrasonication times were investigated. Size distribution profiles obtained by DLS technique were plotted as intensity-weighted and modelled using probability distribution functions. CryoTEM images were analysed visually in order to corroborate droplet size data. The authors have built upon the previous theoretical approach, suggesting a modified Hinze theory to explain droplet behavior and stability for varying emulsifier compositions. Nanoemulsions were characterized by zeta potential measurements to explain stability with the aid of modified Hinze theory. EXPERIMENTAL SECTION Materials The gemini surfactant, N,N′-bis(dimethyltetradecyl)-1,6-hexanediammonium bromide, abbreviated as 14-6-14 GS, was synthesized via two stages of nucleophilic substitution (SN2) reaction in the laboratory, as discussed in our previous studies.32,33 n-Heptane, employed as oil phase in nanoemulsion synthesis, was procured from Merck Chemicals. Partially hydrolysed poly-acrylamide (PHPA) polymer was obtained from SNF Floerger Company (Andrézieux, France). It is a water-soluble polymer with bulk density 850-950 kg/m3. As per API RP 63 (American Petroleum Institute: Recommended Practices for evaluation of polymers used in EOR operations) evaluation, PHPA has high molecular weight of 2.1 × 107 g/mol with 26.4% hydrolysis. Silica (SiO2) nanopowder (particle size range 5-15 nm; specific surface area 590690 m2/g) was purchased from Sigma-Aldrich (Merck Industries). Nanoemulsion preparation by High-Energy method Nanoemulsion droplets comprised a hydrophobic core comprising n-heptane (oleic component), stabilized by {gemini surfactant (14-6-14 GS) + polymer (PHPA) + silica (SiO2) nanoparticle} shell, and dispersed in an aqueous phase. In this study, nanoemulsions were prepared by high-energy method. Initially, nanoemulsion synthesis process involved uniform mixing/stirring a solution containing 10% (v/v) oil (n-heptane) and 90% (v/v) aqueous solution using a magnetic stir bar for 15 minutes at 1000 rpm. Thereafter, nanoemulsions were prepared by ultrasonication using Hielscher UP200Ht Ultrasonic Homogenizer (200 W, 26 kHz).


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During ultrasonication, acoustic waves create cavitation bubbles. Ultimately, these bubbles implode generating sufficiently high shear conditions to break oil droplets into smaller ones.1,34 This process continues until droplet size reaches nanoscale range; and become independent of the ultrasonication time.1,14,34,35 The influence of varying ultrasonication times on oil (nheptane) droplet size have been studied with the help of dynamic light scattering (DLS) experiments. Fig. 1 shows the mechanism for preparation of nanoemulsion oil droplets by ultrasonication (high-energy method).

Fig. 1. Schematic for nanoemulsion synthesis by ultrasonication method. Acoustic waves generated during this process create cavitation bubbles that implode and collapse, generating highly localized shear stresses to break emulsion droplets into nano-sized droplets.

Nanoemulsion droplet characterization Dynamic light scattering (DLS) studies Nanoemulsions were characterized with the aid of Zetasizer Nano S90 (Malvern, Germany) dynamic light scattering apparatus at 303 K. The instrument, equipped with a He-Ne laser (633 nm, max. 4 mV), measured the average droplet sizes as well as size distribution of nanoemulsion formulations. The nanoemulsion sample(s) placed in a quartz cuvette was exposed to monochromatic light beam that scattered after interacting with dispersed oil droplets. The scattered beam passed through a vertical polarizer and counter via photomultiplier tube, resulting in translational diffusion of droplets in Brownian (random) motion in the nanoemulsion phase. Scattering angle of 90° was employed to study the droplet size distribution using 21CFR part 11 software. Prior to DLS testing of nanoemulsion


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specimen, a portable Refracto 30PX meter was employed to determine the refractive indices. Absorbance values were determined in the wavelength range 200-800 nm using UV-1800 UVVIS Spectrophotometer (Shimadzu, Japan). Each DLS experiment was repeated three times to ensure better repeatability of results. Transmission electron cryomicroscopy (CryoTEM) The morphology as well as structure of oil droplets nanoemulsion specimens were studied using a JOEL JEM-2100F Multipurpose Transmission electron microscope, operating a field emission electro gun (120-200 kV acceleration voltage). Initially, the specimens were vitrified using a controlled environment vitrification system (CEVS) to preserve the native structure of the fluid. Liquid nitrogen atmosphere was employed to achieve a fixed (cryogenic) temperature of -170°C. Using a micropipette, a small quantity of nanoemulsion was spread on a carboncoated copper (Cu) grid and preserved (or vitrified) in a frozen hydrated state by rapid freezing in liquid ethane, near liquid nitrogen temperature (-170°C). While maintaining temperature condition, the nanoemulsion samples were introduced into the electron microscope column and thereafter, vacuum condition was achieved in the system. Ethane cryogen was used due to its superior thermal contact with nanoemulsion in comparison to low boiling point liquid nitrogen. The vitrified sample(s) were transferred under the electron microscope apparatus with the help of Gatan 626 cryoholder; and the transmitted electrons from nanoemulsion analyses obtained the magnified images. Zeta potential measurements Potential at the shear (or slipping) plane within the diffused layers is determined as zeta potential or electrokinetic potential, denoted by the symbol ζ. Zeta-Meter 4.0 was employed for determination of zeta potential onto oil droplet surface in prepared nanoemulsions at 303 K. Nanoemulsion fluids were initially placed in a viewing chamber and electric field was activated for potentiometric measurements. In nanoemulsion continuous phase, an electrical double layer develops between the oil droplet and surrounding continuous medium. Zeta potential value is measured as the potential difference existing between the dispersed oil droplets and aqueous dispersion medium. Using microelectrophoresis principle, the electrophoretic velocity of dispersed oil droplet(s) is directly proportional to zeta potential values, measured in millivolts (mV). However, this measurement assumes only electrostatic repulsive interactions as the primary factor affecting emulsion stability. However, the steric hindrance of the coating assembly also plays an important role in improving formulation stability, which is irrespective of the surface charge between the oil droplet surfaces. Therefore,


