A comprehensive framework for surfactant selection and design for emulsion based chemical product design

Accepted Manuscript Title: A Comprehensive Framework for Surfactant Selection and Design for Emulsion Based Chemical Product Design Author: Michele Mattei Georgios M. Kontogeorgis Rafiqul Gani PII: DOI: Reference:

S0378-3812(13)00614-6 http://dx.doi.org/doi:10.1016/j.fluid.2013.10.030 FLUID 9844

To appear in:

Fluid Phase Equilibria

Received date: Revised date: Accepted date:

23-7-2013 11-10-2013 16-10-2013

Please cite this article as: M. Mattei, G.M. Kontogeorgis, R. Gani, A Comprehensive Framework for Surfactant Selection and Design for Emulsion Based Chemical Product Design, Fluid Phase Equilibria (2013), http://dx.doi.org/10.1016/j.fluid.2013.10.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Highlights  A systematic framework for the selection and design of surfactants is presented  A GC-based model for cloud point is proposed

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 The application of the framework is highlighted through two case-studies

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 Case-studies involve the design of emulsified sunscreen and hand-wash

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A Comprehensive Framework for Surfactant Selection

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and Design for Emulsion Based Chemical Product

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Design

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Michele Mattei a,b, Georgios M. Kontogeorgis b, Rafiqul Gani a,*

Computer Aided Process-Product Engineering Center (CAPEC), Department of Chemical and

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Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 229, DK-2800, Kgs. Lyngby, Denmark

Center for Energy Resources Engineering (CERE), Department of Chemical and Biochemical

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b

*

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Denmark

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Engineering, Technical University of Denmark, Søltofts Plads, Building 229, DK-2800, Kgs. Lyngby,

Corresponding author: Rafiqul Gani: e-mail address: [email protected]; phone number: +45 45 25 28 82;

fax number: +45 45 93 29 06 ABSTRACT

The manufacture of emulsified products is of increasing interest in the consumer oriented chemical industry. Several cosmetic, house-hold and pharmaceutical products are in the emulsified form when sold and/or they are expected to form an emulsion when used. Therefore, there is a need for the

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development of a methodology and relevant tools in order to spare time and resources in the design of emulsion-based chemical products, so that the products can reach the market faster and at a

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reduced cost. The understanding and modeling of the characteristic behavior of emulsions and their peculiar ingredients is consequently necessary to tackle this problem with computer-aided methods

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and tools. A comprehensive framework for the selection and design of surfactants, the main

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responsible for the formation and the stability of emulsions, is presented here together with the modeling of the cloud point, a key-property of nonionic surfactants, with a group-contribution model.

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The mathematical formulation of a standard product design problem is presented, together with the list of both the pure component properties (related to nonionic surfactants) and the mixture

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properties (relevant to the overall products as an emulsion) needed for the solution of the design

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algorithm. These models are then applied together with established predictive models for pure

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component properties of ionic surfactants and for standard mixture properties such as the density, the viscosity, the surface and the interfacial tension, but also the type of emulsion expected (through

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the hydrophilic-lipophilic balance), its stability (through the hydrophilic-lipophilic deviation), etc., forming a robust chemical product design tool. The application of this framework is highlighted for the design of some emulsion based chemical products. KEYWORDS: Product design, emulsions, property prediction, cloud point 1. CHEMICAL PRODUCT DESIGN Recently, a substantial shift is observed from materials valued for their purity, to materials sold for their performance behavior [1], [2]. To meet these challenges, on a global and local scale, while

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remaining profitable and maintaining sustainable growth, there has been an increasing interest in the formulation and solution of product design problems [3]. The chemical product tree shown in Figure 1

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gives an idea of the size of this shift: the roots of the tree consist in a limited number of Raw Materials which are processed to obtain the commodity products (Basic Products). Specialty

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chemicals (Intermediate Products) are then manufactured from the commodities and finally the

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leaves of the tree represent a large portfolio of higher value products (Refined Chemicals & Consumer Products) obtained by processing and/or combining the chemicals of the previous product classes. As

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one ascends the product tree, the number of products belonging to each category grows exponentially from around 10 for the raw material class, up to almost 30000 in the last class of higher

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value added products. This last class is composed of formulations, devices and technology based

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consumer goods. Formulated products include pharmaceuticals, paints, food, cosmetic, detergents,

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pesticides, in which 5 to more than 20 ingredients are usually present, representing a wide range of chemical compounds such as polymers, surfactants, solids, solvents, pigments, aromas and so on [4].

