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Optimization of nano-emulsions prepared by low-energy emulsification methods at constant temperature using a factorial design study
Colloids and Surfaces A: Physicochem. Eng. Aspects 288 (2006) 144–150
Optimization of nano-emulsions prepared by low-energy emulsification methods at constant temperature using a factorial design study C.M. Pey a , A. Maestro a , I. Sol´e a,b , C. Gonz´alez a , C. Solans b , J.M. Guti´errez a,∗ a
b
Departament d’Enginyeria Qu´ımica, Universitat de Barcelona, Mart´ı i Franqu´es 1, 08028 Barcelona, Spain Departament Tecnologia de Tensioactius, Institut d’Investigacions Qu´ımiques i Ambientals de Barcelona (IIQAB), Consejo Superior de Investigaciones Cient´ıficas (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain Received 3 October 2005; received in revised form 3 February 2006; accepted 10 February 2006 Available online 6 March 2006
Abstract The aim of this work is the study and optimization of composition and preparation method of nano-emulsions O/W by addition of one of the components at constant temperature. Experimental design techniques have been used to carry out this study. A factorial design has been done in order to investigate the effect of formulation and preparation variables over emulsion properties. The conclusion of this study is that emulsion droplet size and polydispersity change with composition and preparation method. These variables have been optimized using a central composite design obtaining response surfaces that describe this preparation method of nano-emulsions. © 2006 Elsevier B.V. All rights reserved. Keywords: Nano-emulsions; Emulsification method; O/W emulsion; Full factorial design; Response surface methodology
1. Introduction Nano-emulsions, also referred to in the literature as miniemulsions [1–3], ultrafine emulsions [4,5], emulsoids [6,7], unstable microemulsions [8], submicrometer emulsions [9,10], . . ., are a class of emulsions with very small and uniform droplet size, typically in the range of 20–500 nm. Due to their small droplet size, they may appear transparent or translucent, resembling microemulsions. However, in contrast to microemulsions, they are not thermodynamically stable, i.e. they are not equilibrium phases and the size of the droplets tends to increase with time, before phase separation. Nevertheless, the small droplet size makes them stable for a long time against sedimentation and creaming, hence offering increased stability. The characteristic properties of nano-emulsions are interesting for practical applications. Nano-emulsions are used in cosmetics as personal-care formulations [11–13], in agrochemicals for pesticide delivery [14,15], in chemical industry for the preparation of latex particles [16–18], etc. They can be used in
∗
Corresponding author. Tel.: +93 402 12 92; fax: +93 402 12 91. E-mail address: [email protected] (J.M. Guti´errez).
0927-7757/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfa.2006.02.026
pharmaceutical field as drug delivery systems [19–21] for parenteral, oral, ocular or transdermal administration. A direct consequence of the thermodynamic instability of nano-emulsions is the dependence of their properties on the preparation method. Nano-emulsions can be achieved either by high-energy emulsification methods (e.g., high-pressure homogenization) or by low-energy emulsification methods which employ the physicochemical properties of the system [22]. These methods make use of changing the spontaneous curvature of surfactant with temperature for nonionic surfactants called phase inversion temperature (PIT) method and introduced by Shinoda and Saito [23] or with the variation of the volume fraction of one of the components, emulsion inversion point (EIP) method [24]. In recent years, some studies have been carried out on the mechanism of formation of nano-emulsions by low-energy emulsification methods [25–30]. These studies show the relationship between nano-emulsions properties and system components, some aspects of preparation method and influence of phases which are present during the phase inversion. In the case of O/W nano-emulsions, the main requirement for the formation of bluish transparent or translucent emulsion is the presence of bicontinuous microemulsion or lamellar liquid crystalline phase during the emulsification process where a complete solubiliza-
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tion of the oil phase exists [28,29]. Depending on the preparation method, different droplet size distributions might be achieved, explaining why the route of preparation can have an influence on the emulsion stability. However, despite the formation mechanism and the effect of different variables on the nano-emulsions properties prepared by low-energy emulsification methods have been studied before, there are few systematic studies dealing with the effect of formation and composition variables and, in these, all of the variables are held constant during test runs except the one being studied, following the traditional method of experimentation evaluating only one variable (or factor) at time. This type of experiment reveals the effect of the chosen variable under set conditions assuming that variables are independent and that the effect will be the same at another level of the remaining variables. It does not show what would happen if other variables are also changed. In these cases, experimental design is an effective and efficient optimization strategy to overcome these drawbacks which has found widespread application in all branches. Some basic statistical methods are also developed to use in different techniques of experimental design. Experimental design allows to estimate the effects of several variables simultaneously. The significant factors affecting the nano-emulsion properties can be deduced, in a screening study, by applying a full factorial design, a powerful tool commonly used in exploratory studies. Response surface methodology (RSM), which consists in a group of mathematical and statistical techniques, is useful in the modeling and analysis of processes in which a response of interests, such as droplet size, is simultaneously influenced by several significant variables [31–33]. The aim of this work was to study by using experimental design methodology the influence of different variables and to evaluate simultaneous effect of more significant variables on droplet size of O/W nano-emulsions of system water–Tween20/Span20–liquid paraffin prepared by the stepwise addition of one component to the others at constant temperature. The experimental conditions selected were then applied in order to obtain a minimum droplet diameter. 2. Experimental 2.1. Materials Sorbitan monolaurate (Span20 (S20), NHLB = 8.6), and polyoxyethylene sorbitan monolaurate (Tween20 (T20), NHLB = 16.7), technical grade surfactants, were purchased from Sigma-Aldrich. Liquid paraffin was obtained from Merck KGaA, Darmstadt, Germany. It was a mixture of C18 to C40 linear chains, with a density δ ≈ 0.85 g/ml. All products were used without further purification. Water was desionized and further purified by Milli-Q filtration.