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zeta potential determination predicts the relative influences of electrostatic and steric bulk interactions within the nanoemulsion phase.13,35 In conjunction with modified theoretical approach, this study may prove to be a constructive tool for understanding droplet behavior and stabilization. RESULTS AND DISCUSSIONS Effect of ultrasonication time on oil droplet size The nanoemulsion system in this study consists of an n-heptane oil core stabilized in the presence of 14-6-14 gemini surfactant / PHPA polymer / SiO2 nanoparticle; and dispersed in aqueous medium. Earlier studies show that the average nanoemulsion droplet size decays in an exponential trend with increase in ultrasonication time as well as other factors such as number of passes in homogenizer, intensity and duty cycle.14,36-38 The influence of ultrasonication time is based on modelling droplet populations and determining average droplet size values for emulsions in the submicron range.39,40 This differentiation in nanoemulsions from generalized emulsion systems exists in deformability property, thereby controlling the emulsifier-induced stabilization of newly created interfaces.14 Taisne et al. reported that formed nano-sized oil droplets do not show significant coalescing behavior, which would otherwise be evident in deviation to exponential decay kinetics from analysis of droplet size versus ultrasonication time plots.41 Hence, investigations on droplet kinetics is pivotal for the effective prediction of droplet size during ultrasonication.13,14,23,36,41,42 The evolution of oil droplet size with increasing ultrasonication times for different nanoemulsion compositions is depicted in Fig. 2. Visual observation of prepared nanoemulsions shows that phase behavior gradually changes from translucent (milky) to transparent in appearance as ultrasonication time is increased. Initially, the average droplet size (average hydrodynamic diameter) decreases with passage of time in an exponentially decaying trend. The initial decrease in dh with time is attributed to decreasing interfacial tension between the dispersed oil droplets and surrounding aqueous medium in nanoemulsion system, which makes it easier for the high shear conditions to cause rupture of oil droplets. At the end of specified time interval(s), a state of droplet stability is achieved wherein the oil droplets remain unaffected by the implosion of cavitation bubbles in nanoemulsion phase; and the droplet size becomes roughly constant.13,14,42 At this condition, the oil droplet size may be described as an independent function of time elapsed. In order to follow emulsification process during ultrasonication, the size evolution of each type of nanoemulsion system can be fitted with the help of a mono-exponential function of sonication time (t) as depicted in Eq. (1).


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𝑑ℎ(𝑡) = 𝑑𝑜 +𝐴𝑒(𝑡 ― 𝑡𝑜)/𝜏

(1)

In the above relation, dh(t) is the mean or average oil droplet diameter (nm), do is the saturation diameter of droplets (nm), A is the value of fitting constant and 𝜏 is the characteristic decay time (min). Prior to to (=1 min), no droplet size data were recorded due to extreme heterogeneity of the system. Table 1 shows the exponential decay parameters (with standard deviation) and measures favourable regression coefficient values for different nanoemulsion compositions. It is pertinent that to = 1 min represents the first measurable instant of time at which the stabilized oil droplet has the average diameter value of do + A. Analysis of average oil droplet versus ultrasonication time plots shows good fitting values (> 0.99) in all nanoemulsion systems. The oil droplet diameter (after saturation) values are found to be 21.31 (± 3.43) nm, 173.66 (± 2.24 nm) and 93.88 (± 3.22 nm) using Levenberg-Marquardt algorithm in exponential decay function for nanoemulsion systems containing only surfactant, surfactant + polymer and surfactant + polymer + nanoparticle systems respectively. 1000 0.100 % 14-6-14 GS 0.100 % 14-6-14 GS + 0.050 % PHPA

900

0.100 % 14-6-14 GS + 0.050 % PHPA + 0.025 % SiO 2

800

Average droplet diameter, dh (nm)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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700 600 500 400 300 200 100 0 0

5

10

15

20

25

30

Ultrasonication time (mins)

Fig. 2. Experimental data showing evolution of oil droplet as a function of ultrasonication time-period. Phase behavior of prepared nanoemulsions gradually altered from translucent to transparent state with increasing ultrasonication times. Table 1. Mono-exponential decay parameters for different oil-in-water nanoemulsion systems at 303 K Nanoemulsion compositions

0.10 % 14-6-14 GS

Exponential decay parameters Saturation

Exponential fitting

Characteristic

Coefficient of

diameter, do ±

constant, A

decay time, to

determination,

S.D. (nm)

(nm)

(min)

R2

21.31 ± 3.43

387.72

3.02

0.994


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0.10 % 14-6-14 GS +

173.66 ± 2.24

519.56

4.02

0.999

93.88 ± 3.22

458.68

4.12

0.997

0.05 % PHPA 0.10 % 14-6-14 GS + 0.05 % PHPA + 0.025 % SiO2 NP

CryoTEM (Transmission Electron microscopy) analyses The morphological structures of oil droplets in oil-in-water nanoemulsion systems are analysed using cryoTEM imaging technique. The microscopic images obtained under vitrified, frozenhydrated condition confirm the formation of nanoemulsions for different compositions, as depicted in Fig. 3. CryoTEM analyses allow direct, visual investigation of nanoemulsion droplets and the relative influences of addition of surfactant/polymer/nanoparticle. In this study, the original oil droplet structure is retained by freezing and subsequent preservation involved during sample preparation, displaying a dispersion of nano-sized oil droplets (dark spots) in continuous aqueous medium (white background). Dynamic light scattering (DLS) results alone does not reveal changes in droplet size, resulting in inaccurate representation of nanoemulsion stability. Thus, larger-sized droplets formed due to aggregation or flocculation of oil remain unnoticed during DLS studies.12,43 Therefore, data analysis of DLS data combined with visual analysis of CryoTEM micrographs provide improved insight about the effects of PHPA and/or SiO2 addition on nanoemulsion stability. Fig. 3(a) shows that oil droplets uniformly appear as spherical/rounded spots, separated from one another, indicating the formation of 14-6-14 GS coating that effectively prevents the coalescing ability of nanoemulsion droplets and causes emulsion stabilization. Addition of PHPA polymer in surfactant-stabilized nanoemulsions results in the formation of network-like structure comprising dispersed oil droplets, as evident in Fig. 3(b). This may be attributed to the adsorption of PHPA molecules, which tend to encapsulate the surfactant-stabilized oil droplets and entangle with one another to form a network. In this type of nanoemulsion, the ratio of adsorbed layer thickness to droplet thickness plays an important role in understanding morphological characteristics of nanoemulsion.44 For surfactant-polymer-nanoparticle (SPN) nanoemulsions, a more well-defined morphology (consisting of network of smaller, irregularsized oil droplets) is observed as evident from analysis of Fig. 3(c). The addition of silica (SiO2) nanoparticles improves nanoemulsion stability by promoting adsorption at the oil-aqueous interfaces and preventing further aggregation of oil droplets.14,45,46 The average size of dispersed oil droplets decrease on addition of nanoparticles. In addition, non-uniformity in


