Thermodynamic prediction of active ingredient loading in polymeric microparticles

Journal of Controlled Release 60 (1999) 77–100

Thermodynamic prediction of active ingredient loading in polymeric microparticles a a, b b Ginger Tse , Daniel Blankschtein *, Adi Shefer , Samuel Shefer a

Department of Chemical Engineering, and Center for Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA b International Flavors and Fragrances Inc., Union Beach, NJ 07735, USA Received 12 November 1998; accepted 29 January 1999

Abstract The growing use of microparticles as a controlled-delivery system for pharmaceutical and non-pharmaceutical active ingredients (AIs) has prompted a costly trial-and-error development of new and effective microparticle systems. In order to facilitate a more rational design and optimization of AI loadings in microparticles, we have developed a molecular– thermodynamic theory to predict the loading of liquid AIs in polymeric microparticles that are manufactured by a solvent evaporation process. This process involves the emulsification of a liquid polymer solution (consisting of polymer and AI dissolved in a volatile solvent) in an aqueous surfactant solution. The theory describes the equilibrium distribution of the AI between the aqueous phase and the dispersed polymeric droplets. The universal functional activity coefficient (UNIFAC) and UNIFAC–Free Volume (FV) group-contribution methods are utilized to model the nonidealities in the water and polymeric droplet phases, respectively. The inputs to the theory are: (i) the chemical structures, densities and total masses of the manufacturing ingredients, (ii) the manufacturing temperature and (iii) the glass transition temperature of the polymer. Since surfactant concentrations exceeding the critical micellar concentration (CMC) are often required in order to stabilize the dispersed polymeric droplets during the emulsion manufacturing process, the theory also accounts for AI solubilization in surfactant micelles present in the manufacturing solution. To test the AI loading predictions, we compare theoretical predictions of AI loadings in poly(lactic acid), poly(methyl methacrylate) and polystyrene microparticles to experimentally measured ones for five model AIs with varying degrees of hydrophobicity (benzyl alcohol, n-octanol, geraniol, farnesol and galaxolide). We also demonstrate how the developed theory can be utilized to screen polymers with respect to their abilities to load a given AI, as well as to provide guidelines for manufacturing microparticles having the desired AI loading.  1999 Elsevier Science B.V. All rights reserved. Keywords: Active ingredient loading; Thermodynamic loading model; Microspheres / microparticles; UNIFAC / UNIFAC–FV; Groupcontribution method

*Corresponding author. Tel.: 11-617-253-4594; fax: 11-617-252-1651. E-mail address: [email protected] (D. Blankschtein) 0168-3659 / 99 / $ – see front matter  1999 Elsevier Science B.V. All rights reserved. PII: S0168-3659( 99 )00056-5

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1. Introduction Microparticle controlled-delivery systems are often utilized to enhance and prolong the effectiveness of active ingredients (AIs), such as flavors, fragrances, pesticides and herbicides [1–4]. Microparticles can be envisioned to improve AI delivery through three features: 1. Loading of the AI in microparticles helps to retain the activity of unstable or sensitive AIs and to prevent adverse interactions between the AI and other ingredients present in the product formulation [2,5,6]. 2. The surface characteristics of the microparticle can be designed to enhance the adhesion of the microparticles to a targeted site [3,7]. 3. Diffusion of the AI out of the microparticles can be sustained at a desired rate through appropriate selection of the material used to load the AI. The design of an effective microparticle controlled-delivery system may require consideration of each of these three features. Due to the variety of AIs available, systematic procedures do not exist for simultaneously optimizing the three microparticle features for any given AI. The rational design of an effective microparticle system for a given AI would be facilitated by a fundamental understanding of each feature. This paper presents a molecular– thermodynamic theory developed to understand the effects of the manufacturing ingredients and the manufacturing conditions on AI loadings in microparticles. The availability of a molecularly based theory that can quantitatively predict AI loadings in microparticles would be very valuable. First, the theory could be used to screen materials, usually natural or synthetic polymers [8,9], with respect to their abilities to load a given AI. The theory could also be utilized to optimize the manufacturing of the microparticles, for example, by indicating (i) more suitable raw materials, (ii) optimal concentrations for the raw materials or (iii) a more appropriate operating temperature. Finally, the AI loading in the microparticles directly affects the rate at which the AI is released from the microparticles, and therefore, quantitative predictions of AI loadings in microparti-

cles should also aid in the design of the microparticle’s sustained-release capabilities [10,11]. Materials, such as polymers, can be evaluated qualitatively with respect to their abilities to load a given AI based on the solubility of the AI in the polymer in the context of the regular solution theory [12–15]. Regular solution theory is commonly utilized to provide a first-order estimate of the compatibility between a polymer and an AI, because this simple approach only requires knowledge of the solubility parameters of the polymer and of the AI. In general, high AI loadings in the polymer are expected when the AI and the polymer have similar solubility parameters (within 2 MPa 1 / 2 of each other) [14]. Solubility parameters for many common polymers and solvents have been tabulated [16–18], or they may be estimated using group-contribution methods [16,19,20]. Note that, within the context of the regular solution theory, only the enthalpic interactions between the AI and the polymer are considered. In addition, regular solution theory does not generally apply to polymer–AI systems that interact through specific interactions, such as those of the polar or hydrogen-bonding type. Furthermore, the presence of other ingredients in the manufacturing system may also affect the AI loading, and therefore, quantitative estimates of AI loadings in polymeric microparticles require consideration of how the microparticles are prepared. Polymeric microparticles can be manufactured with good size control by emulsification-type processes, such as solvent evaporation, hot melt–freezing and emulsion polymerization [9,21–23]. These processes all involve the emulsification of a liquid polymeric phase in an immiscible, or partially miscible, liquid (referred to as the continuous medium). The resulting polymeric droplets are usually stabilized by a surfactant or polymeric emulsifier, such as poly(vinyl alcohol), which is present in the continuous medium. In solvent evaporation methods, the polymeric phase is a solution of the polymer and the AI dissolved in a volatile solvent. Solid microparticles are formed following evaporation of the solvent from the emulsified polymeric phase droplets. Hot melt–freezing processes eliminate the need for using solvents during manufacturing [22]. In this process, the polymeric phase, which is a solution of the polymer and the AI at a temperature above the

G. Tse et al. / Journal of Controlled Release 60 (1999) 77 – 100

melting point of the polymer, is solidified following emulsification by cooling the entire emulsion to a temperature below the polymer’s melting temperature. In emulsion polymerization processes, the polymeric phase is a solution of the reacting monomer and the AI, and solid microparticles are formed upon complete reaction of the monomers [21,23]. A commonly used approach to quantitatively predict AI loadings in polymeric microparticles that are prepared by emulsification-type processes involves evaluating the distribution of the AI between the dispersed polymeric phase and the continuous medium. This distribution can be characterized by the AI distribution coefficient, which represents the ratio of the AI concentrations in the polymeric phase and in the continuous medium. AI distribution coefficients between a polymeric phase and an aqueous phase have been correlated empirically to other characteristic AI physical parameters, such as the AI octanol–water partition coefficient, or to the estimated interaction energies between different components in the polymeric and aqueous phases [10,15,24,25]. While these correlations provide useful quantitative predictions of AI loadings in polymeric systems, they require experimental measurements of AI distribution coefficients to generate the correlations. In addition, the correlations typically apply only to the specific polymer system for which they were developed. Correlations for other polymer systems may require additional experimental measurements of AI distribution coefficients for those polymers. More general theoretical approaches have also been developed to quantitatively predict the solubility of AIs in polymers based on the Flory– Huggins theory of polymer solutions [11,26–28]. These theories, however, consider AI concentrations at infinite dilution. In practice, high AI concentrations may be encountered both in the polymeric phase and in the continuous medium, and therefore, the presence of AI–AI interactions, as well as the entropy of mixing the AI with the polymer or with the continuous medium, may not be accounted for properly in an infinite dilution analysis. In this paper, we develop a molecular–thermodynamic theory to predict the loading of liquid AIs in polymeric microparticles that are prepared by the solvent evaporation manufacturing process. Generalization of the developed theory to predict AI loadings

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in polymeric microparticles manufactured by the hot melt–freezing process has also been considered and will be presented elsewhere. The theory can further be extended to predict AI loadings in polymeric microparticles prepared by emulsion polymerization, whereby the reaction, and subsequent disappearance, of the monomers in the dispersed polymeric droplets is analogous to the evaporation of the solvent from the polymeric droplets in the solvent evaporation process. The theory developed here offers several new features when compared to the existing methodologies described above. First, the theory utilizes groupcontribution methods to describe the nonidealities in the polymeric phase and in the continuous medium, thus allowing a general prediction of AI loadings in a polymeric microparticle system for which the chemical structures and densities of the manufacturing ingredients are known. Second, the developed theory accounts for the possible solubilization of AI in surfactant micelles that may be present in the manufacturing solution. Indeed, surfactant concentrations exceeding the critical micellar concentration (CMC), the concentration at which micelles first form in solution, are often required in emulsificationtype manufacturing processes in order to sufficiently stabilize the polymeric phase droplets against agglomeration. As a result, AI solubilization in surfactant micelles may occur, and this can significantly reduce the amount of AI loaded in the microparticles. Third, since high AI loadings in the microparticles may be achieved in practice, the finite concentrations of the AI in the polymeric phase and in the continuous medium are taken into account. Fourth, the theory explicitly accounts for the different ingredients utilized during manufacturing, the concentration of each ingredient and the manufacturing temperature. Therefore, the theory can be utilized to predict how the AI loading in the microparticles changes when these manufacturing conditions are varied. To test the theoretical loading predictions, we compare theoretically predicted AI loadings with experimentally measured ones in poly(lactic acid) (PLA), poly(methyl methacrylate) (PMMA) and polystyrene (PS) microparticles prepared by the solvent evaporation manufacturing process. The five model AIs examined in the comparison (benzyl