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equilibrate during a time that depended on the viscosity of the phases present (from a few hours to several weeks). Compositions are expressed in wt.% ratio between components and also as w/w ratio. The boundary lines drawn on the phase diagram lie equidistant between consecutive experimental measurements on either side of the phase boundary. Anisotropic phases were identified visually under polarized light. 2.3. Emulsion formation Emulsions were prepared by continuous addition of water with a dosing pump at 25 ◦ C to the surfactant-oil mixture previously homogenized and continuously mixed with a helix mixer. The addition was carried out under controlled mixing and addition rates. The final water composition was always 70% w/w. 2.4. Droplet size and polydispersity Emulsion droplet size and polydispersity (intensity based size distributions) were measured by photon correlation spectroscopy (PCS) using Malvern Zetasizer ZS at 25 ◦ C. Samples were diluted with water for the measurements. 2.5. Software STATGRAPHICS (Statistical Institution Edition Version 4.1, Statistical Graphics Co., Rockville, MD, USA) was used. 2.6. Variable screening The influence of different variables and their interactions on emulsion properties was checked by using experimental design. In fast screening studies, linear or second order interaction models are common. Four variables were considered addition time, mixing rate, O/S ratio and Tween20/Span20 relation (%Tween20). The droplet size was chosen as the main response since it determines whether an emulsion is a nanoemulsion or not. To ascertain the individual effects of these variables on emulsion droplet size, a full factorial design for four variables at two levels was applied. This experimental design requires one experiment at all possible combinations between two levels of each variable considered. In general, the necessary number of experiments is 2k , where k is the number of factors. Different replicates were carried out in different days, preparing again each mixture from pure components, so a total 32 runs were performed. The experimental variables considered and the design matrix are shown in Table 1. All points shown represent the average of measurements done at same experimental conditions. The design matrix was generated and results evaluated by using Statgraphics V4.1 software.
2.2. Phase diagrams
2.7. Simultaneous effect of the selected variables on the emulsion properties
All components were weighted and mixed with a Heidolph REAX Top vibromixer. Then, samples were kept at 25 ◦ C to
The preparation method is controlled by different variables that can interact. One experimental approach is to apply the
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Table 1 Full factorial design matrix of screening experiments and mean droplet diameter measured
Table 3 Experimental field for a design matrix: variables and emulsion properties measured
Run
%Tween20
O/S
Addition time (min)
Agitation rate (rpm)
Droplet diameter (nm)
Run
Addition rate (ml/min)
Agitation rate (rpm)
Droplet diameter (nm)
Polydispersity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2
2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0
51.3 51.3 51.3 51.3 4.3 4.3 4.3 4.3 51.3 51.3 51.3 51.3 4.3 4.3 4.3 4.3
200 200 200 200 200 200 200 200 700 700 700 700 700 700 700 700
81.7 94.1 145.0 164.0 98.2 94.9 139.3 149.1 80.6 102.1 144.3 173.1 86.0 90.1 143.6 158.2
1 2 3 4 5 6 7 8 9 10 11
4.0 4.0 16.0 16.0 1.5 18.5 10.0 10.0 10.0 10.0 10.0
250 550 250 550 400 400 188 612 400 400 400
93.3 87.3 106.1 91.4 78.9 94.9 91.2 101.4 92.1 91.8 92.3
0.146 0.141 0.173 0.131 0.102 0.130 0.146 0.161 0.140 0.120 0.125
cated. Statgraphics software was used to obtain the combination of values that draw the surface response. 3. Results and discussion
response surface methodology (RSM) for modeling emulsion droplet size and polydispersity as a function of selected variables. Two different central composite design (CCD), with k = 2, were used in order to generate 11 treatment combinations, one with O/S ratio and Tween20/Span20 relation as independent variables holding constant preparation conditions, an another with mixing and addition rates as independent variables at a constant composition. In this case, five levels of each variable were studied. In the statistical model, Y denotes different emulsion properties and x1 and x2 the two independent variables studied. Tables 2 and 3 show the actual levels corresponding to the coded settings, the treatment combinations and responses of two central composite designs that have been done. This design is represented by a second-order polynomial regression model, Eq. (1), to generate contour plots: Y = b0 + b1 x1 + b2 x2 + b11 x12 + b22 x22 + b12 x1 x2 + ε.