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The common practice, in the development of such products, is still the experiment-based and trialand-error approach. However, a systematic procedure, able to design a higher added value product with enhanced product qualities, represents an efficient alternative, with respect to time and resources, speeding up the product development. FIGURE 1 HERE 1.1 FORMULATION DESIGN

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Many chemical-based personal care products of everyday life such as sun lotions, shower creams, insect repellents, etc. are liquid formulations, while examples of non-personal care products are

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paints, pesticides and drugs. These can be classified as “consumer-oriented products” since their needs, on the basis of which they are designed, are defined by the consumers. Therefore these

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products need to satisfy multiple needs of the consumers [5]. A sunscreen lotion, for example, must

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provide protection against sunburns and skin cancer, but must also prevent skin aging, and be, for example, long lasting, safe, easily applicable, with good sensorial properties (color and odor) [6].

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Because a single chemical is unlikely to satisfy these multiple needs, a blend of several chemicals is usually sought. A formulation may then contain materials from different classes of chemicals, such as

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polymers, surfactants, solvents, pigments and aromas. These classes of chemicals are usually

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classified as follows [5]:

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 Active ingredients: these chemicals are the most important ones in the formulation, because they

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satisfy the main needs of the product, thus defining the function of the product itself. For example, the function of a sunscreen lotion is to protect the skin against the UV radiation.  Solvent mixture: it is usually present in high concentration in the formulation and has the function of dissolving the active ingredients and other chemicals in the formulation, ensuring the product to be a single liquid phase and to be properly delivered. The solvent mixture must evaporate after application.

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 Additives: these chemicals are usually present in low concentration and they satisfy the secondary needs of the product, enhancing the end-use product properties. Examples are pigments and

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aromas, to enhance the sensorial properties of the formulation. The presence of several classes of chemicals to be included in the formulation design leads to the

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necessity of a step-by-step hierarchical design methodology, in order to avoid any combinatorial

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explosion due to the high number of possible candidate formulations to be generated and screened,

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systematically and efficiently, while at the same time excluding “blind” trial-and-error solutions. Several methodologies and frameworks have been developed, in order to address the need for the

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solution of a formulation design problem, with the aid of adequate property models and computeraided tools. Raman and Maranas [7] addressed the problem incorporating topological indices for

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correlating the necessary physico-chemical properties, while Chemmangattuvalappil et al. [8] applied

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combined property clustering and group-contribution techniques. Teixeira et al. [9] directed their

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attention towards structured products (more specifically microencapsulate perfumes for textile application), while Charpentier [10] focused on the multi-scale problem generated by the introduction in the methodology of economic, social and environmental constraints. Figure 2 shows the work-flow diagram of the computer-aided design/verification stage, based on “define target – match target” paradigm as presented by Conte et al. [5], highlighting input, output and tools used for each step. The methodology employs the “reverse design” technique. The defined target properties of the product are then the known variables and input for the property models. Appropriate property models are needed to estimate the target properties of the candidates so that they are evaluated and then accepted or rejected. At the same time the mixture compositions that satisfy the product constraints

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are determined, using suitable mixture property models as well as phase stability algorithms. As shown in Figure 2, if in any task a solution is not found, it is possible to return to a previous task to

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refine the problem definition.