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shape/morphology is evident in the presence of SiO2 nanoparticles, showing effective packing arrangement as compared to uniformly dispersed, spherical/rounded droplets in surfactantstabilized nanoemulsions (Fig. 3(a)). Therefore, nanoemulsions can be effectively optimized, depending on 14-6-14 GS/PHPA polymer/silica nanoparticle compositions.


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Fig. 3. Cryo-TEM micrographs of nanoemulsions with varying compositions: (a) Surfactant-stabilized nanoemulsion droplets magnified at 500 nm; (b) Surfactant-polymer nanoemulsions magnified at 500 nm; (c) Surfactant-polymer-nanoparticle nanoemulsions magnified to 500 nm

Droplet size distribution studies Droplet size measurements by dynamic light scattering (DLS) technique confirm the formation of nanoemulsions, thereby corroborating results obtained from CryoTEM imaging analyses. Figs. 4(a), 4(b) and 4(c) depict the respective intensity-weighted size distribution profiles for nanoemulsion systems in the presence of surfactant, surfactant + polymer and surfactant + polymer + nanoparticle. For surfactant-stabilized nanoemulsions, large droplet sizes are observed at low surfactant concentration due to relatively high interfacial tension (IFT) and oil droplet coalescing ability, as supported by low zeta potential values. Oil droplets are identified in the size range 6.8-35.6 nm, 5.8-29.2 nm and 4.2-25.4 nm for nanoemulsion systems containing 0.020%, 0.050% and 0.100% 14-6-14 GS respectively. With increasing surfactant concentration, the average size of oil droplets decrease due to electrical repulsive interactions existing among the surfactant molecules adsorbed onto droplet surface.47 This significantly reduces IFT values at the interface between oil droplet and surrounding aqueous phase by increasing interfacial area available for GS adsorption, denoting improved rate of droplet breakdown and increase in emulsion stability. Increase in surfactant dosage to concentrations as high as 0.200% 14-6-14 GS exhibits a shift in droplet profile (size ranging between 4.5 nm and 31.9 nm) to the right, indicating an increased tendency of oil droplets to recoalesce. This is attributed to increase in subsequent encounter of a formed nano-sized oil droplet with its


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neighbouring droplets.48,49 When GS concentration is increased to 0.200% from 0.100% in nanoemulsion fluid, the time-scale of surfactant adsorption onto droplet surface becomes longer than that of collision, thereby reducing the ability of 14-6-14 GS molecules to sufficient cover the newly created oil-aqueous interfaces and increasing oil droplet sizes within continuous nanoemulsion phase.48-50 Addition of PHPA polymer to surfactant-stabilized nanoemulsions causes increase in average droplet size due to the entanglement of adsorbed polymer molecule chains, which results in formation of a network-like structure with improved aggregation ability.44,51 The thickness of polymer + surfactant coating layer is pivotal in the measurement of DLS data. Stabilization of oil droplets in the presence of 14-6-14 GS is observed to increase from 4.2-25.4 nm in the absence of PHPA to 102.6-298.6 nm, 110.4-319.4 nm, 125.9-358.8 nm and 181.5-463.7 nm in the presence of 0.010% PHPA, 0.025% PHPA, 0.050% PHPA and 0.100% PHPA respectively. PHPA reduces the repulsions between the adsorbed surfactant molecules by steric hindrance of long-chain particles (polymers). With increasing polymer concentration, the coating layer thickness is observed to increase and nanoemulsion droplets undergo steric stabilization. As a result, polymer addition may lead to increase in oil droplet size without significant loss in stability. Silica nanoparticles show synergistic influence on oil droplet behavior in the presence of 146-14 GS and PHPA polymer. SiO2 particles are adsorbed onto the oil-aqueous interface, thereby lowering interfacial tension at the oil-aqueous interface. The mechanical barrier formed by nanoparticles in conjugation with polymer + surfactant molecules protect the dispersed oil droplets from flocculation and recoalescence; and form robust, stable interfaces between the oil and aqueous phases. Therefore, GS + PHPA + SiO2 act as electrostatic and steric barriers/stabilizers against droplet coalescence. For surfactant-polymer-nanoparticle (SPN) systems, the coating layer generate more stable barriers with lower coating thickness-to-droplet diameter ratio than those in case of surfactant-polymer nanoemulsions.14,45,46 Therefore, introduction of silica nanoparticles improves the stability of nanoemulsions containing 14-614 GS and PHPA polymer. With increasing nanoparticle concentration, SiO2 particles are more effectively adsorbed and closely packed onto the oil-aqueous interface, resulting in reduction in average size of saturated oil droplets. Like surfactant-polymer systems, a smaller distribution peak is also obtained in the intensity versus hydrodynamic diameter plots for surfactantpolymer-nanoparticle emulsions containing 0.010% SiO2, 0.025% SiO2, 0.050% SiO2 and 0.100% SiO2 with respective droplet size profiles in ranges 96.6-248.7 nm, 88.4-222.3 nm, 80.2-198.4 nm and 78.1-191.6 nm. Minimization of interfacial energy by surfactant is