(1)
The experiment at the centre of the experimental field was performed three times and some more runs have been also repliTable 2 Experimental field for a design matrix: variables and emulsion properties measured Run
%Tween20
O/S
Droplet diameter (nm)
Polydispersity
1 2 3 4 5 6 7 8 9 10 11
45.1 45.1 51.2 51.2 43.8 52.5 48.2 48.2 48.2 48.2 48.2
2.00 3.00 2.00 3.00 2.50 2.50 1.79 3.21 2.50 2.50 2.50
121.6 174.5 97.0 160.6 148.0 141.6 76.0 148.2 111.0 111.1 108.8
0.174 0.302 0.117 0.117 0.225 0.134 0.092 0.143 0.131 0.100 0.126
The application of different experimental design techniques allowed programming an experimental strategy to study and optimize the preparation method of O/W nano-emulsions of water–Tween20/Span20–liquid paraffin prepared by addition of one component at constant temperature. 3.1. Factor screening Some parameters were preliminary considered in order to define the experimental field. Variables such as oil and surfactants ratio (O/S) and Tween20/Span20 relation (%Tween20) were selected in order to obtain nano-emulsions stable enough to have time to measure their droplet size and polydisersity. Mixing rate and addition time were selected using the same criteria, taking into account that emulsion properties, as thermodynamic unstable systems, depend on preparation method. Once the variables and their experimental field were selected, the influence of variables and their interactions on droplet size was checked. Estimated effects and interactions can be seen in Table 4, which also shows the standard error corresponding to each effect. Effects and interactions are defined as the differences between the mean droplet sizes obtained at the low level and the mean droplet size obtained at the high level of each variable. It is necessary to determine which differences are significantly different from zero and which are due to the experimental error. A statistical method is used based in analysis of the variance (ANOVA) which determines the statistical signification of effects and interactions comparing the mean square with an estimation of experimental error. Raw data have been used to do the ANOVA analysis. In this case, five effects and interactions have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level (Table 5). Comparing the mean square against this estimation, only two factors appeared to be significant: O/S ratio and %Tween20, both
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Table 4 Effects corresponding to factors and interactions obtained by full factorial design Factor/interaction
Effect
Average A: %Tween20 B: O/S relation C: addition time D: agitation rate A×B A×C A×D B×C B×D C×D A×B×C A×B×D A×C×D B×C×D A×B×C×D
122.8 13.3 62.4 2.2 2.9 5.9 10.6 5.0 7.0 7.6 3.8 0.3 −1.3 2.0 −3.9 3.2
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.4 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8
Fig. 1. Droplet diameter function of addition time and mixing rate for two different compositions.
having a positive influence on the response, which was not completely clear in Table 1, especially for %Tween20. So, when doubts exist, this full factorial analysis is capable to clarify if one effect influences the response or not. The study also shows that there were significant interactions between some factors: %Tween20 and addition time (BC), O/S ratio and addition time (BD), O/S ratio and mixing rate (AC). Therefore, this analysis concludes that the rest of effects and interactions are due to experimental error. So preparation variables seemed to have no influence on emulsion properties. Instead of these results, significant differences are observed between emulsions prepared on different experimental conditions. However, this effect is dependent on the composition studied, as it is shown in Fig. 1. As an example, Fig. 1 shows that addition time, that has no influence according to ANOVA analysis, has a negative influence when working at 48.2% Tween and O/S 2:1 (a smaller droplet size is obtained). However, it has a positive influence when samples
with 51.2% Tween20 and O/S 3:1 (composition that gives less stable emulsions) are used, because if the addition time is very long these relatively unstable samples start to destabilize. Consequently, addition time have contrary effects depending on the composition of the samples, that are compensated in the ANOVA analysis, and it seems that it does not influence the properties of the nano-emulsions. This is a limitation of ANOVA, and results cannot be taken blindly, without being accurately analyzed. The results from this first step led to study and optimize firstly the formulation variables at constant preparation conditions. Then the study and optimization of preparation variables on emulsion properties has been done for one optimal composition. 3.2. Study and optimization of formulation variables A central composite design has been done in order to study and optimize the final composition of O/W emulsions for the system water–Tween20/Span20–liquid paraffin at constant preparation conditions and final continuous phase (400 rpm, 33 min,
Table 5 ANOVA on the data obtained using full factorial design Sum of squares
d.f.
A: %Tween20 B: O/S relation C: addition time D: agitation rate AB AC AD BC BD CD ABC ABD ACD BCD ABCD
924.2 20365.8 25.0 43.6 184.1 589.2 130.9 255.7 298.5 73.5 0.4 8.3 21.7 77.5 53.9
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Total error Total (correlation)
360.936 28182.5
9 24
Mean square
F-ratio
P-value
924.167 20365.8 25.0441 43.6182 184.051 589.212 130.925 255.707 298.479 73.4837 0.368767 8.26905 21.6635 77.5385 53.8878
23.04 507.82 0.62 1.09 4.59 14.69 3.26 6.38 7.44 1.83 0.01 0.21 0.54 1.93 1.34
0.001 0.000 0.450 0.324 0.061 0.004 0.104 0.033 0.023 0.209 0.926 0.661 0.481 0.198 0.276
40.104
R2 = 0.9872; R2 (ad) = 0.9658; standard error of estimation = 6.33277; mean absolute error = 2.18777; F-ratio: MS factor/MS error.
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Fig. 2. Response surface: droplet diameter as a function of formulation variables.
70%W). In this case, the range of %Tween and O/S has been slightly extended (Table 2). The results of this study show that experimental response as a function of O/S ratio and %Tween20 can be approximated by a quadratic equation, the term (O/S)2 having no significance. The crossed term (Tween20)(O/S), which was found no significant when experiments on Table 1 were analyzed, was found significant in this case, when range was extended. It indicates that results are only valid into the experimental range. The equation which describes droplet size in terms of the significant variables (i.e. effects with P-values lower than 0.05) is as follows: droplet diameter (nm) = a + b (%Tween20) + c O S + d (%Tween20)2 + e (Tween20) O S
(2)
Fig. 3. Droplet size as a function of %Tween20 for one OS ratio. Quadratic model.