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1.2 EMULSION DESIGN

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Formulations can also have other physical forms [11]: suspensions containing insoluble chemicals dispersed in the liquid with the help of a dispersant; emulsions where solid constituents have been

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emulsified through selected emulsifiers together with solvents and additives; solid products such as pharmaceutical tablets or soap bars. In chemical product design, Cussler and Moggridge [2]

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distinguish between commodities, chemical devices, molecular products and microstructured products, where the term “microstructure” refers to a chemical organization on the scale of

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micrometers, belonging to the colloidal domain and incorporating polymer solutions, foams, gels and emulsions. The performances of such products are related not only to the presence of active

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ingredients and additives in the formulation, but also to the product’s structural and material properties [12]. Among the microstructured products, emulsified products are the most relevant, particularly in the food and cosmetic industries. Emulsions are defined as mixtures of two normally immiscible liquids, kinetically stabilized by emulsifiers (most often surface active agents, better known as surfactants) that lie on the interface between the two phases. Active ingredients and additives are usually dissolved in the continuous and/or dispersed phases, according to the needs of the products. Bernardo and Saraiva [13] proposed a simultaneous approach to address product and process design, with special attention to cosmetic emulsions, while Bagajewicz et al. [14] extended a generic

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approach [15] to consider price-competitive markets. Recently, a systematic procedure, which is applicable to the design of emulsified formulated products, has been proposed by Mattei et al. [16]

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and it is further extended in this work (see Figure 2).

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FIGURE 2 HERE

For emulsion-based chemical product design, the solvent(s) design task (Task 3a) provides as output

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two non-miscible liquid phases and an additional task (Task 3b: Surfactant(s) design) is needed.

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Necessarily, some of the models applied for the definition of the target properties might differ when considering an emulsified product, rather than a homogeneous formulation. In particular, surfactants

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are key chemicals in most emulsified formulations and a wide range of peculiar properties need to be

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considered when designing or selecting chemicals such as surfactants [17].

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2. SURFACTANT SELECTION AND DESIGN

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A surfactant design problem does not differ from any other molecular and/or mixture/blend design problem and it can therefore be described through the following generic mathematical representation [3]:

(1) (2) (3) (4) (5)

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(6)

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(7)

In the above equations, x represents the vector of continuous variables (such as mixture

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compositions), y the vector of binary integer variables (such as compound identity), hi(x) is a set of

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equality constraints (related to molecular structure generation, chemical feasibility rules, etc.), gi(x) is a set of inequality constraints (related to environmental constraints and/or special property

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constraints) and FOBJ is the objective function (to be maximized) on the basis of which the design

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choices are taken. In the specific case of surfactant design, the numerical constraints presented in the equations above depend on the problem definition, such as on the need for the surfactant system

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into the final product. In fact, the main application of this category of chemical is the emulsion

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stabilization, while surfactants can also be found in commercial products to form microemulsions

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(particularly common in the pharmaceutical industry) or as active ingredients (as previously defined) in detergents. Other formulated products which are not in the emulsified form have key-properties strongly influenced by the surfactant content: surfactants are usually added to formulations with the goal of preventing the re-deposition of dispersed chemicals (as in toothpastes) or of reducing the surface tension of the product (as in enhanced oil recovery fluids). Each of the various needs generate a different product design problem and while it can be mathematically described with the same set of equations presented above, the form of hi(x), gi(x) and FOBJ is different and the property models needed are also different in each case. 2.1 SURFACTANT PROPERTY MODELS

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Surfactants are characterized by an amphiphilic nature, which means that a part of them is hydrophilic (water-like), while another part is hydrophobic, or lipophilic (oil-like). In order to describe

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their behavior in relation to two non-miscible phases and the range of temperatures at which they are active, some non-conventional properties, such as cloud point and critical micelle concentration, are

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needed. It should be noted, however, that model-based product design methodologies for emulsified

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products have not been yet developed to a level as those for homogeneous formulations, and so predictive models for some of the needed properties are missing. However, although many of these

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properties are strictly mixture properties since they refer to the mutual behavior of surfactants in water mixtures, they can be modeled as pure component properties, depending only on the

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molecular structure of the surfactant involved, since either the temperature or the composition are

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usually kept constant. Hence these properties are modeled as primary properties and can be, as a first

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approximation, estimated using group-contribution methods. The set of properties needed for surfactant selection and design in an emulsion-based chemical product design problem and the

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models used for their estimation are summarized in Table S1 for primary properties and in Table S2 for secondary properties, in the Supplementary Material. The models used to estimate the properties may be classified, for each class of properties, into those that are predictive by nature and those that are not. For example, estimating properties only from molecular structural information involves predictive models, such as the group-contribution methods, while estimating properties from compound specific coefficients involves the use of correlation which are not predictive by nature. In chemical product design, both types of models are needed. During the evaluation of candidate products, the models need to be predictive and computationally fast and