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alcohol, n-octanol, geraniol, farnesol and galaxolide) span a range of hydrophobicities characteristic of fragrance components, as well as of other pharmaceutical and non-pharmaceutical AIs, such as pesticides or flavor ingredients. Since dichloromethane is a good solvent for PLA, PMMA and PS, this volatile solvent was used to dissolve the polymers with the AI prior to emulsification. Cetyltrimethylammonium bromide (CTAB), a cationic surfactant, was utilized to stabilize the polymeric phase droplets during manufacturing. The manufactured microparticles therefore had an overall net positive charge and possessed targeting capabilities for a negatively charged surface. The remainder of the paper is organized as follows. In Section 2, we develop the molecular– thermodynamic theory for predicting AI loadings in polymeric microparticles. In Section 3, we provide details of the experimental methods utilized. In

Section 4, we present a comparison of the theoretically predicted AI loadings with the experimentally measured ones. We also provide examples of how the theory can be utilized to select polymers for loading a given AI, as well as to provide guidelines for manufacturing microparticles having a desired AI loading. Finally, in Section 5, we present concluding remarks.

2. Theory In the solvent evaporation manufacturing process, a liquid polymer solution, consisting of polymer and AI dissolved in a volatile solvent, is emulsified in a continuous medium. We have utilized relatively hydrophobic polymers in order to load the relatively hydrophobic model liquid AIs that were examined (see Table 1 for a list of the chemical structures and

Table 1 Structures and physical properties of the model AIs examined: benzyl alcohol, n-octanol, geraniol, farnesol and galaxolide a Model active ingredient

Benzyl alcohol

Chemical structure

Molecular weight [g / mol]

Density [g / cm 3 ]

Vapor pressure at 258C [bar]

log K o / w

Solubility parameter [(cal / cm 3 )1 / 2 ]

108.14

1.041

1.4310 24

1.10

12.05

25

2

10.30

n-Octanol

130.23

0.827

9.8310

Geraniol

154.25

0.889

3.0310 26

2.75

9.81

Farnesol

222.37

0.879

8.1310 210

4.77

9.12

Galaxolide

258.40

1.004

2.8310 29

5.66

8.80

a

Vapor pressures at 258C were estimated using a corresponding-states equation developed by Lee and Kesler (see pages 205–208 in Ref. [29]). Octanol–water partition coefficients, K o / w , were estimated from quantitative structure–activity relationships [30,31], while solubility parameters were estimated using Hoy’s group-contribution method [20]

G. Tse et al. / Journal of Controlled Release 60 (1999) 77 – 100

physical properties of the model liquid AIs examined in this paper). Consequently, a hydrophilic aqueous surfactant solution serves as the continuous medium. As the solvent evaporates from the manufacturing solution, the AI distributes between the polymeric droplets and the surrounding aqueous surfactant solution (see Fig. 1). Note that, if the surfactant utilized to stabilize the polymeric droplets is present at a concentration exceeding the CMC, then surfactant micelles, which can solubilize the AI, are also present in the manufacturing solution (see Fig. 1). The polymeric droplet phase initially contains only polymer and AI dissolved in the volatile solvent. However, water may penetrate into the polymeric droplets during manufacturing. Using the universal functional activity coefficient free volume (UNIFAC–FV) group-contribution method, which is described in Section 2.3, the solubility of water in the PLA, PMMA and PS polymeric droplet phases was estimated to be very low ( , 10 25 wt.%). With these calculated water solubilities, the predicted AI loadings were found to be insensitive to the presence of water in the polymeric droplets. As a result, the presence of water in the polymeric droplet phase can be safely neglected, with the polymeric droplets assumed to contain only polymer, loaded AI and solvent. During manufacturing, the water phase may contain AI that is not loaded, surfactant monomers,

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dichloromethane solvent and water. Using the theory that we develop below to predict AI loadings, less than a 0.01% change in the predicted AI loadings was observed when the surfactant monomers were considered to be present in the water phase. The insensitivity of the AI loading predictions to the presence of the surfactant monomers results from the low surfactant monomer concentration in the aqueous solution (typically around 1 mM). The AI loading predictions were also found to be lowered on average by 0.15% when dichloromethane solvent was considered to be present in the water phase under saturated conditions. Due to these small effects, the presence of both the surfactant monomers and the solvent in the water phase can be safely neglected, with only two components (AI and water) assumed to be present in the water phase. Based on experimental measurements of the solubilization of dichloromethane in the CTAB micelles [32], we estimated that dichloromethane constitutes less than 1% of the micellar phase under saturated conditions. As a result, the presence of solvent in the surfactant micelle phase has been neglected, with the micellar phase assumed to contain only micellized surfactant and solubilized AI. The amount of AI loaded in the polymeric microparticles is characterized by the AI loading, which is defined as AI Loading final mass of AI in the microparticles 5 ]]]]]]]]]] 3 100 final total mass of the microparticles m Pa x Pa Ma ]]]]]] 5 ]]]] 3 100 5 m Pp 1 m Pa 1 m sP x pP Mp 1 x aP Ma 1 x sP Ms 3 100

Fig. 1. Schematic representation of the solvent evaporation manufacturing process illustrating the distribution of the AI (shown by the squares) between the polymeric microparticles, water, and surfactant micelles. The solvent that evaporates during manufacturing is depicted by the triangles.

(1)

where m Pi and x Pi are the masses and mole fractions, respectively, of each component i (i 5 p for polymer, a for AI, or s for solvent) in the polymeric droplets (P) at the end of the manufacturing process, and Mi is the molecular weight of component i. The AI loading represents the weight percent composition of the AI in the microparticles. Note that the surfactant entrapped on the surface of the microparticles contributes negligibly to the total mass of the microparticles. For the CTAB surfactant, assuming full coverage of the surfactant on the surface of the microparti-

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cles, we estimated that the mass of CTAB entrapped on the surface of the microparticles represents less than 1% of the total mass of the microparticles. Therefore, in the denominator of Eq. (1), the mass of the entrapped surfactant has not been included in the total mass of the microparticles. From Eq. (1), the AI loading can be evaluated if the mole fractions of the polymer, x Pp , and of the AI, x aP , in the solidified polymeric droplets are known. Note that, because the summation of all the component mole fractions in the polymeric droplet phase must be equal to unity sx Pp 1 x Pa 1 x Ps 5 1d, the solvent mole fraction, x Ps , can be determined directly from the mole fractions of the polymer and of the AI. These latter two mole fractions can be evaluated through a consideration of the equilibrium distribution of the AI between the polymeric droplets and the aqueous surfactant solution at the end of the solvent evaporation manufacturing process.

2.1. Equilibrium AI distribution The equilibrium AI distribution between the polymeric droplets (P), water (W) and the surfactant micelles (M) is described by equating the chemical potentials, m Ia , of the AI in each phase I (I5P, W or M), that is, M m Pa 5 mW a 5 ma

(2)

This is equivalent to an equality between the AI activities, a Ia , in each phase I (I5P, W or M), where the pure-component reference state is utilized for the AI in each phase. Specifically, M a Pa 5 a W a 5 aa

(3)

The AI activities can be expressed more conveniently as a product of the AI mole fractions, x Ia , and the AI activity coefficients, g Ia , in each phase I (I5P, W or M). Note that the AI activity coefficients are functions of temperature (T ), pressure (P) and the independent mole fractions in each phase. In this case, Eq. (3) reduces to W W x Pa g Pa sT,P,x Pa ,x Pp d 5 x W a g a sT,P,x a d M M 5 xM a g a sT,P,x a d

(4)

In order to determine the two mole fractions, x Pa

and x Pp , required in Eq. (1) to evaluate the AI loading, we note that, in Eq. (4), there are six unknowns (T, P and four independent mole fractions) in the two equalities. In general, the temperature and pressure of the system are specified by the manufacturing conditions. Therefore, two additional constraints are needed in order to evaluate the four independent mole fractions in Eq. (4). First, the total amount of AI that is utilized to manufacture the microparticles is generally known. We determined from mass balance checks around the manufacturing process that there is no loss of AI either by evaporation with the solvent or by adhesion to the walls of the manufacturing container. Therefore, the total amount of AI utilized to manufacture the microparticles, m TOTAL , must distribute between a the three available phases, that is, m TOTAL 5 m aP 1 m aW 1 m aM a

(5)

The mass balance in Eq. (5) provides a relationship between the AI mole fractions in each phase (x Pa , x W a and x M a ), given the total initial masses of the polymer, the AI and the water, as well as the total surfactant concentration, utilized in the manufacturing process, as described in more detail in Appendix A. Furthermore, in the equilibrium thermodynamic analysis, the kinetics of the solvent evaporation process are simulated through an evaluation of the equilibrium AI distribution between the liquid polymeric droplets and the aqueous surfactant solution as a function of the solvent content in the polymeric droplet phase. Solvent diffusion out of the polymeric droplets results in a change in the AI concentration, and consequently, in a change in the AI activity, in the polymeric droplet phase. AI diffuses out of the polymeric droplets in response to this shift in the equilibrium of the system in order to establish a new equilibrium. In the initial stages of the solvent evaporation process, the polymer concentration in the polymeric droplet phase is so dilute that the AI and the solvent diffuse out of the polymeric droplets at similar rates. Therefore, AI diffusion out of the polymeric droplets re-establishes equilibrium concurrently with the shifts in equilibrium prompted by solvent diffusion out of the polymeric droplets, and the equilibrium analysis is reasonable.