Fig. 4. Droplet size as a function of OS ratio for one %Tween20. Quadratic model.
a = 5893.3 ± 1055.9; b = −231.6 ± 47.0; c = −176.8 ± 157.6; d = 2.3 ± 0.5; e = 4.7 ± 3.2. The R2 of this equation is 99.21%, indicating that the fitting is really good. Variances of every factor and their importance can be obtained by the F-test method showing only significant parameters at 95% of confidence level. Fig. 2 shows response surface developed by the model for O/S ratio and Tween20/Span20 relation. These graphs offer a visual means of understanding how factors influence the measurement system. In this case, Tween20/Span20 relation has a non-lineal behavior (a quadratic dependence) showing an optimal relation which minimizes the droplet size (Fig. 2). This optimal relation, i.e. the minimum is dependent on the O/S ratio due to the existence of a light interaction between these two variables. Fig. 3 shows an example of a cross sectional cut of the Fig. 2 where O/S ratio was maintained constant, and here the optimum can also be observed. Referring to O/S ratio, an almost lineal behavior was observed denoting that the smaller O/S ratio, the smaller diameter is obtained (Fig. 2). An example of a cross section of Fig. 2 where the %Tween20 was maintained constant can be seen in Fig. 4. This figure shows that points can be nearly described by a straight line. In order to validate the empiric model, some more experimental data were obtained and added to the Figs. 3 and 4. Results denote that the empiric model describes quite well the behaviour
of the system because most of experimental data are between the confidence levels of the model at 95%. Some studies of phase transitions during emulsification process have been done to explain these results. Fig. 5 shows an example of a partial phase diagram at constant %Tween20, where the phases that are crossed during emulsification process
Fig. 5. Phase diagram. Emulsification paths.
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for different emulsification paths at this relation have been determined. The diagram shows the presence of a two-phase region near the 0% water line, as these surfactants are insoluble in paraffin. When a small amount of water is added (around 2–3%) a water-in-oil microemulsion appears (Om ). Higher amounts of water cause the appearance of a multiphase region where liquid crystalline phases have been identified under polarized light. This region is wide and extends to relatively high concentrations of water at the highest concentrations of surfactants tested. When the O/S ratio is increased the multiphase area progressively narrows, and disappears at 3:1 O/S. When more water is added, a two-phase region (oil-in-water microemulsion Wm and free oil O), appears. Is into this zone, at 70% water, where nano-emulsions are formed. Best results (small droplet size and low polydispersity) were obtained when the multiphase area was present at higher percentage of water, i.e., at the highest concentrations of surfactants. If one tries to extend the O/S ratio further than 3:1, nano-emulsions cannot be formed, as any liquid crystal is not crossed along the emulsification path, and it seems to be necessary [22,29].
surface developed by the model for mixing and addition rate. Droplet size decreases when addition rate of water over the mixture of oil and surfactants is slow. Emulsions with small droplet size are obtained if, during the emulsification path, the equilibrium is reached in a zone with liquid crystal phases or bicontinuous phases, with as much of oil as possible solubilized in [22,29]. Then, when the nano-emulsion is formed, the oil is intimate mixed with the rest of components and it has just to be redistributed. However, the presence of liquid crystal phases confers more viscosity to the system due to the presence of an organized structure, and it means that a good mixture necessary for equilibrium requires enough time and high mixing rate. So, high mixing rates are useful in order to obtain these phases transitions. Otherwise, when the multiphase zone is crossed and we enter into the Wm + O zone, where emulsion droplets are formed, the viscosity of the system is drastically decreased. Then, if the mixing rate is too high, it could promote some destabilization mechanisms like coalescence, sedimentation, . . . resulting a higher final droplet size.
3.3. Study and optimization of the effect of preparation variables on emulsion properties
4. Conclusions
A central composite design has been done to study and optimize the preparation variables of emulsions O/W at a constant composition (48.2% Tween20, 2:1 O/S ratio). In this case, two preparation variables are studied: addition and mixing rate. For the experimental data (Table 3), regression technique was used to fit the parameters to a response surface. The Eq. (2) describes the droplet size in terms of preparation variables, only showing significant parameters (with P-values lower than 0.05) at 95% of confidence level: droplet diameter (nm) = a + b (addition rate) + c (mixing rate) + d (mixing rate)2
(3)
a = 117.922 ± 20.208; b = 0.823 ± 0.423; c = 0.150 ± −0.104; d = 1.54 × 10−4 ± 1.28 × 10−4 . The fitting of this equation is not very good (R2 = 80.99%). However, it is capable to qualitatively explain the dependence of droplet size on formation variables. Fig. 6 shows the response
Experimental design is a good tool to study and optimize nano-emulsion properties prepared by low-energy emulsification methods. Eq. (2) fits experimental results related to composition variables. A linear dependence of the droplet size with O/S was obtained, according to the fact that smaller droplet sizes were obtained when a higher amount of surfactant was present, since the multiphase zone with liquid crystal crossed during emulsification was extended to higher concentrations of water. A quadratic dependence of droplet size on %Tween20 was found, according to an optimal Tween20/Span20 relation (related to an optimal HLB number, NHLB ). Eq. (2) predicts a slight interaction between OS and %Tween, and it can be explained because the oil present preferentially solubilizes the Span20 and, as a result, changes the effective Tween20/Span20 relation. Although Eq. (3) does not fit results so well as Eq. (2), it qualitatively predicts experimental results, according to emulsification mechanism. A linear dependence of droplet size on addition rate was obtained, the best nano-emulsions found at the lowest addition rates. At lower addition rates the liquid crystalline phases with oil solubilized, necessary for a good emulsification, have more time to be appropriately formed. A quadratic dependence of droplet size on mixing rate was found, related to an optimal mixing rate. Small droplet size were, then, obtained when mixing rate was the adequate to obtain liquid crystalline phases without promoting some destabilization mechanisms when emulsion droplets were formed during the emulsification process. Acknowledgment
Fig. 6. Response surface: droplet diameter as a function of preparation variables.
This work was funded under Spanish MCYT Project No. PPQ2002-04514-C03-02.