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functional in holding the polymer as well as nanoparticles at the oil-aqueous interface; and controls the formation of three-dimensional network structure formed by the irreversible bridging of PHPA polymeric chain with SiO2 nanoparticles. 35

30

0.020% 14-6-14 GS 0.050% 14-6-14 GS 0.100% 14-6-14 GS 0.200% 14-6-14 GS

(a)

Intensity (%)

25

20

15

10

5

0 1

10

100

Hydrodynamic diameter, dh (nm) 50 0.100% 14-6-14 GS + 0.010% PHPA 0.100% 14-6-14 GS + 0.025% PHPA 0.100% 14-6-14 GS + 0.050% PHPA 0.100% 14-6-14 GS + 0.100% PHPA

45

(b)

40 35

Intensity (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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30 25 20 15 10 5 0 10

100

Hydrodynamic diameter, dh (nm)


1000

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65

0.100% 14-6-14 GS + 0.050% PHPA + 0.010% SiO2 NP

60 55

0.100% 14-6-14 GS + 0.050% PHPA + 0.025% SiO2 NP

(c)

50

0.100% 14-6-14 GS + 0.050% PHPA + 0.050% SiO2 NP 0.100% 14-6-14 GS + 0.050% PHPA + 0.100% SiO2 NP

45

Intensity (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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40 35 30 25 20 15 10 5 0 10

100

1000

Hydrodynamic diameter, dh (nm)

Fig. 4. Nanoemulsion droplet size distribution expressed as scattering intensity versus hydrodynamic diameter (dh) plots with varying concentrations: (a) 14-6-14 GS-based nanoemulsions; (b) 14-6-14 GS + PHPA stabilized nanoemulsions; (c) 14-6-14 GS + PHPA + SiO2 stabilized nanoemulsions

The statistical analysis of droplet size distribution has been performed using different analytical probability functions. This study has significant importance in the size analysis of oil droplets dispersed within the nanoemulsion phase. The results of these models have been analysed mathematically to describe the influence of surfactant, polymer and nanoparticle addition in nanoemulsion systems. In this study, statistical methods for the analysis of droplet size profile data obtained from the analysis of scattering (monochromatic) light intensity versus hydrodynamic size plots employ three different models: normal (Gaussian) distribution, lognormal (Galton) distribution, and Cauchy-Lorentz (Breit-Wigner or Lorentzian) distribution. The probability density function of normal distribution, with every variable modeled as a cumulative sum of small and independent variables, may be expressed in Eq. (2). I = 𝐼0 +

𝐴 𝑤

2

𝜋 √(2)

𝑒 ―2(𝑑ℎ ― 𝑑𝑐)/𝑤

(2)

Log-normal distribution function, describing the normal distribution of logarithm of variables obtained from the analysis of droplet profiles within a defined size range, is shown in Eq. (3). 𝐴

I = 𝐼0 + 𝑤.𝑑ℎ √(2𝜋)𝑒

― [ln

2

( )] /2𝑤 𝑑ℎ

2

𝑑𝑐

(3)

The Cauchy-Lorentz distribution, commonly known as the Lorentzian profile, defines an infinitely divisible likelihood distribution in which the dispersed oil droplets is assumed to


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never reach a finite range on either side. The probability density function corresponding to this distribution model is shown in Eq. (4). I = 𝐼0 +

2𝐴

𝑤

(4)

𝜋 .4(𝑑ℎ ― 𝑑𝑐)2 + 𝑤2

In the above equations, w is the standard deviation, A is the area under the normalized droplet size distribution curve, Io is the offset intensity value and dc is the average droplet diameter (or location parameter). R2 is the coefficient of determination and determines the how well experimental data are replicated by the model. It is observed that the size distribution profile area is observed to similar for both normal and log-normal functions due to the normalization of data-sets within a finite range. On the contrary, the Cauchy-Lorentz distribution encompasses infinite convergence of droplet size and the measured area is, hence, found to significantly higher than those obtained from normal and log-normal distribution studies. The value of dc signifies the peak position and depicts the maximum intensity corresponding to oil droplet diameter as per the conditions and functions of the said model. The value of standard deviation (w) roughly predicts the range of droplet sizes (starting from lowest size upto the highest droplet size). As expected, w increases drastically by the addition of polymer in surfactant-stabilized nanoemulsions due to increased droplet size. Thereafter, nanoparticles act by adsorbing onto the interface, causing closer packing of the coating layer around the oil droplet, as evident from reduced w values. Though all distribution analyses show good fitting of experimental data, each function is found to statistically suit a specific nanoemulsion system. For instance, surfactant-stabilized nanoemulsion system is best fitted with the help of log-normal (or Galton) probability distribution, whereas surfactant + polymer based nanoemulsion is effectively modelled using normal (or Gaussian) distribution function. For surfactant-based nanoemulsion, the adjusted coefficient of determination (R2) value is calculated as 0.990 in case of log-normal distribution, as compared to R2 values of 0.972 and 0.959 for Gaussian and Cauchy-Lorentz distribution functions. This result may be attributed to nearly asymmetrical distribution of oil droplets within nanoemulsion phase, as corroborated from visual analysis of Cryo-TEM imaging as observed in Fig. 3(a). In the presence of surfactant and polymer, the normal or bell-curve distribution shows best fitting of DLS data with R2 value of 0.982 due to the formation of welldefined network structure with polymer + surfactant barrier encapsulating the oil droplets dispersed with the continuous aqueous phase. This statistical fit is corroborated from symmetrical distribution of stabilized oil droplets, as evidenced from Fig. 3(b) for surfactant-