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cheap, while during the verification of a small number of candidates, correlative models may be used, if the correlation coefficients are available. During the evaluation stage, the models need to be, at

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least, qualitatively correct, while during the verification stage, the models also need to be quantitatively correct. Group-contribution methods are extensively considered here since they only

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need the molecular structure of the pure component and they exhibit a good accuracy together with

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a wide range of applicability. In the Marrero and Gani group-contribution model [18], the property estimation is performed at three levels, and the property prediction model has the form reported

(8)

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below:

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Where f(X) is a function of the property X and it may contain adjustable model parameters depending

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on the property involved. In (8), Ci is the contribution of the first-order group of type-i that occurs Ni times. Dj is the contribution of the second-order group of type-j that occurs Mj times. Ek is the

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contribution of the third-order group of type-k that occurs Ok times. The definition and identification of the second-order groups has a theoretical basis, where the principle of conjugation has been employed [19]. The criteria used for the identification of third-order groups are analogous to those used for second-order groups except for the types of compounds intended to be represented [18]. The role of the second and third-order groups is to provide more structural information about the portions of the molecular structure of a compound, where the description through the lower-order groups is insufficient. Through this addition, obstacles as partial description of the proximity effects and the lack of distinction between isomers can be overcome. The ultimate objective of this multilevel

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scheme is to enhance the accuracy, reliability and the range of application of the model [18]. For determination of contributions Ci, Dj and Ek, Marrero and Gani [18] suggested a multi-level approach.

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As an alternative to the step-wise regression method, a simultaneous regression method can be applied, in which the regression is performed by considering all the terms containing first, second and

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third-order groups in a single regression step.

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When addressing the reverse problem with group-contribution models, computer-aided tools are necessary in order to reliably and quickly identify the chemical satisfying the target properties

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defined. It is also necessary, then, to provide connectivity rules so that the groups collected can be combined in all the possible legal combinations. When applying to surfactants, this stage becomes

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very relevant since it is not only necessary to make sure to connect groups so that no free

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attachments are left in the molecules generated, but also that two distinct moieties (the hydrophilic

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and lipophilic parts) are generated. This introduces a number of extra-constraints to be satisfied in

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order to generate acceptable surfactant candidates. 2.2 MIXTURE PROPERTIES

The design and selection of surfactants for chemical product design need pure component properties as well as mixture properties. In fact, the interaction of the chosen surfactant with the other constituents of the product needs to be evaluated; in the specific case of emulsified products, particularly, the interaction of surfactants with both the water and the oil phases are of great importance. Proper mixture property models which are able to relate the formulation properties to those of the single ingredients are needed. A comprehensive list of the models used is provided in Table S3 in the Supplementary Material.

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As listed in Table S3, for some mixture properties linear mixing rules can be used to predict the property. For the generic mixture property ζ, the mixture property model based on a linear mixing

(9)

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rule is described by the following equation:

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Where xi is the composition of compound i in the mixture, and ζi the pure component property. Linear property models give good predictions of mixture properties for chemical systems

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characterized by negligible excess properties of mixing. For those chemical systems having large excess properties of mixing, more detailed models are needed. Emulsions are systems far from the

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ideality, and therefore dedicated models for the estimation of some of the mixture properties (listed

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in the right column of Table S3) are necessary. Since these models are quite complex, they can hardly

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be employed as models for screening of alternative designs, but they should be rather used in the verification stage, or on a second step, when the search space for the candidates has been reduced by

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the application of proper constraints on other mixture properties. 3. MODELING OF THE CLOUD POINT OF SURFACTANTS WITH A GROUP-CONTRIBUTION METHOD One of the surfactant-related pure component properties considered necessary for the development of a model-based methodology for surfactant design is the cloud point, sometimes called the cloud temperature. This property is specific of mixtures between water and nonionic surfactants and it is defined as the temperature at which the mixture starts to phase separate and two phases appear, thus becoming cloudy [33]. This phenomenon is of particular relevance for surfactants containing polyoxyethylene chains (thus nonionic surfactants), exhibiting reverse solubility versus temperature

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behavior. This is clearly visible in Figure 3, where a standard phase behavior of an aqueous mixture of a polyoxyethylene-based nonionic surfactant is reported.