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Optimization of nano-emulsions prepared by low-energy emulsification methods at constant temperature using a factorial design study C.M. Pey a , A. Maestro a , I. Sol´e a,b , C. Gonz´alez a , C. Solans b , J.M. Guti´errez a,∗ a
b
Departament d’Enginyeria Qu´ımica, Universitat de Barcelona, Mart´ı i Franqu´es 1, 08028 Barcelona, Spain Departament Tecnologia de Tensioactius, Institut d’Investigacions Qu´ımiques i Ambientals de Barcelona (IIQAB), Consejo Superior de Investigaciones Cient´ıficas (CSIC), Jordi Girona 18-26, 08034 Barcelona, Spain Received 3 October 2005; received in revised form 3 February 2006; accepted 10 February 2006 Available online 6 March 2006
Abstract The aim of this work is the study and optimization of composition and preparation method of nano-emulsions O/W by addition of one of the components at constant temperature. Experimental design techniques have been used to carry out this study. A factorial design has been done in order to investigate the effect of formulation and preparation variables over emulsion properties. The conclusion of this study is that emulsion droplet size and polydispersity change with composition and preparation method. These variables have been optimized using a central composite design obtaining response surfaces that describe this preparation method of nano-emulsions. © 2006 Elsevier B.V. All rights reserved. Keywords: Nano-emulsions; Emulsification method; O/W emulsion; Full factorial design; Response surface methodology
1. Introduction Nano-emulsions, also referred to in the literature as miniemulsions [1–3], ultrafine emulsions [4,5], emulsoids [6,7], unstable microemulsions [8], submicrometer emulsions [9,10], . . ., are a class of emulsions with very small and uniform droplet size, typically in the range of 20–500 nm. Due to their small droplet size, they may appear transparent or translucent, resembling microemulsions. However, in contrast to microemulsions, they are not thermodynamically stable, i.e. they are not equilibrium phases and the size of the droplets tends to increase with time, before phase separation. Nevertheless, the small droplet size makes them stable for a long time against sedimentation and creaming, hence offering increased stability. The characteristic properties of nano-emulsions are interesting for practical applications. Nano-emulsions are used in cosmetics as personal-care formulations [11–13], in agrochemicals for pesticide delivery [14,15], in chemical industry for the preparation of latex particles [16–18], etc. They can be used in
∗
Corresponding author. Tel.: +93 402 12 92; fax: +93 402 12 91. E-mail address: [email protected] (J.M. Guti´errez).
0927-7757/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfa.2006.02.026
pharmaceutical field as drug delivery systems [19–21] for parenteral, oral, ocular or transdermal administration. A direct consequence of the thermodynamic instability of nano-emulsions is the dependence of their properties on the preparation method. Nano-emulsions can be achieved either by high-energy emulsification methods (e.g., high-pressure homogenization) or by low-energy emulsification methods which employ the physicochemical properties of the system [22]. These methods make use of changing the spontaneous curvature of surfactant with temperature for nonionic surfactants called phase inversion temperature (PIT) method and introduced by Shinoda and Saito [23] or with the variation of the volume fraction of one of the components, emulsion inversion point (EIP) method [24]. In recent years, some studies have been carried out on the mechanism of formation of nano-emulsions by low-energy emulsification methods [25–30]. These studies show the relationship between nano-emulsions properties and system components, some aspects of preparation method and influence of phases which are present during the phase inversion. In the case of O/W nano-emulsions, the main requirement for the formation of bluish transparent or translucent emulsion is the presence of bicontinuous microemulsion or lamellar liquid crystalline phase during the emulsification process where a complete solubiliza-
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tion of the oil phase exists [28,29]. Depending on the preparation method, different droplet size distributions might be achieved, explaining why the route of preparation can have an influence on the emulsion stability. However, despite the formation mechanism and the effect of different variables on the nano-emulsions properties prepared by low-energy emulsification methods have been studied before, there are few systematic studies dealing with the effect of formation and composition variables and, in these, all of the variables are held constant during test runs except the one being studied, following the traditional method of experimentation evaluating only one variable (or factor) at time. This type of experiment reveals the effect of the chosen variable under set conditions assuming that variables are independent and that the effect will be the same at another level of the remaining variables. It does not show what would happen if other variables are also changed. In these cases, experimental design is an effective and efficient optimization strategy to overcome these drawbacks which has found widespread application in all branches. Some basic statistical methods are also developed to use in different techniques of experimental design. Experimental design allows to estimate the effects of several variables simultaneously. The significant factors affecting the nano-emulsion properties can be deduced, in a screening study, by applying a full factorial design, a powerful tool commonly used in exploratory studies. Response surface methodology (RSM), which consists in a group of mathematical and statistical techniques, is useful in the modeling and analysis of processes in which a response of interests, such as droplet size, is simultaneously influenced by several significant variables [31–33]. The aim of this work was to study by using experimental design methodology the influence of different variables and to evaluate simultaneous effect of more significant variables on droplet size of O/W nano-emulsions of system water–Tween20/Span20–liquid paraffin prepared by the stepwise addition of one component to the others at constant temperature. The experimental conditions selected were then applied in order to obtain a minimum droplet diameter. 2. Experimental 2.1. Materials Sorbitan monolaurate (Span20 (S20), NHLB = 8.6), and polyoxyethylene sorbitan monolaurate (Tween20 (T20), NHLB = 16.7), technical grade surfactants, were purchased from Sigma-Aldrich. Liquid paraffin was obtained from Merck KGaA, Darmstadt, Germany. It was a mixture of C18 to C40 linear chains, with a density δ ≈ 0.85 g/ml. All products were used without further purification. Water was desionized and further purified by Milli-Q filtration.