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With continued solvent evaporation, the polymeric droplets become more viscous. In this case, the AI may diffuse out of the polymeric droplets at a slower rate than the smaller solvent molecules. As a result, the slower diffusion of the AI out of the polymeric droplets may inhibit the re-establishment of equilibrium in response to the shifts in the equilibrium resulting from the decreasing solvent concentration in the polymeric droplets. However, it is also in this limit that the polymeric droplets eventually solidify. The equilibrium AI distribution achieved just prior to solidification is then frozen in the microparticles, and the AI loading is evaluated from the equilibrium AI distribution frozen in the solidified microparticles. It should be noted that even if kinetic effects become important, an equilibrium analysis provides a lower bound for the predicted AI loadings since kinetic considerations result in the entrapment of higher AI loadings in the microparticles. In the equilibrium analysis, evaluation of the AI loading frozen in the microparticles, therefore, requires specification of the condition at which the liquid polymeric droplets solidify. This solidification condition, which is described in more detail in Section 2.5, provides the second constraint needed to solve Eq. (4). With the mass balance and solidification constraints, the equilibrium AI distribution between the polymeric droplets and the aqueous manufacturing solution is then evaluated from Eq. (4) using the activity coefficient models described below.

2.2. Aqueous phase model: UNIFAC The modeling of aqueous systems represents a challenging task due to the ordered structure of water, resulting from strong hydrogen bonding interactions between water molecules. In Eq. (4), the aqueous phase AI activity coefficient, g W a , is predicted using the UNIFAC group-contribution method developed by Fredenslund et al. [33]. The UNIFAC method permits a general a priori estimation of activity coefficients for almost any molecule whose chemical structure is known, even if no experimental vapor–liquid equilibrium (VLE) or liquid–liquid equilibrium (LLE) data are available for that molecule. This generality is often not possible with other activity coefficient estimation methods that require

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experimental VLE or LLE data to fit model parameters. Indeed, this is advantageous for the study of AI loadings in polymeric microparticles because experimental VLE or LLE data are often not available for most AIs of practical interest. The UNIFAC method is based on a ‘‘solution-ofgroups’’ concept in which each component molecule is broken down into defined structural groups [33,34]. The local composition in the mixture is determined from the probability of placing groups next to each other on a lattice based on their sizes and shapes, as well as on the interaction energy between the groups. Each structural group k is characterized by a volume parameter R k and a surface area parameter, Q k , both of which are calculated from van der Waals group volumes and surface areas [35]. Interactions between two structural groups m and n are described by binary interaction parameters, a mn , which are fit from a large database of VLE and / or LLE data for molecules containing a variety of structural groups [33,36,37]. We have utilized the recently revised set of UNIFAC interaction parameters, which were fit to an extended database of both VLE and LLE data [37]. Even though the AI distribution between the polymeric droplets and the aqueous manufacturing solution constitutes a LLE problem, LLE properties predicted using interaction parameters derived from the mixed VLE / LLE database are often found to agree better with experimental data than those predicted using only LLE-based interaction parameters, perhaps due to the limited size of the LLE database [38,39]. Note that specific interactions, such as hydrogen bonding interactions between a water molecule and a hydroxyl group, can be treated using the UNIFAC method as long as VLE or LLE data for molecules having similar interactions are contained in the VLE / LLE database utilized to fit the UNIFAC interaction parameters. This offers an advantage over other activity coefficient estimation methods, such as the regular solution theory [12], that were developed to describe nonpolar, and therefore, nonaqueous, systems. Within the UNIFAC framework, the AI activity coefficient in the aqueous solution, g W a , is evaluated based on contributions from a combinatorial activity coefficient, g Ca , and a residual activity coefficient, g aR , that is,

84 C R ln g W a 5 ln g a 1 ln g a

G. Tse et al. / Journal of Controlled Release 60 (1999) 77 – 100

(6)

The combinatorial activity coefficient, g Ca , accounts for differences in the shapes and sizes of the molecular structural groups present in the solution, while the residual activity coefficient, g Ra , accounts for enthalpic interactions between the molecular structural groups. Details of the precise forms of the expressions for g Ca and g Ra , as well as examples of how to use these expressions, are presented in Ref. [29] (pp. 314–332) and in Ref. [33]. In order to confirm the ability of the UNIFAC group-contribution method to predict the aqueous phase AI activity coefficient in Eq. (4), UNIFAC activity coefficient predictions were compared to those derived from published experimental VLE data at atmospheric pressure for binary aqueous systems over a wide range of solution compositions [40]. Published experimental VLE data were only available for one of the model AIs considered here (benzyl alcohol). However, in the UNIFAC method, a given structural group provides the same contribution to a thermodynamic property regardless of its surrounding environment. Therefore, the aqueous binary systems examined contained solutes having structural groups similar to those present in the other model AIs considered (including saturated and unsaturated alkyl groups, hydroxyl groups and aromatic structures). The average relative deviation in the predicted activity coefficients was 11% for eleven different binary aqueous systems examined (consisting of acrolein, allyl alcohol, benzyl alcohol, 1butanol, cyclohexanol, dichloromethane, 1-hexanol, 2-methyl-3-buten-2-ol, 1-pentanol, phenol or 1-propanol) over a wide range of solution compositions. Such deviations in the predicted aqueous phase AI activity coefficient were found to change the predicted AI loadings, on average, by less than 0.1%. Therefore, the UNIFAC group-contribution method should provide a reasonable description of the aqueous phase AI activity coefficient.

2.3. Polymeric droplet phase model: UNIFAC–FV Since the manufactured microparticles are relatively large (mean diameters range between 1 and 150 mm), the microparticle curvature will not affect the AI chemical potential in the polymeric droplet

phase. As a result, the polymeric droplets can be treated as a macroscopic polymeric liquid phase. Flory–Huggins lattice theory is often utilized for the thermodynamic description of polymer solutions due to the relative simplicity of the resulting model equations [28]. Within the context of this statistical– mechanical theory, the free energy of mixing contains an entropic contribution, which accounts for the combinatorics of randomly mixing long polymer chains with small solvent molecules, and an enthalpic contribution, which accounts for the interactions between the polymer segments and the solvent molecules using a Hildebrand–Scatchard regular solution formulation of the enthalpy of mixing [12,41]. The enthalpic interactions are characterized by the Flory–Huggins x parameter, which is generally an empirically fitted parameter. For polymer–AI systems of practical interest, limited experimental thermodynamic data, such as vapor pressure or osmotic pressure measurements, are available to deduce the relevant Flory–Huggins x parameters directly. Furthermore, estimates of the Flory–Huggins x parameter made using the solubility parameters of the polymer and the AI within the context of the Hildebrand–Scatchard regular solution theory do not apply as generally to polymer–AI systems that interact through specific interactions, such as those of the hydrogen-bonding type [12,41]. As a result, we have followed a different approach and estimated the AI activity coefficient in the polymeric liquid phase using the UNIFAC–FV group-contribution method [42]. This method is based on the original UNIFAC group-contribution method (described above in Section 2.2). The UNIFAC–FV method requires input of only the chemical structures, molecular weights, and densities of the AI, polymer and solvent in order to predict the AI activity coefficient in the polymeric droplet phase. Furthermore, the same interaction parameters as those utilized in the UNIFAC approach are also used in the UNIFAC–FV method. As discussed in Section 2.2, these interaction parameters allow a prediction of AI activity coefficients in solutions containing molecules that interact through London dispersion forces, as well as through specific interactions, such as those of the permanent dipole-permanent dipole or hydrogen-bonding type. In the polymeric droplet phase, differences in the

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sizes and densities of the polymer and the AI (or solvent) may result in nonzero volume changes upon mixing that are not accounted for in the original UNIFAC method [43]. These free-volume differences between the AI (or solvent) and the polymer are treated in the UNIFAC–FV method through an additional free-volume contribution to the AI activity coefficient. Within the context of the UNIFAC–FV method, the AI activity coefficient in the polymeric droplet phase, g Pa , is determined from [42] ln g Pa 5 ln g Ca 1 ln g aR 1 ln g FV a

(7)