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FIGURE 3 HERE

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In Figure 3, different regions can be recognized: L1 identifies an aqueous surfactant solution where the surfactant is organized in ordinary or reverse spherical micelles; W represents a very diluted

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surfactant solution (around the critical micelle concentration); S indicates the presence of solid

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surfactant; Lα, H1 and V1, instead, are regions where the surfactant is aggregated in “uncommon” structures such as, respectively, lamellar, normal hexagonal and bi-continuous cubic structures. The

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last three regions are sometimes grouped together as “viscous” meso-phases, since their rheological properties and behavior are substantially different from those of the ordinary and reverse spherical

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micellar solution. The line determining the W + L1 two-phase area is also known as the cloud point

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line. It is evident that, as a mixture property, the cloud point does not depend only on the system

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considered, but it is influenced by the surfactant content. However, it is common practice to define as cloud point the numerical value assumed by the cloud point curve at a surfactant weight percentage of 1%, measured by visual observation method: the temperature at which the visible solubility changes to cloudy over a range of 1°C or less is taken as cloud point [35]. Several efforts have been made in order to develop a model to predict the cloud point, only on the basis of the molecular structure of the surfactant involved and the quantitative structure-activity relationship (QSPR) models have been extensively applied [35], [36], [37]. The authors, on the other hand, are not aware of any group-contribution based method developed in relation to this property. These methods, however, apply very well to chemical process and product design because they can

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provide accurate predictions without being computationally demanding. Moreover, they can be used in computer-aided molecular design because they employ the same building blocks for molecular

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representation [24]. In this work we have applied the Marrero and Gani GC-method [18] to this property.

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3.1 DATA-SET

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An original data-set consisting of 86 nonionic surfactants have been collected from different sources [35], [36], [37], [38]. The data set contains linear alkyl, branched alkyl, alkyl phenyl ethoxylates,

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carbohydrate-derivative ethoxylates, alkyl polyoxyethylene-polyoxypropylene copolymers and ethoxylated amides. All experimental data are measured by visual observation method in 1% aqueous

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surfactant solutions and are reported in Table 1, divided in different classes.

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TABLE 1 HERE

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Before applying the Marrero and Gani CG-method to the data-set chosen for the parameter

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estimation step, it is necessary to analyze the matrix of group occurrences to make sure that each groups describes at least two of the surfactants presents in the data-set. A single occurrence would actually distort the performance of the model, leading to a perfect match for the compounds with those groups, providing uncertain extrapolation capabilities. Moreover, some of the data of Table 1 are excluded from the data-set since their experimental value for the cloud point is inconsistent with other values and are therefore identified as outliers. The outliers are identified as they are inconsistent with the assumption that the cloud point of linear alkyl ethoxylates increases with increasing length of the ethoxylated chain and with decreasing length of the carbon chain. These surfactants whose CP values are excluded are highlighted in grey in Table 1.

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3.2 MODEL DEVELOPMENT In order to determine the most suitable form of f(X) of the constitutive equation of the Marrero and

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Gani method, as in Equation 8, it is necessary to observe the trend of the experimental data of the property to be estimated as a function of the main representative groups of the chemicals under

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investigation. Considering the largest family of nonionic surfactants: the linear alkyl ethoxylates, the

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trend of the cloud point as a function of the number of ethoxylate groups (CH2CH2O) in the hydrophilic chain is analyzed, as shown in Figures S1 and S2 in the Supplementary Material.

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As seen in Figure S1, the dependence of the cloud point on the number of ethoxylate groups of linear alkyl ethoxylates is not linear. On the other hand, Figure S2 shows that the dependence of the square

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of the cloud point is linear. This justifies then the choice of the form of f(X), as in the following

(10)

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Equation 10:

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Where the cloud point is expressed in K. Given the equation above, in order to represent the remaining 72 compounds, 13 first order groups and 1 second order group are needed, according to the original set of parameters by Marrero and Gani [18]. The results of the parameter estimation step performed through the step-wise regression method are illustrated in the parity plot of Figure 4. FIGURE 4 HERE

The results in Figure 4 indicate that the accuracy of the Marrero and Gani GC-methods using only first and second order groups is not satisfactory, as quantified in Table S4 in the Supplementary Material.