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equilibrate during a time that depended on the viscosity of the phases present (from a few hours to several weeks). Compositions are expressed in wt.% ratio between components and also as w/w ratio. The boundary lines drawn on the phase diagram lie equidistant between consecutive experimental measurements on either side of the phase boundary. Anisotropic phases were identified visually under polarized light. 2.3. Emulsion formation Emulsions were prepared by continuous addition of water with a dosing pump at 25 ◦ C to the surfactant-oil mixture previously homogenized and continuously mixed with a helix mixer. The addition was carried out under controlled mixing and addition rates. The final water composition was always 70% w/w. 2.4. Droplet size and polydispersity Emulsion droplet size and polydispersity (intensity based size distributions) were measured by photon correlation spectroscopy (PCS) using Malvern Zetasizer ZS at 25 ◦ C. Samples were diluted with water for the measurements. 2.5. Software STATGRAPHICS (Statistical Institution Edition Version 4.1, Statistical Graphics Co., Rockville, MD, USA) was used. 2.6. Variable screening The influence of different variables and their interactions on emulsion properties was checked by using experimental design. In fast screening studies, linear or second order interaction models are common. Four variables were considered addition time, mixing rate, O/S ratio and Tween20/Span20 relation (%Tween20). The droplet size was chosen as the main response since it determines whether an emulsion is a nanoemulsion or not. To ascertain the individual effects of these variables on emulsion droplet size, a full factorial design for four variables at two levels was applied. This experimental design requires one experiment at all possible combinations between two levels of each variable considered. In general, the necessary number of experiments is 2k , where k is the number of factors. Different replicates were carried out in different days, preparing again each mixture from pure components, so a total 32 runs were performed. The experimental variables considered and the design matrix are shown in Table 1. All points shown represent the average of measurements done at same experimental conditions. The design matrix was generated and results evaluated by using Statgraphics V4.1 software.
2.2. Phase diagrams
2.7. Simultaneous effect of the selected variables on the emulsion properties
All components were weighted and mixed with a Heidolph REAX Top vibromixer. Then, samples were kept at 25 ◦ C to
The preparation method is controlled by different variables that can interact. One experimental approach is to apply the
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Table 1 Full factorial design matrix of screening experiments and mean droplet diameter measured
Table 3 Experimental field for a design matrix: variables and emulsion properties measured
Run
%Tween20
O/S
Addition time (min)
Agitation rate (rpm)
Droplet diameter (nm)
Run
Addition rate (ml/min)
Agitation rate (rpm)
Droplet diameter (nm)
Polydispersity
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2 48.2 51.2
2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0 2.0 2.0 3.0 3.0
51.3 51.3 51.3 51.3 4.3 4.3 4.3 4.3 51.3 51.3 51.3 51.3 4.3 4.3 4.3 4.3
200 200 200 200 200 200 200 200 700 700 700 700 700 700 700 700
81.7 94.1 145.0 164.0 98.2 94.9 139.3 149.1 80.6 102.1 144.3 173.1 86.0 90.1 143.6 158.2
1 2 3 4 5 6 7 8 9 10 11
4.0 4.0 16.0 16.0 1.5 18.5 10.0 10.0 10.0 10.0 10.0
250 550 250 550 400 400 188 612 400 400 400
93.3 87.3 106.1 91.4 78.9 94.9 91.2 101.4 92.1 91.8 92.3
0.146 0.141 0.173 0.131 0.102 0.130 0.146 0.161 0.140 0.120 0.125
cated. Statgraphics software was used to obtain the combination of values that draw the surface response. 3. Results and discussion
response surface methodology (RSM) for modeling emulsion droplet size and polydispersity as a function of selected variables. Two different central composite design (CCD), with k = 2, were used in order to generate 11 treatment combinations, one with O/S ratio and Tween20/Span20 relation as independent variables holding constant preparation conditions, an another with mixing and addition rates as independent variables at a constant composition. In this case, five levels of each variable were studied. In the statistical model, Y denotes different emulsion properties and x1 and x2 the two independent variables studied. Tables 2 and 3 show the actual levels corresponding to the coded settings, the treatment combinations and responses of two central composite designs that have been done. This design is represented by a second-order polynomial regression model, Eq. (1), to generate contour plots: Y = b0 + b1 x1 + b2 x2 + b11 x12 + b22 x22 + b12 x1 x2 + ε.