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polymer stabilized nanoemulsion system. The respective values of R2 for surfactant-polymernanoparticle (SPN) emulsions are found to be 0.959, 0.965 and 0.976 for normal, log-normal and Cauchy-Lorentz models. The Cauchy-Lorentz distribution function proves to be comparatively better than other functions in fitting intensity-weighted size dataset values. This behavior is attributed possibly due to the addition of surface-active silica (SiO2) nanoparticles in the system, which differently adsorb onto the oil-aqueous interfaces (Refer to Fig. 3(c)). The arrangement as well as relative efficacy of nanoparticles at the electrostatic/steric barrier depends on interfacial energy. Consequently, a continuous probability distribution such as Cauchy-Lorentz type, wherein the oil-aqueous interface is dynamic and the average size of oil droplet(s) never converges to a finite value, may be ideal in understanding size distribution in surfactant-polymer-nanoparticle (SPN) systems. However, it is pertinent that the data set in each system was modelled successfully with each distribution model, irrespective of their distinctly varying density functions. The fitting parameters obtained from the analysis of droplet size distribution profiles by normal, log-normal and Cauchy-Lorentz distributions are shown in Table 2. Table 2. Comparison of fitting parameter values obtained using different probability distribution functions for analysis of intensity versus hydrodynamic size plots for nanoemulsion systems. Composition of

Fitting

Probability distribution function

Nanoemulsion

Parameters

system (o/w = 1:9)

Normal

Log-Normal

Cauchy-Lorentz

(Gaussian)

(Galton)

(Breit-Wigner)

0.100% 14-6-14 GS

Std. deviation (w)

7.408

0.322

9.903

stabilized

Area (A)

2.213

2.392

4.611

nanoemulsion

Offset (Io)

-0.017

-0.684

-5.199

Center (dc)

12.278

12.654

12.250

0.972

0.990

0.959

Adj

R2

0.100% 14-6-14 GS

Std. deviation (w)

92.144

0.189

135.782

+ 0.050% PHPA

Area (A)

35.073

33.337

85.045

stabilized

Offset (Io)

-1.502

-0.449

-10.756

nanoemulsion

Center (dc)

238.818

241.817

239.454

0.982

0.944

0.976

Adj

R2

0.100% 14-6-14 GS

Std. deviation (w)

37.102

0.136

45.457

+ 0.050% PHPA +

Area (A)

15.717

15.814

29.059

0.050% SiO2

Offset (Io)

1.00622

1.507

-4.000

stabilized

Center (dc)

137.139

138.134

136.901

nanoemulsion

Adj R2

0.959

0.965

0.976


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Theoretical investigations on Oil Droplet behavior Nanoemulsion formulations are characterized by the balance between two reverse processes: droplet breakdown and recoalescence. These processes are responsible for controlling droplet deformation, promoted by significantly high shear conditions during ultrasonication method. For (macro) emulsion systems to exhibit reduction in droplet size (< 500 nm) and form nanoemulsion systems, a requisite condition is that the rate of droplet breakdown must be greater than that of droplet recoalescence. This occurs when the applied shear exceeds the interfacial energy (Laplacian energy) of the emulsion droplets. In this section, the oil droplet deformation process is investigated for surfactant (only) nanoemulsions, surfactant + polymer nanoemulsions and surfactant + polymer + nanoparticle based nanoemulsions.13,17,34,52 Therefore, the classical form of Taylor equation as well as Hinze’s work has been modified and improved to account for the turbulent flow generated in case of nanoemulsion synthesis by ultrasonication process. In 1932, Taylor performed experimental studies on a single emulsion droplet under the effect of laminar flow field.19 As per Taylor’s findings, a droplet remains in stable condition without breaking unless the applied shear stress exceeds the interfacial stress responsible for holding the drop in the same shape.19,20 Therefore, a critical capillary number (Cacrit,d) exists below which the droplet should not break. Therefore, the classical form of Taylor’s theory can be expressed using the Eq. (5) as: Cacrit,d =

τ𝑎𝑝𝑝𝑙𝑖𝑒𝑑

(5)

(𝜎/𝑑)

where, 𝜏applied is the applied shear stress, σ is the interfacial tension existing at the oil-aqueous interface and d is the average droplet size. The above relation describes Taylor’s criterion to achieve droplet breakup. The applied stress in laminar field may be defined as 𝜏applied ~ µcγ̇, where µc is the viscosity of continuous phase and γ̇ is the value of shear rate in continuous phase. Therefore, the droplet size can be predicted using Eq. (6):

[ ]

𝑑 = Cacrit,d

𝜎

(6)

µcγ̇

Though Taylor’s theory provides an ideal, generalized understanding of droplet deformation, it cannot be applied in the ultrasonication-assisted synthesis of nanoemulsions. This is because ultrasonication results in turbulent flow, a condition that is not postulated by Taylor’s theory. To overcome this drawback, an improved form of this theory was developed by Hinze for predicting droplet size in turbulent flow systems.21,23,53-55 The Hinze theory states that two independent dimensionless numbers (or parameters) exist when a droplet of diameter d, density


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ρd, and viscosity µd, with interfacial tension between the two phases σ, is deformed by the surrounding outer phase with stress 𝜏applied.53-55 The (two) numbers that govern the non-laminar flow problem are the critical Weber number (Wecrit) and the Ohnesorge number (Oh). The Weber’s number is useful for analysing fluid behavior where interface(s) exist between two different phases and represents the ratio of the applied shear stress to the interfacial stress, as depicted in Eq. (7). 𝑊𝑒𝑐𝑟𝑖𝑡 = τ𝑎𝑝𝑝𝑙𝑖𝑒𝑑.𝑑 𝜎

(7)

The Ohnesorge number (Oh), introduced by Wolfgang von Ohnesorge, is the ratio of viscocapillary time scale to Rayleigh breakup time scale. This dimensionless number relates viscous forces to inertial and interfacial tension forces, and is mathematically depicted in the form (8): 𝑂ℎ = µ𝑑 √(ρ𝑑.σ.d)

(8)

A large Wecrit means that the applied shear stress responsible for droplet deformation dominates over the interfacial stress responsible for droplet stability. A high Oh value signifies that the localized viscous stresses exceed the inertial and interfacial stresses in droplet breakup dynamics.14,23 Oh is related to Weber number (We) and Reynolds number (Re) of the droplet through the equation 𝑂ℎ = 𝑊𝑒0.5 𝑅𝑒 In this equation, the Reynolds number of the droplet (Re) is obtained by the Eq. (9). (9)