The combinatorial (g Ca ) and residual (g Ra ) activity coefficient contributions in Eq. (7) are computed in the same manner as those described in Eq. (6). The free-volume contribution (g FV a ) is derived from Flory’s equation of state [44], where the enthalpic portion of the equation of state is taken to be zero, since the enthalpic interactions are already accounted for in the residual activity coefficient in Eq. (7). The precise form of the free-volume contribution is described in Refs. [42] and [45], along with examples illustrating how the equations may be used to evaluate the activity coefficient of a small molecule in a polymer solution. The ability of the UNIFAC–FV method to predict AI activity coefficients in polymer solutions was evaluated in order to validate the selection of this method for the description of the polymeric droplet phase activity coefficient in Eq. (4). Again, no published experimental VLE or LLE data were available for ternary or binary polymer solutions containing any of the model AIs considered here. In this paper, therefore, we examined binary solutions of the polymers considered here for which experimental solvent activity data were available. Specifically, predicted and experimentally deduced solvent activity coefficients were compared in PS and PMMA polymer solutions using experimental data provided in Refs. [45] and [46]. Comparisons for poly(butyl methacrylate) (PBMA) solutions were also used to evaluate the AI activity coefficient predictions in the model PMMA polymer system. No experimental activity coefficient data were available for the model PLA system; however, since the model PMMA and PLA polymers contain similar structural groups, we have utilized the comparisons in the

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PMMA and PBMA polymer systems as an indication of the ability of the UNIFAC–FV group-contribution method to describe AI activity coefficients in the PLA polymer system. The average relative deviation between the predicted and experimentally deduced solvent activity coefficients for fifteen different solvents (acetone, benzene, 2-butanone, butylacetate, carbon tetrachloride, chloroform, cyclohexane, diethyl ketone, dioxane, ethylbenzene, nonane, n-propylacetate, toluene, 1,2,4-trimethylbenzene and m-xylene) in PS solutions spanning a wide range of solution compositions was 10%. This uncertainty in the AI activity coefficient in the polymeric droplet phase results in an average 0.4% change in the predicted AI loadings in the PS microparticle systems examined. In the PBMA and PMMA polymer solutions, predicted solvent activity coefficients for 2butanone, toluene and diethylether deviated on average by 30% from the experimental values. In this case, a 30% uncertainty in the predicted AI activity coefficient in the polymeric droplet phase would result in 1.2 and 0.4% changes, on average, in the predicted AI loadings in the model PMMA and PLA polymers, respectively. In summary, the UNIFAC– FV method should provide reasonable predictions of AI activity coefficients in the PS and PLA polymeric droplet phases, while greater uncertainties are expected in the AI activity coefficient predictions in the PMMA polymeric droplet phase.

2.4. Micellar-phase model During the manufacturing process, the positively charged CTAB surfactant helps to stabilize the emulsified polymeric droplets against agglomeration due to attractive interdroplet van der Waals forces [47,48]. Surfactant concentrations greater than the CMC are often needed to completely stabilize the droplets. As a result, surfactant micelles are present in the aqueous manufacturing solution. In the micelles, the surfactant molecules arrange themselves so that the hydrophobic surfactant tail groups are clustered together in the core of the micelle, while the hydrophilic surfactant head groups are oriented towards the aqueous phase (see Fig. 1). This results in the formation of a shielded hydrophobic core region in which it is possible to solubilize hydro-

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phobic solutes, such as the model AIs examined. Furthermore, even more hydrophilic solutes may act as co-surfactants, which are then solubilized within the palisade layer of the micelle [49,50]. Complex descriptions of solubilization have been developed to account for the ordered structure and orientation of the solubilized solute and the surfactant in the micelles [51,52]. These theories have been applied to the solubilization of simple molecules, such as linear, saturated alcohols and simple benzene derivatives. The generalization of these theories for the prediction of the solubilization of complex AI molecules of practical interest (for example, molecules consisting of unsaturated carbons, branches or multiple aromatic / nonaromatic rings) has not been well established. In addition, general solubilization models that do not contain empirically fitted parameters are desirable since experimental data regarding the solubilization of AIs in relevant surfactant systems may not be available. In this paper, the thermodynamic loading theory was first developed neglecting AI solubilization in surfactant micelles altogether. As is shown in Section 4.3, this leads to a general overprediction of AI loadings. In practice, surfactant micelles compete with the polymeric droplets for AI, thus decreasing the amount of AI loaded. In accounting for the possibility of AI solubilization in the surfactant micelles, the goal is to evaluate the AI mole fraction in the surfactant micelles, x M a . This mole fraction enters directly in the evaluation of the AI equilibrium distribution in the manufacturing solution through the calculation of the AI activity in the micellar phase in Eq. (4) and through the mass balance constraint in Eq. (5). The model AIs considered here are relatively hydrophobic compounds that may contain a polar functionality. Therefore, they may be treated as a co-surfactant in the micelles, whereby the polar functionality plays the role of the surfactant head group and the remainder of the molecule acts as the surfactant tail group. AI solubilization in the CTAB micelles may then be viewed as a mixed micellization process. Molecular–thermodynamic theories developed to describe the formation of mixed surfactant micelles decompose the free energy of forming a mixed surfactant micelle into several contributions, including the free energy of transferring the surfac-

tant tail groups from the aqueous solution into the hydrophobic core of the micelles, the free energy of forming an interface between the hydrophobic micellar core and the surrounding aqueous solution, the free energy associated with anchoring one end of the surfactant tails at the micellar interface, and the free energy associated with steric and electrostatic interactions between the surfactant head groups at the micellar interface [53]. To a first approximation, we have estimated AI solubilization in the surfactant micelles by accounting only for the transfer free-energy contribution, which is the largest contribution to the free energy of forming mixed micelles [53]. In this case, the surfactant micelles are treated as a bulk surfactant liquid phase, and AI solubilization in the surfactant micelles is determined by evaluating the equilibrium distribution of the AI between water and the bulk surfactant liquid. Since we neglect the ordered structure and orientation of the surfactant in the micelles, we do not distinguish between the head group region and the tail group region of the AI or the CTAB surfactant. Furthermore, since we neglect electrostatic interactions between the surfactant head groups, it is assumed that, in the case of the positively charged CTAB surfactant examined, the AI is solubilized in the micellar pseudophase composed only of the uncharged, amphiphilic portion, or cetyltrimethylammonium (CTA) portion, of CTAB. The equilibrium AI distribution between water and the micellar pseudophase is described by equating W W the AI activities in the water (a W a 5 x a 3 g a ) and in M M M the bulk surfactant liquid (a a 5 x a 3 g a ), as shown by the right-hand-side equalities in Eqs. (3) and (4). In Section 2.2, the UNIFAC method was utilized to evaluate the AI activity coefficient in the water. Since the sizes of the AI and the surfactant are similar, free-volume differences between the AI and the surfactant are not important. Therefore, for consistency, the UNIFAC group-contribution method presented in Section 2.2 is also utilized to estimate the AI activity coefficient in the micellar pseudophase. For very hydrophobic AIs, such as farnesol and galaxolide, the formation of the micellar pseudoM phase is nearly ideal (that is, a M a ¯ x a ), due to the compatibility between the AI and the hydrophobic CTA surfactant [54]. However, in practice, since the

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AI does not form micelles by itself, mixed AI–CTA micelles cannot exist if the mixed micelle is too concentrated in AI. We therefore expect that a M a for the real mixed AI–CTA micelles attains a value of unity (corresponding to AI saturation in the mixed micelles) at a value of x M a of less than unity. In the context of the micellar pseudophase treatment, a M a 5 1 only when x M a 5 1. Consequently, the actual activity of the AI in the micelles increases with increasing xM a faster than that predicted by the micellar pseudophase treatment. Therefore, at any given equilibrium activity, the micellar pseudophase treatment overpredicts x M a . This leads to an overprediction of AI solubilization in the surfactant micelles, and subsequently, to an underprediction of the AI loadings in the polymeric microparticles for the more hydrophobic AIs. For the more hydrophilic AIs, such as benzyl alcohol, mixing the AI in the micellar pseudophase results in positive deviations from ideality, or a M a . x aM [54]. In this case, it is not as obvious how the micellar pseudophase treatment will be able to predict AI solubilization in the surfactant micelles. Therefore, in Table 2, we compare predicted solubilization constants, Kx , for the two relatively hydrophilic AIs examined (benzyl alcohol and n-octanol) at an infinitely dilute concentration in an aqueous CTAB surfactant solution with those that were experimentally measured by Abu-Hamdiyyah [55]. Note that Kx is defined by xM a Kx 5 ] x aW

(8)

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hydrophilic AIs examined. This may result from the neglect of electrostatic effects in the micellar pseudophase. Indeed, the presence of the nonionic AI in the cationic CTAB micelles may reduce the neglected electrostatic repulsions between the charged surfactant headgroups, thus resulting in increased AI solubilization. While the comparisons in Table 2 were made for an AI at an infinitely dilute concentration in the CTAB surfactant solution, no experimentally measured AI solubilization constants were available for the more hydrophilic AIs at finite concentrations in a CTAB surfactant solution. Nevertheless, as in the case of the more hydrophobic AIs examined, the micellar pseudophase treatment is expected to overpredict the solubilization of the more hydrophilic AIs in the surfactant micelles at AI concentrations close to the saturation limit. In summary, in the context of the micellar pseudophase treatment, we expect solubilization of the more hydrophilic AIs, such as benzyl alcohol, to be underestimated at fairly dilute AI concentrations (with respect to the saturation limit). Note that an underprediction of AI solubilization in the surfactant micelles results in higher predicted AI loadings in the polymeric microparticles, because AI that would otherwise be solubilized in the surfactant micelles is now loaded in the microparticles. On the other hand, AI solubilization will be overestimated for the more hydrophobic AIs, such as farnesol and galaxolide, and as a result, the AI loading in the polymeric microparticles will be underpredicted for these AIs.