(1)
The experiment at the centre of the experimental field was performed three times and some more runs have been also repliTable 2 Experimental field for a design matrix: variables and emulsion properties measured Run
%Tween20
O/S
Droplet diameter (nm)
Polydispersity
1 2 3 4 5 6 7 8 9 10 11
45.1 45.1 51.2 51.2 43.8 52.5 48.2 48.2 48.2 48.2 48.2
2.00 3.00 2.00 3.00 2.50 2.50 1.79 3.21 2.50 2.50 2.50
121.6 174.5 97.0 160.6 148.0 141.6 76.0 148.2 111.0 111.1 108.8
0.174 0.302 0.117 0.117 0.225 0.134 0.092 0.143 0.131 0.100 0.126
The application of different experimental design techniques allowed programming an experimental strategy to study and optimize the preparation method of O/W nano-emulsions of water–Tween20/Span20–liquid paraffin prepared by addition of one component at constant temperature. 3.1. Factor screening Some parameters were preliminary considered in order to define the experimental field. Variables such as oil and surfactants ratio (O/S) and Tween20/Span20 relation (%Tween20) were selected in order to obtain nano-emulsions stable enough to have time to measure their droplet size and polydisersity. Mixing rate and addition time were selected using the same criteria, taking into account that emulsion properties, as thermodynamic unstable systems, depend on preparation method. Once the variables and their experimental field were selected, the influence of variables and their interactions on droplet size was checked. Estimated effects and interactions can be seen in Table 4, which also shows the standard error corresponding to each effect. Effects and interactions are defined as the differences between the mean droplet sizes obtained at the low level and the mean droplet size obtained at the high level of each variable. It is necessary to determine which differences are significantly different from zero and which are due to the experimental error. A statistical method is used based in analysis of the variance (ANOVA) which determines the statistical signification of effects and interactions comparing the mean square with an estimation of experimental error. Raw data have been used to do the ANOVA analysis. In this case, five effects and interactions have P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level (Table 5). Comparing the mean square against this estimation, only two factors appeared to be significant: O/S ratio and %Tween20, both
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Table 4 Effects corresponding to factors and interactions obtained by full factorial design Factor/interaction
Effect
Average A: %Tween20 B: O/S relation C: addition time D: agitation rate A×B A×C A×D B×C B×D C×D A×B×C A×B×D A×C×D B×C×D A×B×C×D
122.8 13.3 62.4 2.2 2.9 5.9 10.6 5.0 7.0 7.6 3.8 0.3 −1.3 2.0 −3.9 3.2
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.4 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8
Fig. 1. Droplet diameter function of addition time and mixing rate for two different compositions.
having a positive influence on the response, which was not completely clear in Table 1, especially for %Tween20. So, when doubts exist, this full factorial analysis is capable to clarify if one effect influences the response or not. The study also shows that there were significant interactions between some factors: %Tween20 and addition time (BC), O/S ratio and addition time (BD), O/S ratio and mixing rate (AC). Therefore, this analysis concludes that the rest of effects and interactions are due to experimental error. So preparation variables seemed to have no influence on emulsion properties. Instead of these results, significant differences are observed between emulsions prepared on different experimental conditions. However, this effect is dependent on the composition studied, as it is shown in Fig. 1. As an example, Fig. 1 shows that addition time, that has no influence according to ANOVA analysis, has a negative influence when working at 48.2% Tween and O/S 2:1 (a smaller droplet size is obtained). However, it has a positive influence when samples
with 51.2% Tween20 and O/S 3:1 (composition that gives less stable emulsions) are used, because if the addition time is very long these relatively unstable samples start to destabilize. Consequently, addition time have contrary effects depending on the composition of the samples, that are compensated in the ANOVA analysis, and it seems that it does not influence the properties of the nano-emulsions. This is a limitation of ANOVA, and results cannot be taken blindly, without being accurately analyzed. The results from this first step led to study and optimize firstly the formulation variables at constant preparation conditions. Then the study and optimization of preparation variables on emulsion properties has been done for one optimal composition. 3.2. Study and optimization of formulation variables A central composite design has been done in order to study and optimize the final composition of O/W emulsions for the system water–Tween20/Span20–liquid paraffin at constant preparation conditions and final continuous phase (400 rpm, 33 min,
Table 5 ANOVA on the data obtained using full factorial design Sum of squares
d.f.
A: %Tween20 B: O/S relation C: addition time D: agitation rate AB AC AD BC BD CD ABC ABD ACD BCD ABCD
924.2 20365.8 25.0 43.6 184.1 589.2 130.9 255.7 298.5 73.5 0.4 8.3 21.7 77.5 53.9
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Total error Total (correlation)
360.936 28182.5
9 24
Mean square
F-ratio
P-value
924.167 20365.8 25.0441 43.6182 184.051 589.212 130.925 255.707 298.479 73.4837 0.368767 8.26905 21.6635 77.5385 53.8878
23.04 507.82 0.62 1.09 4.59 14.69 3.26 6.38 7.44 1.83 0.01 0.21 0.54 1.93 1.34
0.001 0.000 0.450 0.324 0.061 0.004 0.104 0.033 0.023 0.209 0.926 0.661 0.481 0.198 0.276
40.104
R2 = 0.9872; R2 (ad) = 0.9658; standard error of estimation = 6.33277; mean absolute error = 2.18777; F-ratio: MS factor/MS error.
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Fig. 2. Response surface: droplet diameter as a function of formulation variables.
70%W). In this case, the range of %Tween and O/S has been slightly extended (Table 2). The results of this study show that experimental response as a function of O/S ratio and %Tween20 can be approximated by a quadratic equation, the term (O/S)2 having no significance. The crossed term (Tween20)(O/S), which was found no significant when experiments on Table 1 were analyzed, was found significant in this case, when range was extended. It indicates that results are only valid into the experimental range. The equation which describes droplet size in terms of the significant variables (i.e. effects with P-values lower than 0.05) is as follows: droplet diameter (nm) = a + b (%Tween20) + c O S + d (%Tween20)2 + e (Tween20) O S
(2)
Fig. 3. Droplet size as a function of %Tween20 for one OS ratio. Quadratic model.