𝑅𝑒 = (ρ𝑑. V𝑑.𝑑)/µ𝑑

Where, Vd refers to the average velocity of dispersed oil droplets with respect to the continuous aqueous phase, formulated as Vd ~ (𝜏applied / ρd)0.5. Classically, Hinze proposed a functional relation (10) to determine the critical Weber number below which the droplet will not break by modified definition as a function of Ohnesorge number: 𝑊𝑒𝑐𝑟𝑖𝑡 = 𝐶𝑎𝑡𝑐𝑟𝑖𝑡,𝑑[1 + f (Oh)]

(10)

In this equation, Catcrit,d is a constant (similar to Cacrit,d in Taylor’s approach) and f(Oh) is a function of Ohnesorge number, indicating the contribution of viscosity effects of the droplet. The addition of polymer as well as nanoparticle has significant effects on the viscosity of the surfactant-stabilized nanoemulsions, thereby affecting the values of We and Oh, the two most


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important parameters important for droplet breakup analysis. Originally, Hinze’s work was proposed for macroemulsion systems with large oil droplet diameter (d ~ 100 µm) where Oh (surfactant + polymer + nanoparticle) >


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(surfactant + polymer). The condition for droplet breakdown to occur is described in Eq. (12) as follows: 𝑊𝑒𝑐𝑟𝑖𝑡,𝑎 =

ρ𝑑𝑉𝑎2𝑎

(12)

σ

Fig. 5. Physical model for nanoemulsion synthesis. (a) A binary fission process is proposed, in which a filament extrudes out of the parent emulsion droplet. (b) Emulsifier molecules such as surfactant, polymer and/or nanoparticles aid in droplet stabilization by encapsulating droplet surface.

In this relation, Wecrit,a is the Weber number based on the extruding filament (and not on the parent droplet), Va is the velocity scale of the filament and a is the filament length. Obtaining Oh values > 1, viscous forces are found to be comparable to inertial and interfacial forces, which is indicative of nanoemulsion stability. This is also an underlying concept that contributes to the fact that nanoemulsions are observed to be kinetically stable, and/or exhibit interfacial/dimensional stability over a considerable period in comparison to unstable macroemulsion systems. Thus, the influence of droplet viscosity is significant in nanoemulsions resulting in filament length, a ~ dRed-0.5, and velocity, ua ~ udRed0.5.14,35,56 Thus, reverting the above equation (12) to Hinze’s theory and substituting suitable parameters, the final droplet size may be employed to achieve the correlation (13). 𝑊𝑒𝑐𝑟𝑖𝑡,𝑑𝑂ℎ ―0.4 = τ𝑎𝑝𝑝𝑙𝑖𝑒𝑑.𝑑 𝜎

[

µ𝑑

]

―0.4

√(ρ𝑑σ.d))


(13)

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The above equation states that an effective Weber number exists (Weeff = Wecrit,d Oh-0.4) for each nanoemulsion system, depending on the composition, and interactions between the droplet and continuous phase is pivotal in droplet size profile distribution studies. Fig. 6 shows the Wecrit,d versus Oh plots with a two-parameter power law model with flow behavior index of nearly ~0.4. Model analysis showed that Wecrit,d is nearly proportional to the two-fifth power of Ohnesorge number (Oh) value, determined using the modified version of classical Hinze’s theory. The coefficient of determination (R2) values were calculated as 0.985, 0.989 and 0.973, which fits the experimental data well. Therefore, nanoemulsion synthesis by high-energy (ultrasonication) process lies in the favorable Weber number (We)-to-Ohnesorge number (Oh) regime. Similarities in the predicted and obtained values of exponential factor employed in the Power law fit prove that the dimensionless analysis approach is indeed useful in explaining droplet size data obtained from experimental studies. However, there is some scattering of data around the model fit values, which occur due to uncertainties (or errors) involved due to the complexity and dynamic processes involved in ultrasonication. Therefore, the experimental data showed good fitting results with the proposed scaling model of Wecrit,d ∝ Oh0.4 to predict nanoemulsion droplet behavior, based on the principle of parent droplet/filament breakdown. This scaling relation fits over a wide range of experimental data for completely different nanoemulsion systems investigated in this paper. Furthermore, the potency of the modified Hinze theory suggests its suitability as an effective guiding tool to determine critical parameters and enable rational formulation design of nanoemulsion systems. 0.19

(a)

0.18

Weber number (Wecrit,d)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.17

0.16

0.15

Wecrit,d = 0.024 Oh0.398 0.14

0.13

0.12 60

70

80

90

100

110

120

130

Ohnesorge number (Oh)


140

150

160

Page 23 of 32

0.032

(b)

0.031


0.030 0.029 0.028 0.027 0.026

Wecrit,d = 0.018 Oh0.389

0.025 0.024 0.023 2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8

4.0


0.32 0.31 0.30


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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(c)

0.29 0.28 0.27 0.26

Wecrit,d = 0.088 Oh0.421

0.25 0.24 0.23 0.22 10

11

12

13

14

15

16

17

18

19

20


Fig. 6. Validation of proposed scaling relation Wecrit,d ∝ Oh0.4 for a wide range of We and Oh values, expressed in terms of dimensions of parent droplet: (a) 14-6-14 GS nanoemulsions; (b) 14-6-14 GS + PHPA polymer nanoemulsions; (c) 14-6-14 GS + PHPA + SiO2 nanoemulsion systems.