2.5. Microparticle solidification where x M a is the mole fraction of solubilized AI in the micelles, and x aW is the AI mole fraction in the water. As can be seen from Table 2, the micellar pseudophase treatment underpredicts the measured AI solubilization constants for the two relatively

Table 2 Predicted and experimentally measured [55] AI solubilization constants (Kx ) for benzyl alcohol and n-octanol at infinite dilution in an aqueous CTAB surfactant solution Model AI

Experimental log Kx

Predicted log Kx

Benzyl alcohol n-Octanol

3.0260.08 4.1460.02

2.37 3.32

Diluents, such as the AI and the solvent, which are present in the polymeric droplets, plasticize the polymer and lower its glass transition temperature, T g . During manufacturing, as the solvent leaves the polymeric droplets and the AI distributes between the polymeric droplets and the aqueous phase, the T g of the polymeric droplets increases and the polymeric droplets become more viscous. Eventually, the T g of the polymeric droplets exceeds the ambient manufacturing temperature, and the polymeric droplets become glassy. When this glass transition occurs, molecular motion within the polymeric droplets slows down considerably. Consequently, it is antici-

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pated that the polymeric droplets solidify into their final microparticle state when the T g of the polymeric droplets equals the manufacturing temperature because further diffusion of solvent out of the glassy polymeric droplets will be extremely slow. The microparticle solidification point can be characterized by the residual polymer volume fraction, f solid , present in the polymeric droplet when the T g p of the polymeric droplet is equal to the manufacturing temperature. This selection enables characterization of the microparticle solidification point by a single composition variable for the loadings of different AIs in a given polymeric microparticle system. Note that the composition of neither the AI nor the solvent in the solidified microparticles would provide a unique solidification criterion for a given polymer since the amount of AI loaded in the microparticles and the residual amount of solvent remaining in the microparticles depends on the AI loaded. Note also that f solid is related to the selected p concentration variables, x Pa and x Pp , in Eq. (4) by x Pp 3Vp f psolid 5 ]]]]]]]]]]]] sx Pa 3Vad 1sx Pp 3Vpd 1 fs1 2 x Pa 2 x Ppd 3Vs g (9) where Vi is the molar volume of species i (i5a, p or s). Kelley and Bueche [56] developed a relationship between the T g of a polymer solution and the diluent content based on polymer free-volume considerations. Specifically, for a polymer (p) containing a diluent (d), the glass transition temperature of the polymer / diluent system, T g , is determined from

ap fp T g,p 1 ads1 2 fpdT g,d T g 5 ]]]]]]] ap fp 1 ads1 2 fpd

(10)

where ap and ad are the differences between the thermal expansion coefficients above and below the T g of the polymer and diluent, respectively, fp is the polymer volume fraction, and T g,p and T g,d are the glass transition temperatures of the pure polymer and diluent, respectively. Eq. (10) assumes that the total free volume fraction in any glassy polymer or glassy polymer / diluent system is 0.025, as derived from considerations of polymer segmental motions at the glass transition [57]. f solid can then be determined as p

the value of fp in Eq. (10), given the inputs of ap , ad , T g,p , and T g,d , as well as the condition that T g 5258C (the ambient manufacturing temperature for our experiments). For polymers, ap is generally taken to be 4.83 10 24 per 8C, while ad for low-molecular-weight diluents is usually around 10 23 per 8C [57]. We found that the value of f solid changes by less than p 5% for a 20% change in the values of ap and ad . In addition, the resulting 5% change in f solid was found p to change the predicted AI loadings by 0.5% on average. Therefore, it is reasonable that these values for ap and ad can be utilized to evaluate the solidification criterion for any given microparticle system. T g s of polymers have been tabulated for many common types of polymers, or can be measured by differential scanning calorimetry (DSC) [58]. On the other hand, T g s for diluents, such as AIs or solvents, are often unknown and not directly measurable because they are extremely low. We found that a 20% difference between the T g s of the AI and of the solvent would result in less than a 4% change in the solid predicted f p solidification criterion. As observed above, this small change would have a relatively small effect on the predicted AI loadings. Therefore, the AI and the solvent utilized to create the microparticles are assumed to plasticize the polymer to the same extent so that they may be characterized by the same ad and T g,d . The T g,d for the AI–dichloromethane diluent considered here is taken to be the same as the T g,d of dichloromethane (278.78C) used by Li et al. [59] in their theoretical analysis of the kinetics and thermodynamics of solvent extraction / evaporation from polymeric microspheres. Finally, it should be noted that even if all the solvent evaporates from the manufacturing solution, there is always some equilibrium amount of AI loaded in the microparticles. The polymer volume fraction remaining in the microparticles under this condition represents the maximum allowable polymax mer volume fraction, f p , for the specific polymer– AI system. If the targeted f solid determined from Eq. p (10) is larger than f pmax , the polymeric droplets cannot reach the glass transition solidification point even if all of the solvent evaporates from the manufacturing solution. In this situation, the AI is

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taken to be loaded into rubbery microparticles under the new solidification criterion that f solid 5 f pmax . p

3. Materials and methods

3.1. Materials Radiolabeled 14 C-benzyl alcohol, 14 C-n-octanol, H-geraniol and 3 H-farnesol were all obtained from American Radiolabeled Chemicals. Radiolabeled 14 C-galaxolide was synthesized by Wizard Laboratories. Non-radiolabeled benzyl alcohol and n-octanol were obtained from Aldrich, while the nonradiolabeled geraniol, farnesol and galaxolide were manufactured by International Flavors and Fragrances Inc. Both PMMA and PS were obtained from Polysciences, while PLA was obtained from Cargill. Molecular weights of the polymers examined are provided in Table 3. The CTAB surfactant was obtained from Aldrich. The CMC of the surfactant was found to be 0.98 mM at 258C from surface ¨ K10T tensiometension measurements using a Kruss ter according to the procedure described in Ref. [60]. Dichloromethane was of analytical reagent grade. The ethyl acetate solvent utilized to dissolve the PMMA microparticles was also of analytical reagent grade. The Aquasol-2 scintillation cocktail was purchased from Packard Instruments. Deionized water was utilized to prepare all of the surfactant solutions. All chemicals were used as received.

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heating rate of 158C / min. Samples were cycled twice at temperatures between 0 and 1508C. The T g was determined during the second heating cycle by the midpoint in the change of slope in a plot of the specific heat of the sample versus temperature. The reported polymer T g s represent the average of measurements on three separate polymer samples, and the reported error is the 95% confidence limit in the mean.

3

3.2. Polymer glass transition temperature The polymer T g s were measured using a DSC 7 differential scanning calorimeter (Perkin Elmer). DSC assays were performed under nitrogen using a

3.3. Polymeric microparticle manufacturing Polymeric microparticles were manufactured by an emulsification solvent evaporation process. The manufacturing conditions for each polymer were selected to prevent microparticle agglomeration during production. The general procedure involved first preparing a liquid polymer solution by dissolving 0.5–1.5 g of the polymer with the AI in 5 ml of dichloromethane solvent. The amount of AI utilized corresponded to a 16.7 wt.% AI content in the polymer (for example, 1.5 g of polymer would be combined with 0.3 g of AI in the solvent). After the polymer was completely dissolved, the polymer solution was emulsified in 80 ml of either a 3 mM or a 10 mM CTAB surfactant solution by mixing at approximately 3000 rpm with a Silverson L4RTA high shear mixer for 1 min. Different surfactant solution concentrations were utilized in order to sufficiently stabilize the polymeric microparticles against agglomeration during manufacturing, as well as to investigate the effect of surfactant concentration on AI loadings in the case of the PS microparticle system. Following emulsification, the impeller of the Silverson mixer was washed with 20 ml of the appropriate surfactant solution, and the washings were added to the manufacturing solution, thus

Table 3 Physical properties of the model polymers examined: poly(lactic acid), polystyrene and poly(methyl methacrylate)a Polymer

Abbreviation

Molecular weight [g / mol]

Density [g / cm 3 ]

T g [8C]

f solid p

Poly(lactic acid) Polystyrene Poly(methyl methacrylate)

PLA PS PMMA

90,000 50,000 100,000

1.2260.05 1.0660.06 1.1860.07

4467 6264 9262

0.9260.03 0.8560.01 0.7660.01

a

Densities were determined by volume displacement at 258C, and glass transition temperatures, Tg, were measured by DSC. The characteristic solidification volume fractions, f solid , for each polymer were calculated using Eq. (10) p

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increasing the total surfactant solution volume to 100 ml. This washing step was performed in order to recover all of the AI for mass balance considerations. The emulsion was then transferred to a magnetic stirrer where the solvent was allowed to evaporate from the dispersed polymeric droplets under continuous stirring for 2–3 h. Note that stirring the emulsion for longer times did not result in any changes in the measured AI loadings.

experiments, losses of the sample during filtration were observed, resulting in a slightly poorer mass balance closure. Since filtration serves only to separate the microparticles containing loaded AI from the aqueous surfactant manufacturing solution, these losses do not affect the measured AI loadings. Finally, it should be noted that all of the reported experimental measurements represent the average of at least three repeated experiments, and the reported error is the 95% confidence limit in the mean.