Fig. 4. Droplet size as a function of OS ratio for one %Tween20. Quadratic model.
a = 5893.3 ± 1055.9; b = −231.6 ± 47.0; c = −176.8 ± 157.6; d = 2.3 ± 0.5; e = 4.7 ± 3.2. The R2 of this equation is 99.21%, indicating that the fitting is really good. Variances of every factor and their importance can be obtained by the F-test method showing only significant parameters at 95% of confidence level. Fig. 2 shows response surface developed by the model for O/S ratio and Tween20/Span20 relation. These graphs offer a visual means of understanding how factors influence the measurement system. In this case, Tween20/Span20 relation has a non-lineal behavior (a quadratic dependence) showing an optimal relation which minimizes the droplet size (Fig. 2). This optimal relation, i.e. the minimum is dependent on the O/S ratio due to the existence of a light interaction between these two variables. Fig. 3 shows an example of a cross sectional cut of the Fig. 2 where O/S ratio was maintained constant, and here the optimum can also be observed. Referring to O/S ratio, an almost lineal behavior was observed denoting that the smaller O/S ratio, the smaller diameter is obtained (Fig. 2). An example of a cross section of Fig. 2 where the %Tween20 was maintained constant can be seen in Fig. 4. This figure shows that points can be nearly described by a straight line. In order to validate the empiric model, some more experimental data were obtained and added to the Figs. 3 and 4. Results denote that the empiric model describes quite well the behaviour
of the system because most of experimental data are between the confidence levels of the model at 95%. Some studies of phase transitions during emulsification process have been done to explain these results. Fig. 5 shows an example of a partial phase diagram at constant %Tween20, where the phases that are crossed during emulsification process
Fig. 5. Phase diagram. Emulsification paths.
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for different emulsification paths at this relation have been determined. The diagram shows the presence of a two-phase region near the 0% water line, as these surfactants are insoluble in paraffin. When a small amount of water is added (around 2–3%) a water-in-oil microemulsion appears (Om ). Higher amounts of water cause the appearance of a multiphase region where liquid crystalline phases have been identified under polarized light. This region is wide and extends to relatively high concentrations of water at the highest concentrations of surfactants tested. When the O/S ratio is increased the multiphase area progressively narrows, and disappears at 3:1 O/S. When more water is added, a two-phase region (oil-in-water microemulsion Wm and free oil O), appears. Is into this zone, at 70% water, where nano-emulsions are formed. Best results (small droplet size and low polydispersity) were obtained when the multiphase area was present at higher percentage of water, i.e., at the highest concentrations of surfactants. If one tries to extend the O/S ratio further than 3:1, nano-emulsions cannot be formed, as any liquid crystal is not crossed along the emulsification path, and it seems to be necessary [22,29].
surface developed by the model for mixing and addition rate. Droplet size decreases when addition rate of water over the mixture of oil and surfactants is slow. Emulsions with small droplet size are obtained if, during the emulsification path, the equilibrium is reached in a zone with liquid crystal phases or bicontinuous phases, with as much of oil as possible solubilized in [22,29]. Then, when the nano-emulsion is formed, the oil is intimate mixed with the rest of components and it has just to be redistributed. However, the presence of liquid crystal phases confers more viscosity to the system due to the presence of an organized structure, and it means that a good mixture necessary for equilibrium requires enough time and high mixing rate. So, high mixing rates are useful in order to obtain these phases transitions. Otherwise, when the multiphase zone is crossed and we enter into the Wm + O zone, where emulsion droplets are formed, the viscosity of the system is drastically decreased. Then, if the mixing rate is too high, it could promote some destabilization mechanisms like coalescence, sedimentation, . . . resulting a higher final droplet size.
3.3. Study and optimization of the effect of preparation variables on emulsion properties
4. Conclusions
A central composite design has been done to study and optimize the preparation variables of emulsions O/W at a constant composition (48.2% Tween20, 2:1 O/S ratio). In this case, two preparation variables are studied: addition and mixing rate. For the experimental data (Table 3), regression technique was used to fit the parameters to a response surface. The Eq. (2) describes the droplet size in terms of preparation variables, only showing significant parameters (with P-values lower than 0.05) at 95% of confidence level: droplet diameter (nm) = a + b (addition rate) + c (mixing rate) + d (mixing rate)2
(3)
a = 117.922 ± 20.208; b = 0.823 ± 0.423; c = 0.150 ± −0.104; d = 1.54 × 10−4 ± 1.28 × 10−4 . The fitting of this equation is not very good (R2 = 80.99%). However, it is capable to qualitatively explain the dependence of droplet size on formation variables. Fig. 6 shows the response
Experimental design is a good tool to study and optimize nano-emulsion properties prepared by low-energy emulsification methods. Eq. (2) fits experimental results related to composition variables. A linear dependence of the droplet size with O/S was obtained, according to the fact that smaller droplet sizes were obtained when a higher amount of surfactant was present, since the multiphase zone with liquid crystal crossed during emulsification was extended to higher concentrations of water. A quadratic dependence of droplet size on %Tween20 was found, according to an optimal Tween20/Span20 relation (related to an optimal HLB number, NHLB ). Eq. (2) predicts a slight interaction between OS and %Tween, and it can be explained because the oil present preferentially solubilizes the Span20 and, as a result, changes the effective Tween20/Span20 relation. Although Eq. (3) does not fit results so well as Eq. (2), it qualitatively predicts experimental results, according to emulsification mechanism. A linear dependence of droplet size on addition rate was obtained, the best nano-emulsions found at the lowest addition rates. At lower addition rates the liquid crystalline phases with oil solubilized, necessary for a good emulsification, have more time to be appropriately formed. A quadratic dependence of droplet size on mixing rate was found, related to an optimal mixing rate. Small droplet size were, then, obtained when mixing rate was the adequate to obtain liquid crystalline phases without promoting some destabilization mechanisms when emulsion droplets were formed during the emulsification process. Acknowledgment
Fig. 6. Response surface: droplet diameter as a function of preparation variables.
This work was funded under Spanish MCYT Project No. PPQ2002-04514-C03-02.
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