Zeta potential and kinetic stability studies A schematic explaining the variations in nanoemulsion (oil) droplet profiles in the presence of different emulsifier compositions and molecular interactions responsible for droplet stabilization are illustrated in Figs. 7(a), 7(b) and 7(c). Zeta potential values for 14-6-14 surfactant-stabilized systems are initially measured as +42.7 mV, showing the influence of electrostatic (repulsive) interactions between the positively charged (cationic surfactant coating) oil droplet surfaces. The addition of PHPA aids in polymer-induced encapsulation of surfactant-stabilized oil droplets. In this type of nanoemulsion, a second factor known as steric hindrance comes into play, which tends to overcome the electrostatic interactions and form a network cluster of dispersed oil droplets.13,14,27 This results in reduced surface charge existing


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between (polymer-coated) nanoemulsion droplets. However, reduced zeta potential (+5.6 mV) obtained in 14-6-14 GS + PHPA based systems does not necessarily indicate reduced stability due to the dominant steric hindrance effect. Zeta potential of nanoemulsion (oil) droplets further reduce to values as low as -30.2 mV in the presence of negatively charged SiO2 nanoparticles, showing that the electrostatic repulsive forces increase drastically and exceed the influence of steric bulk of PHPA polymer molecules. Fig. 7(d) shows the zeta potential versus time-stamp graphs for nanoemulsions stabilized in the presence of surfactant (dimer), surfactant + polymer and surfactant + polymer + nanoparticle. Time-dependent zeta potentiometric investigations show that surface charge (zeta potential) gradually decrease in magnitude with passage of time. This is attributed to eventual coalescence of stabilized oil droplets and subsequent reduction in available oil-aqueous interfacial area within the continuous immiscible phase, which reduces nanoemulsion stability. Zeta potential results corroborate with the findings of the proposed Hinze theoretical approach, wherein Weber number as well as Ohnesorge number values for nanoemulsions containing 146-14 GS (We = 0.132-0.179; Oh = 73.465-154.950) and 14-6-14 GS + PHPA + SiO2 (We = 0.239-0.305; Oh = 10.213-19.125) are higher in comparison to those obtained (We = 0.0310.024; Oh = 2.260-3.911) in case of 14-6-14 + PHPA systems. It is clear that lowest magnitudes of We are obtained in 14-6-14 GS + PHPA nanoemulsions, showing that the interfacial stress holding the polymer-surfactant coated droplet is much higher; and hence requires significant applied stress to cause droplet breakdown. Therefore, surfactant-polymer systems are characterized by bigger oil droplets and reduced tendency for droplet breakdown, exhibiting least stability in comparison to other nanoemulsion formulations. Weber number is found to greater in case of case of surfactant-polymer-nanoparticle systems. This shows that droplet breakdown in GS+PHPA+SiO2-stabilized oil droplets is primarily due to the dominance of inertial applied stress over interfacial stress. In this case, a smaller value of Oh exists that changes the translational Weber number values to some extent, accounting for the improved kinetic stability of prepared nanoemulsions.55,56 Relatively similar Weber numbers for GSstabilized droplet dispersions show a similar particle deformation effect in the presence of surfactant only. However, Ohnesorge number is larger for surfactant-stabilized nanoemulsions, which shows increased influence of droplet viscous stresses (inside the droplet). As a result, the oil droplets are non-uniformly distributed in the continuous emulsion phase, causing greater propensity for aggregate formation and reduced kinetic stability, as confirmed from zeta potential measurements.1,14,23,42,56,57


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Page 25 of 32

60

(d)

50

14-6-14 GS 14-6-14 GS + PHPA 14-6-14 GS + PHPA + SiO2

40

Zeta potential (mV)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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30 20 10 0 0 -10

24

48

72

96

120

144

168

Time (hr)

-20 -30 -40 -50

Fig. 7. Illustration of nanoemulsion droplet stabilization mechanisms using zeta potential (droplet surface charge) values and an analysis of the relative effects of electrostatic repulsive interactions and steric hindrance. (a), (b), (c) represent droplet profiles in different nanoemulsion compositions. (d) Zeta potential for 14-6-14 GS, 14-6-14 GS + PHPA and 14-6-14 GS + PHPA + SiO2 nanoemulsion droplet surfaces measured as a function of time elapsed (in hours).


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Based on the findings of this manuscript, we remark that oil droplet behavior in oil-in-water nanoemulsion systems is the result of complex interactions among different stabilizing constituents (surfactant, polymer, and nanoparticle). These interactions simultaneously contribute toward droplet breakdown and recoalescence processes, which in turn affects the distribution of stabilized droplets. Fig. 8 shows a graphical model showing the distribution of 14-6-14 GS, PHPA polymer and SiO2 nanoparticles onto the oil droplet surface within the continuous phase. Surfactant molecules adsorb onto the oil-aqueous interface by decreasing the IFT between oil and aqueous phases. PHPA polymer chains entangle with one another and aid in the formation of a network-like interconnected assembly of dispersed oil droplets. This improves the steric hindrance between neighbouring oil droplets, improving nanoemulsion stability. Nanoparticle addition prevents coalescence of dispersed oil droplets by accommodating onto the available “smaller” adsorption sites, and strengthening the mechanical (repulsion) barrier at the oil-aqueous interfaces. Hence, surfactant, polymer and/or nanoparticle addition affects oil droplet behavior by controlling electrostatic and/or steric interactions between participating molecules, and plays a significant role in understanding nanoemulsion stability.

Fig. 8. Distribution of 14-6-14 surfactant; PHPA polymer; and SiO2 nanoparticle onto oil droplet surface dispersed within continuous aqueous phase in nanoemulsion systems.