3.4. Determination of AI loading AI loadings in the polymeric microparticles were determined by a radiochemical assay. The AI utilized to prepare the initial liquid polymer solution was a dilution of the radiolabeled AI in the non-radiolabeled AI. The overall activity of the AI solution depended on the AI hydrophobicity, and ranged between 0.01 and 1 mCi / g AI. The manufactured microparticle slurry was filtered through 0.45 mm cellulose ester filter paper (Millipore) in a Buchner funnel in order to isolate the microparticles from the manufacturing solution. The recovered microparticles were washed with deionized water in order to remove any AI that adhered to the surface of the microparticles during filtration, and were dried subsequently for 20 min under vacuum. The microparticles were then dissolved in 10 ml of scintillation cocktail and analyzed for radioactivity in a Packard Tri-Carb 4530 liquid scintillation counter. In the cases of the PLA and PMMA microparticles, it was not possible to dissolve the microparticles directly in the Aquasol-2 scintillation cocktail. Therefore, for PLA, the microparticles were placed in 10 ml of scintillation cocktail and heated at 508C (a temperature exceeding the T g of PLA) for 10 min in order to facilitate diffusion of the scintillation cocktail into the microparticles, as well as AI diffusion out of the microparticles. For PMMA, the microparticles were first dissolved in 3 ml of ethyl acetate prior to the addition of the scintillation cocktail. Samples of the filtrate were also analyzed for unencapsulated AI so that mass balances could be checked around the manufacturing and filtration processes. Mass balances closed within an acceptable 610% for all of the experiments conducted except in the case of n-octanol. In the n-octanol loading

4. Results and discussion

4.1. Polymer solidification criterion The measured polymer T g s are listed in Table 3. These values were utilized in Eq. (10) in order to calculate the polymer solidification volume fractions for the three polymers examined. The resulting solidification volume fractions (f solid ) are also listed p in Table 3. For polymers with a higher T g , more diluent (either AI or solvent) must be added to the polymer–diluent solution in order to lower the T g of the polymer–diluent system to the ambient manufacturing temperature. Therefore, the corresponding f solid is lower for polymers with higher T g s. p

4.2. Geraniol loading in PS microparticles Based on solving the equality in Eq. (4), subject to the mass balance constraint in Eq. (5), a profile of the predicted AI loading in the polymeric droplets can be generated as a function of the polymer volume fraction in the microparticles. Fig. 2 shows an example of the predicted loading profile for geraniol in PS microparticles manufactured at 258C. Similar loading profiles are observed for all of the different model AIs and polymers examined. In Fig. 2, f max was predicted using the developed theory to p be 0.94 in the limit where no solvent remains in the polystyrene microparticles. Note that as the solvent evaporation process proceeds, or as solvent leaves the polymeric droplet phase, the polymer volume fraction in the polymeric droplets increases. This is indicated in Fig. 2 by the direction of the solid arrow. In the initial stages of the solvent evaporation

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m Ps becomes small compared to both m Pp and m Pa , the AI loading decreases with continued solvent evaporation. The AI loading in the solid microparticles is determined from the predicted loading curve by specifying the solidification polymer volume fraction (f solid ). From Table 3, PS microparticles solidify p when enough solvent has evaporated such that f solid 5 0.85, a value that is smaller than f pmax . p Therefore, from the loading profile in Fig. 2 at a polymer volume fraction of 0.85, the predicted geraniol loading is 6.5%. Fig. 2. Example of the predicted loading profile of geraniol in polystyrene microparticles shown as a function of the polymer volume fraction remaining in the polymeric droplets. As the solvent evaporates during manufacturing, the polymer volume fraction in the droplets increases, as indicated by the direction of the solid arrow.

process (at low polymer volume fractions), the amount of solvent present in the polymeric droplets is large compared to the amount of polymer or AI present in the polymeric droplets. The AI loading calculated from Eq. (1) is, therefore, dominated by the mass of the solvent in the polymeric droplets (m Ps ), and the AI loading at the beginning of the solvent evaporation process is small due to the presence of m Ps in the denominator of Eq. (1). In this regime, m sP decreases as the solvent evaporates, and therefore, the AI loading increases. It is noted, however, that the solvent makes the polymeric droplet phase a more compatible environment for the AI by providing a favorable entropic mixing contribution to the Gibbs free energy of the polymeric droplet phase. Therefore, when less solvent is present in the polymeric droplets, the AI distributes more readily into the aqueous phase, and the mass of AI remaining in the polymeric droplets, m Pa , decreases. In Eq. (1), m Pa appears in both the numerator and the denominator. The mass of polymer in the polymeric droplets, m Pp , is generally much larger than the mass of AI loaded in the droplets or else the polymeric droplets will not solidify. As a result, m Pp dominates the masses in the denominator of Eq. (1). Therefore, decreases in m Pa affect the numerator of Eq. (1) more than the denominator, and near the end of the solvent evaporation process when

4.3. AI loadings in PS, PLA and PMMA microparticles Using the method described above in the case of geraniol loading in PS microparticles, AI loadings for the five model AIs considered here were predicted for manufacturing PS microparticles at 258C using an initial 16.7 wt.% AI content in the polymer and a 1.5 wt.% polymer content in a 3 mM CTAB surfactant solution. Under these manufacturing conditions, PS microparticles could be prepared without significant agglomeration. Fig. 3 provides a comparison of the experimentally measured AI loadings (shown by the white bars) with theoretically pre-

Fig. 3. Comparison of experimentally measured (white bars) and theoretically predicted (striped bars) AI loadings for benzyl alcohol, n-octanol, geraniol, farnesol and galaxolide in PS microparticles prepared with an initial 16.7 wt.% AI content in the polymer and a 1.5 wt.% polymer content in an aqueous 3 mM CTAB surfactant manufacturing solution. Theoretical predictions made without accounting for AI solubilization in the CTAB micelles are shown by the crossed bars for reference.

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dicted ones for two cases: (i) AI solubilization in surfactant micelles is neglected (shown by the crossed bars) and (ii) AI solubilization in surfactant micelles is treated using the micellar pseudophase approach (shown by the striped bars). Note that the maximum AI loading that can be achieved in the microparticles if all of the solvent diffuses out of the polymeric droplets and all of the AI remains in the polymeric droplet phase corresponds to the initial AI content in the polymer. For example, for the PS microparticles in Fig. 3, the maximum AI loading achievable is 16.7 wt.%. As can be seen in Fig. 3, even the predictions that do not account for AI solubilization in surfactant micelles capture the general trend of increasing AI loadings with increasing AI hydrophobicity. This trend is consistent with observations [10,24] that the hydrophobicity of the AI and, in particular, the solubility of the AI in water, has a strong influence on the loading of AIs in PS microparticles. When an AI is less soluble in the aqueous phase, it will distribute more readily into the hydrophobic PS polymeric droplets. The predictions that do not account for AI solubilization in surfactant micelles also consistently overestimate the AI loadings for the five model AIs examined, since the micelles, which compete with the polymeric droplets for AI, are not accounted for in the manufacturing solution. Fig. 3 shows that by including the effect of AI solubilization in surfactant micelles in the molecular–thermodynamic theory, the agreement between the experimentally measured and theoretically predicted AI loadings is, in general, improved. Furthermore, deviations between the theoretical predictions and the experimental measurements may be explained in the context of the discussion presented in Section 2.4. Specifically, the theory overpredicts the loading of benzyl alcohol in the PS microparticles due to an underestimation of the benzyl alcohol solubilization in the CTAB micelles. On the other hand, the theory underpredicts the loadings of the more hydrophobic AIs examined (including geraniol, farnesol and galaxolide) as a result of an overestimation of AI solubilization in the CTAB micelles. AI loadings for three of the model AIs examined (benzyl alcohol, geraniol and farnesol) were also predicted and measured in two other polymeric microparticle systems. The comparisons are shown

Fig. 4. Comparison of experimentally measured (white bars) and theoretically predicted (striped bars) AI loadings for benzyl alcohol, geraniol and farnesol in PLA microparticles prepared with an initial 16.7 wt.% AI content in the polymer and a 0.5 wt.% polymer content in an aqueous 10 mM CTAB surfactant manufacturing solution.

in Figs. 4 and 5 for PLA and PMMA microparticles, respectively. The PMMA microparticles were manufactured at 258C using an initial 16.7 wt.% AI content in the polymer and a 1.5 wt.% polymer content in a 3 mM CTAB solution. In the case of PLA, microparticles could be prepared without agglomeration only by decreasing the polymer content in the surfactant solution to 0.5 wt.% and increasing the CTAB concentration to 10 mM. Only predictions

Fig. 5. Comparison of experimentally measured (white bars) and theoretically predicted (striped bars) AI loadings for benzyl alcohol, geraniol and farnesol in PMMA microparticles prepared with an initial 16.7 wt.% AI content in the polymer and a 1.5 wt.% polymer content in an aqueous 3 mM CTAB surfactant manufacturing solution.

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that account for AI solubilization in the surfactant micelles using the micellar pseudophase approach are shown in Figs. 4 and 5. However, as in the case of the PS microparticles, it was noted that the theoretical loading predictions that do not account for AI solubilization in surfactant micelles were also found to consistently overpredict the measured AI loadings in both the PLA and PMMA microparticles. The agreement between the theoretical loading predictions (shown by the striped bars) and the experimentally measured loadings (shown by the white bars) for the three model AIs examined in the PLA and PMMA microparticles is similar to that observed for the PS microparticle system. Specifically, loadings for benzyl alcohol are overpredicted in both the PLA and PMMA microparticle systems due to an underestimation of the benzyl alcohol solubilization in the CTAB micelles. In addition, in the PLA microparticle system, the loading of farnesol is underpredicted as a result of an overestimation of the farnesol solubilization in the CTAB micelles. In the PMMA microparticle system, the theoretical loading predictions for both geraniol and farnesol agree reasonably well with the experimental measurements. Based on the evaluation of the ability of the UNIFAC–FV method to describe the AI activity coefficient in the polymeric droplet phase in Section 2.3, we would expect the predictions in the PMMA system to deviate more from the experimental measurements than in the PS or PLA microparticle systems. The better agreement observed in the PMMA microparticle system, particularly for farnesol, when compared to the PS and PLA microparticle systems, may result from a balance between poorer UNIFAC–FV predictions of the polymeric droplet phase activity coefficient and overestimation of AI solubilization in the CTAB micelles. For the three polymeric microparticle systems examined, the average deviation between the theoretical loading predictions and the experimental measurements is 1.7%. While slightly improved predictions of AI loadings in polymeric delivery systems may be made using empirical correlations [10,24,25], the predictions made using the molecular–thermodynamic theory do not require input of experimentally measured AI loadings for any of the AIs or polymers considered.

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4.4. Effect of AI solubilization in surfactant micelles on AI loading predictions Having incorporated the effect of AI solubilization in surfactant micelles into the molecular–thermodynamic loading theory, this theory can also be utilized to predict the change in AI loadings for microparticles manufactured in surfactant solutions having varying surfactant concentrations. Fig. 6 provides a comparison of theoretically predicted (shown by the striped bars) and experimentally measured (shown by the white bars) AI loadings for benzyl alcohol, geraniol and farnesol in PS microparticles prepared at 258C in either a 3 mM or 10 mM CTAB surfactant solution. The initial AI content in the polymer and the polymer content in the surfactant solution were maintained at 16.7 and 1.5 wt.%, respectively. Fig. 6 shows that the molecular–thermodynamic theory is able to predict the experimentally observed trends in the AI loadings for varying surfactant concentrations. Specifically, the loadings of benzyl alcohol are insensitive to the CTAB concentration used because this relatively hydrophilic AI is not solubilized extensively in the surfactant micelles. On the other hand, the loadings of both geraniol and farnesol decrease as the CTAB concentration increases. This results from increased AI solubilization

Fig. 6. Comparison of experimentally measured (white bars) and theoretically predicted (striped bars) AI loadings for benzyl alcohol, geraniol and farnesol in PS microparticles that were manufactured using different CTAB surfactant concentrations (3 and 10 mM) in the aqueous manufacturing solution. The initial AI content in the polymer and the polymer content in the surfactant solution were 16.7 and 1.5 wt.%, respectively.

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in the greater number of surfactant micelles present in the manufacturing solution at the higher 10 mM CTAB concentration. The AI loading predictions were found to be extremely sensitive to the estimated AI solubilization in the surfactant micelles. For example, by decreasing the logarithm of the solubilization constant for farnesol from 6.22 (as predicted using the micellar pseudophase approach) to 5.99, the farnesol loading prediction in the PS microparticles prepared in the 10 mM CTAB surfactant solution increased from 4.6 to 7.1%, which was in better agreement with the experimentally observed loading of 7.1%. Therefore, a more rigorous treatment of AI solubilization in the surfactant micelles (for example, by accounting for electrostatic effects and for the ordered structure of the surfactant and the AI in the micelles) may result in improved quantitative predictions of AI loadings.

4.5. Rational design of microparticles and optimization of AI loadings As mentioned before, the real benefit of having a theoretical framework to predict AI loadings in polymeric microparticles is to facilitate a more rational design and optimization of AI loadings in microparticles for any given AI. Examples of the types of predictions that can be made using the molecular–thermodynamic framework described in Section 2 are given below. In general, the type of AI to be utilized in a given product formulation will be specified by the manufacturer. In the examples below, we consider the delivery of geraniol as a model fragrance component. Note that we demonstrated in Section 4.3 that the developed molecular–thermodynamic framework provides reasonable predictions of geraniol loadings in three different polymeric microparticle systems. The first step in the design of the microparticle system is to select an appropriate polymer to load the AI. The overall design of the microparticle will, of course, also require consideration of the microparticle’s surface targeting and adhesion, as well as its sustained-release, capabilities. Based on considerations of these two additional capabilities, it may be possible to narrow the range of different polymers that can be utilized to manufacture the microparticle delivery system.

For example, for geraniol, one may find that the PLA, PMMA and PS polymers in Table 3 provide sustained release of geraniol at the desired rate. The developed theory can then be utilized to rank these polymers with respect to their abilities to load geraniol. Table 4 provides a comparison of the predicted ranking of geraniol loadings in the three polymers for an arbitrarily defined manufacturing system having an initial 16.7 wt.% geraniol content in the polymer and a 1.5 wt.% polymer content in a 5 mM CTAB surfactant manufacturing solution. Note that geraniol is loaded to varying extents within each polymer due to differences in the polymer densities, the number of repeat units in each polymer, and the enthalpic interactions between the structural groups in geraniol and in the polymer. Based on olfactory analyses, one may find that a 10% geraniol loading in the microparticles is required in order to exceed the odor threshold for geraniol without producing too intense an aroma. From the predicted geraniol loadings in Table 4, PMMA would then be selected as the best polymer to achieve the targeted 10% loading. After an appropriate polymer has been selected for the microparticle matrix, the theory can then be utilized to vary the selection of other manufacturing ingredients to determine their effects on the geraniol loadings. For example, in the rankings provided in Table 4, dichloromethane was utilized as a solvent for the polymer. However, due to increasing environmental concerns, the use of halogenated solvents may be undesirable. A more environmentally friendly solvent that could be utilized to manufacture the PMMA microparticles to load geraniol is ethyl acetate. This solvent is similar in volatility to dichloromethane. Based on the theory, the loading of geraniol in PMMA microparticles was predicted to Table 4 Predicted geraniol loadings in polymeric microparticles prepared with either poly(lactic acid), poly(methyl methacrylate) or polystyrene at 258C with an initial 16.7 wt.% AI content in the polymer and a 1.5 wt.% polymer content in an aqueous 5 mM CTAB surfactant manufacturing solution Polymer

Predicted loading (%)

Poly(lactic acid) Polystyrene Poly(methyl methacrylate)

5.4 5.7 11.2

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increase from 11.2% when using dichloromethane to 11.6% when using ethyl acetate. The geraniol loading is therefore not significantly affected by the use of ethyl acetate as a dichloromethane solvent replacement. This is due to the small amount of residual solvent remaining in the polymeric droplets when they solidify. At these small solvent concentrations in the polymeric droplets, the solvent character does not strongly influence the equilibrium distribution of geraniol between the polymeric droplets and the surrounding aqueous surfactant solution. Similar considerations can be made with respect to the continuous phase, although water is commonly utilized as the continuous phase medium because it is inexpensive, environmentally friendly and readily available. With the selection of the more environmentally friendly ethyl acetate solvent and aqueous continuous phase, the manufacturing temperature and ingredient concentrations can then be optimized in order to tune the predicted geraniol loading of 11.6% in the PMMA microparticles to the targeted 10% loading. Temperature appears explicitly in the residual contribution to the activity coefficient, g Ra , in the context of the UNIFAC and UNIFAC–FV group-contribution methods through the normalization of the interaction parameters between different structural groups by the thermal energy of the system. Since microparticles are typically manufactured over a moderate temperature range, the use of temperatureindependent interaction parameters in the developed loading theory is reasonable. The variation in predicted geraniol loadings in the PMMA microparticles with temperature for temperatures ranging between 10 and 908C (where the aqueous manufacturing solution will neither freeze nor boil) is shown in Fig. 7. As the manufacturing temperature increases, the loading of geraniol in the PMMA microparticles decreases. The loss of water structure at the higher temperatures results in the weakening of the hydrophobic effect, and enables the greater dissolution of geraniol in the water [61]. The apparent break in the geraniol loading profile at approximately 608C results from a change in the solidification behavior of the microparticles. At the higher manufacturing temperatures, f solid estimated from Eq. (10) exceeds p f max , and consequently, geraniol is loaded within p

95

Fig. 7. Predicted variation of geraniol loadings in PMMA microparticles manufactured at varying temperatures. The initial AI content in the polymer and the polymer content in an aqueous 5 mM CTAB surfactant manufacturing solution were 16.7 and 1.5 wt.%, respectively.