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Conclusions Nanoemulsions have gained increasing interest over the past few years owing to their exceptional properties in terms of translucent/transparent physical appearance, stability and interfacial behavior. The present work aimed at investigating the synthesis of surfactantpolymer-nanoparticle stabilized nanoemulsions by ultrasonication method and subsequent analyses of oil droplet behavior in terms of size, distribution and stability. Average diameter of oil droplet was determined as an exponential decay function of ultrasonication time; and governed by the efficiency of cavitation process responsible for generating high-localized shear rates. Droplet morphology was described as the encapsulation of (dispersed) oil droplet with emulsifier molecules (14-6-14 GS/PHPA/SiO2). Cryo-TEM imaging identified dispersed oil droplets as dark spots dispersed (4.2-25.4 nm) within continuous aqueous phase (light background). On PHPA addition, this changed into a network-like structure comprising larger oil droplets (125.9-358.8 nm). Presence of SiO2 nanoparticles (viewed as shiny particles deposited onto droplet surface) resulted in better emulsifier adsorption with size profile in the range 80.2-198.4 nm due to availability of “vacant sites” at the oil-aqueous interface. Droplet size profiles obtained by DLS technique were modelled with good fitting results using normal, log-normal and Cauchy-Lorentz probability functions. Theoretical investigations pertaining to oil droplet behavior in nanoemulsion systems favoured a modified Hinze approach to explain nanoemulsion droplet size and behavior. As per this model, a binary fission process was proposed, in which a filament droplet extrudes out of a parent oil drop under high shear conditions. Oil droplet breakdown was characterized with the help of a scaling relation Wecrit,d ∝ Oh0.4, encompassing two dimensionless entities known as critical Weber number (Wecrit,d) and Ohnesorge number (Oh). Zeta potentiometric results proved that nanoemulsion stability cannot be ideally described by electrostatic barrier effect only, but also required the incorporation of bulk steric hindrance effect. Combined with the modified Hinze theory approach, desired conditions of high Wecrit,d and low Oh values should be leveraged for droplet stabilization. Therefore, droplet stability is primarily attributed to the dominance of inertial stress over interfacial stress (synergistic influence) and degree of localized viscous stresses within oil droplet (anergistic effect). The authors feel that this paper should allow for a promising rationale and formulation strategy for nanoemulsion research.


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Acknowledgements The authors of this article acknowledge the financial support under IIT(ISM)-ONGC MoC project grant for Feasibility of using Nanoparticles for Enhanced Oil Recovery in Fields of ONGC (Ref. No. IRS/CFL/IIT(ISM)/LOA/2018-19. The study was supported by Institute of Reservoir Studies (IRS), ONGC Limited, Ahmedabad, India. References 1. Gupta, A.; Eral, H. B.; Hattona, T. A.; Doyle, P. S. Nanoemulsions: formation, properties and applications. Soft Matter 2016, 12, 2826-2841. DOI: 10.1039/C5SM02958A. 2. Bernardi, D.S.; Pereira, T.A.; Maciel, N.R.; Bortoloto, J.; Viera, G.S.; Oliveira, G.C.; RochaFilho, P.A. Formation and stability of oil-in-water nanoemulsions containing rice bran oil: in vitro and in vivo assessments. J. Nanobiotechnol. 2011, 9, 44. DOI: 10.1186/1477-3155-9-44. 3. Shin, K.; Kim, J. W.; Park, H.; Choi, H. S.; Chae, P. S.; Nam Y. S.; Kim, J. W. Fabrication and stabilization of nanoscale emulsions by formation of a thin polymer membrane at the oil–water interface, RSC Adv. 2015, 5, 46276-46281. DOI: 10.1039/C5RA03872C. 4. Ostróżka-Cieślik, A.; Sarecka-Hujar, B. Chapter 7 - The Use of Nanotechnology in Modern Pharmacotherapy, Editor(s): Alexandru Mihai Grumezescu, Multifunctional Systems for Combined Delivery. Biosens. Diagn. 2017, 139-158. DOI: 10.1016/B978-0-323-527255.00007-1. 5. Singh, Y.; Meher, J. G.; Raval, K.; Khan, F. A.; Chaurasia, M.; Jain, N. K.; Chourasia, M. K. Nanoemulsion: Concepts, development and applications in drug delivery. J. Controlled Release 2017, 252, 28-49. DOI: 10.1016/j.jconrel.2017.03.008. 6. Eral, H. B.; O’Mahony, M.; Shaw, R.; Trout, B. L.; Myerson, A. S.; Doyle, P. S. Composite Hydrogels Laden with Crystalline Active Pharmaceutical Ingredients of Controlled Size and Loading. Chem. Mater. 2014, 26, 6213−6220. DOI: 10.1021/cm502834h. 7. Badruddoza, A. Z. M.; Godfrin, P. D.; Myerson, A. S.; Trout, B. L.; Doyle, P. S. Core−Shell Composite Hydrogels for Controlled Nanocrystal Formation and Release of Hydrophobic Active Pharmaceutical Ingredients. Adv. Healthcare Mater. 2016, 5, 1960−1968. DOI: 10.1002/adhm.201600266. 8. Sonneville-Aubrun, O.; Yukuyama, M. N.; Pizzino, A. Chapter 14 - Application of Nanoemulsions in Cosmetics, Editor(s): Seid Mahdi Jafari, David Julian McClements, Nanoemulsions. Academic Press, 2018, 435-475. DOI: 10.1016/B978-0-12-811838-2.00014X. 9. Wooster, T. J.; Moorea, S. C.; Chen, W.; Andrews, H.; Addepalli, R.; Seymour, R. B.; Osborne, S. A. Biological fate of food nanoemulsions and the nutrients they carry – internalisation, transport and cytotoxicity of edible nanoemulsions in Caco-2 intestinal cells. RSC Adv. 2017, 7, 40053-40066. DOI: 10.1039/C7RA07804H. 10. Li, Y.; Zheng, J.; Xiao, H.; McClements, D. J. Nanoemulsion-based delivery systems for poorly water-soluble bioactive compounds: Influence of formulation parameters on Polymethoxyflavone crystallization. Food Hydrocoll. 2012, 27, 517–528. DOI: 10.1016/j.foodhyd.2011.08.017. 11. Gaudio, L. D.; Pratesi, C. R.; Belloni, A.; Moroni, I. Process for the recovery of heavy oil from an underground reservoir. US Pat. App. 2010, 13/503,033. 12. Sun, X.; Zhang, Y.; Chem, G.; Gai, Z. Application of Nanoparticles in Enhanced Oil Recovery: A Critical Review of Recent Progress. Energies 2017, 10, 345. DOI: 10.3390/en10030345.


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Nanoemulsion Droplet Characterization by Physicochemical and Theoretical Studies


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Stabilization of Dispersed Oil Droplets in Nanoemulsions by

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