rubbery polymeric microparticles that do not contain any residual solvent. The presence of solvent in the microparticles manufactured at temperatures below 608C creates a stronger temperature-dependency of the AI loading, since the mass of solvent remaining in the microparticles, m sP , enters directly in the calculation of the AI loading through the denominator of Eq. (1). From Fig. 7, the targeted 10% loading of geraniol in the PMMA microparticles can be achieved by manufacturing the microparticles at higher temperatures (such as around 408C instead of at 258C). On the other hand, operating at higher temperatures will increase the energy costs associated with manufacturing the microparticles. It may be more desirable instead to consider achieving the targeted geraniol loading by changing the concentrations of the ingredients used to manufacture the microparticles. Figs. 8–10 show the predicted geraniol loadings at 258C for varying the geraniol content in the polymer, the polymer content in the continuous phase and the surfactant concentration in the continuous phase, respectively. In Figs. 8 and 9, the geraniol loading in the PMMA microparticles increases as the geraniol content in the polymer or the polymer content in the water increases. Increasing the total amount of geraniol in the manufacturing system increases the geraniol loading in the PMMA microparticles simply

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Fig. 8. Predicted variation of geraniol loadings in PMMA microparticles that were prepared at 258C using different initial amounts of AI in the polymer. The polymer content in the aqueous 5 mM CTAB surfactant manufacturing solution was maintained at 1.5 wt.%.

because the quantity of geraniol in the system increases. In order to achieve the targeted 10% geraniol loading, either the geraniol content in the polymer or the polymer content in the water could be decreased (for example, by lowering the geraniol content in the polymer to around 15 wt.% or lowering the polymer content in the water to approximately 1 wt.%). The geraniol loading in the PMMA microparticles, on the other hand, decreases with increasing CTAB surfactant concentration, as shown in Fig. 10. At

Fig. 9. Predicted variation of geraniol loadings in PMMA microparticles that were prepared at 258C using different polymer contents in an aqueous 5 mM CTAB surfactant manufacturing solution. The initial AI content in the polymer was maintained at 16.7 wt.%.

Fig. 10. Predicted variation of geraniol loadings in PMMA microparticles that were prepared at 258C using different CTAB surfactant concentrations in the aqueous manufacturing solution. The initial AI content in the polymer and the polymer content in the manufacturing solution were maintained at 16.7 and 1.5 wt.%, respectively.

higher surfactant concentrations, more geraniol will be solubilized in the greater number of micelles present in the manufacturing solution. It should be noted that the surfactant concentration will also directly affect the resulting microparticle size and surface charge density. Both characteristics are expected to have a strong influence on the microparticle’s surface targeting and adhesion capabilities. Therefore, when optimizing the geraniol loading with respect to the surfactant concentration, one needs to balance the effect on the geraniol loading with the effect on the microparticles’ adhesion to a targeted surface. In any case, in the given example, the targeted 10% geraniol loading can be achieved by increasing the CTAB surfactant concentration to approximately 10 mM. Finally, although in the examples provided above we have considered variations in the manufacturing temperature and in the ingredient concentrations independently, the theory can also be utilized to systematically probe the effects on several process variables simultaneously. For example, the theoretically predicted AI loadings could replace time-consuming experimental measurements as inputs to a statistical factorial design analysis [62]. In summary, we have demonstrated how the developed molecular–thermodynamic loading theory may be utilized to rationally design the loading of

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geraniol in a PMMA microparticle delivery system. We have also illustrated how the theory may be utilized to conduct a systematic optimization of the geraniol loading in PMMA microparticles by varying different process variables. This type of optimization could significantly reduce the amount of time required to conduct an experimental optimization of the manufacturing process.

5. Conclusions A molecular–thermodynamic theory was developed to describe the loading of AIs in polymeric microparticles prepared by a solvent evaporation manufacturing process. The theory considers the equilibrium distribution of the AI between dispersed liquid polymeric droplets, water and surfactant micelles that may be present in the manufacturing solution. The nonidealities in the water and in the polymeric droplets were treated using the UNIFAC and UNIFAC–FV group-contribution methods, respectively. AI solubilization in surfactant micelles was modeled by treating the micelles as a bulk surfactant liquid pseudophase. The use of group-contribution methods in the developed theory permits the prediction of AI loadings in microparticle systems for which only the chemical structures, molecular weights, densities and masses of the components present are known. No experimentally measured AI loadings in polymeric microparticles are utilized as inputs to the theory. The theory was shown to provide reasonable quantitative predictions of AI loadings in PLA, PMMA and PS microparticles that were prepared by a solvent evaporation manufacturing process, and to predict the observed trend of decreasing AI loadings with increasing surfactant concentration in the manufacturing solution. The theory is useful in that it can be utilized to first screen materials with respect to their abilities to load a given AI. Examples were provided to illustrate how the theory may be utilized to select other ingredients in the manufacturing solution, as well as to systematically optimize the AI loading in the microparticle system with respect to the manufacturing temperature and the ingredient concentrations. Improved quantitative agreement between the

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theoretically predicted AI loadings and the experimentally measured ones is expected if the description of AI solubilization in the micelles can be improved. Future work will investigate the implementation of more rigorous descriptions of AI solubilization that account for electrostatic effects and for the ordered structure and orientation of the surfactant and the AI in the micelles. In addition, in order to generalize the developed theory for other microparticle manufacturing processes, future work will extend the developed theory to predict AI loadings in microparticles prepared by the hot melt– freezing process. Finally, AI mixtures are commonly utilized in practical controlled-delivery applications. Therefore, future work will also include extension of the developed theory for multicomponent AI mixtures, beginning with an examination of binary AI mixtures.

Acknowledgements Ginger Tse is grateful for the award of an NSF Graduate Fellowship. We are also grateful to International Flavors and Fragrances Inc. for providing materials and financial support for this work. We further thank Dr. Craig Warren for insightful discussions on fragrance science.

Appendix A. Mass balance constraint and relation to AI mole fractions The mass balance needed in order to solve Eq. (4) provides a constraint on the AI masses (m Ia ) in the polymeric droplets (I5P), water (I5W) and the surfactant micelles (I5M), that is, m TOTAL 5 m aP 1 m aW 1 m aM a

(A.1)

where m TOTAL , the total mass of AI in the manufaca turing solution, is known. Eq. (A.1) is written in terms of the AI masses in each respective phase; however, the relevant concentration variables in Eq. (4) are the AI mole fractions in each phase (x Pa , x W a and x M a ) and the polymer mole fraction in the polymeric droplet phase (x pP ). In this appendix, we derive the relationships that relate the three unknown

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masses in Eq. (A.1) to the relevant concentration variables in Eq. (4). First, the water phase contains only two components: AI (a) and water (w), and therefore, from the definition of the mole fraction, it follows that mW a /Ma W x a 5 ]]]]] W m a /Ma 1 m w /Mw

(A.2)

where Mi is the molecular weight of component i (i5a or w), and m w is the mass of water in the manufacturing solution. If the mass of water in the manufacturing solution (m w ) is given, Eq. (A.2) can W then be utilized to directly relate m W a to x a . Next, the micellar phase consists of only two components: AI (a) and micellized surfactant (m), and therefore, it follows that mM a /Ma M x a 5 ]]]]] M M m a /Ma 1 m m /Mm

(A.3)

where m M m is the mass of micellized surfactant, and Mm is the surfactant’s molecular weight. Note that mM m can be evaluated from the specified total surfactant concentration utilized in the manufacturing solution, Cm (in molar units), whereby the distribution of surfactant between the monomers and the micelles is determined from the CMC of the pure surfactant, CCMC (also in molar units). While, in reality, AI solubilization in surfactant micelles could change the value of the pure surfactant’s CMC [63,64], the AI loading predictions were found to be insensitive to small variations in the precise value of the CMC. The mass of micellized surfactant can then be evaluated from mw M m m 5sCm 2 CCMCd 3 ] 3 Mm rw

(A.4)

where rw is the density of water at the manufacturing temperature. Therefore, given the total surfactant concentration in the manufacturing solution (Cm ), Eqs. (A.3) and (A.4) provide a direct relationship M between m M a and x a . Finally, in the polymeric droplets, three components are present: polymer (p), AI (a) and a residual amount of solvent (s). From the definition of the mole fraction, it follows that

m Pa /Ma P x a 5 ]]]]]]]] P m a /Ma 1 m p /Mp 1 m s /Ms

(A.5)

The mass of polymer utilized in the manufacturing process (m p ) is generally specified. The amount of residual solvent remaining in the microparticles (m s ), on the other hand, is not known a priori. However, we note that in Eq. (4), there is a second concentration variable in the polymeric droplet phase, x Pp , which can be expressed in terms of the relevant masses as m Pp /Mp x 5 ]]]]]]]] m aP /Ma 1 m p /Mp 1 m s /Ms P p

(A.6)

Therefore, for the polymeric droplet phase, there are two equations [Eqs. (A.5) and (A.6)], which can be solved iteratively to relate m Pa to x Pa and m s to x Pp . In summary, Eqs. (A.2)–(A.6) provide relationships between the three AI masses in the mass balance constraint in Eq. (5) and the relevant concentration variables in Eq. (4) without introducing any additional unknown variables. It should be noted that the mass balance constraint requires input of the total masses of the polymer, the AI and the water, as well as of the total surfactant concentration, utilized in the manufacturing process.

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