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In particular, the maximum absolute errors (column AADmax) are too high for many categories of surfactants considered to consider the model reliable enough to be implemented in the product

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design methodology. It is therefore considered necessary to include new third order groups in the set of parameters, in order to improve the performances of the method, as described in [24] and [39], in

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particular relation to those compounds for which the correlation indices were poor: branched alkyl

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ethoxylates and carbohydrate-derivate ethoxylates primarily. According to Marrero and Gani [18], in fact, the second order groups are strictly defined and one cannot arbitrarily add new second order

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groups as one can do with third order groups, instead. A step-by-step systematical data-error analysis as in [24] has been performed, generating 5 new third order groups taking care of complex structures

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peculiar of the available data-set. The introduction of structural parameters, in order to take into

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account the peculiar molecular assemblies of the nonionic surfactants considered, as third order

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groups is necessary since the original sets of first and second order groups of the Marrero and Gani method cannot be arbitrarily modified.

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3.3 RESULTS AND DISCUSSION OF RESULTS

Once the new set of groups has been identified, a final parameter regression is performed, where all the group contributions are estimated simultaneously. The parameter values are reported in Table 2, while the performances of the improved methods are given in Figure 5, as a parity plot and in Table S5 in the Supplementary Material as statistical indices. The simultaneous approach gives better model performances, compared to the step-wise approach, and it is therefore preferred. Obviously, when this approach is chosen, the absolute values of the third order contribution might be comparable or even exceed those of the first and second order groups.

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TABLE 2 HERE

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FIGURE 5 HERE It has to be noted that the only second-order group used in this work, AROMRINGs1s4, has a zero

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contribution since the regression performed with the simultaneous method did not find any non-zero

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value for it. Figure S3 in the Supplementary Material highlights the effect of the addition of dedicated third-order groups in the reduction of the absolute error of the model. In particular, it can be noticed

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that only 2 calculated values clearly differ of more than 10 K after the introduction of the third order groups, while in the regression with only first and second order groups 11 correlations show high

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absolute errors.

By comparing the results before and after the addition of the new dedicated third order groups, it can

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be seen that evident improvements have been achieved. In particular, after the addition of the third order groups, linear and branched alkyl ethoxylates show improved statistical indices, and in general

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the absolute errors have been strongly reduced. These results represent an improvement also if compared with those obtained with different QSPR methods [35], [36], [37], as shown in Table 3. TABLE 3 HERE

An example of the application of the model developed here is given in Table A1 in the Appendix. The availability of more reliable experimental data regarding cloud points or nonionic surfactants belonging to other families (such as alkanediols, ethers, esters and fluorinated linear ethoxylates) will broaden the application range of the model. However, it can already be safely applied in the

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surfactant design methodology considering the limited maximum error, with basic limitation the molecular structures available.

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4. CASE STUDIES

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The framework presented in Figure 2, together with the property models reported in chapter 2.1 and 2.2 have been applied to two different surfactant design and selection problems, as a part of

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emulsion-based chemical product design. Also the newly developed group-contribution models for

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the prediction of the cloud point and the critical micelle concentration [24] have been applied as a comprehensive set of group-contribution models regarding nonionic surfactants, forming a reliable

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and consistent tool for the surfactant design. Table 4 lists the properties needed by the methodology and the models used in this work.

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TABLE 4 HERE

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All the property models reported in Table 4 have been tested against experimental values in order to verify their reliability. The values relative to the coefficients of determination of the different models are high, except for the Hansen solubility parameters and the toxicity parameter. However, there are no other predictive models with comparable correlation performances, therefore they are applied for screening of alternative designs, and more accurate and complex models are employed only a few alternatives are available, to verify the reliability of the predictions. Once these models have been verified, they have been implemented in the methodology for chemical product design, as described in the previous chapters. The first case study presented is about the design of an emulsified UV sunscreen, where the

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surfactant system is necessary as emulsifier, that is to stabilize the formulation. The second case study, on the other hand, is about the design of an emulsified hand-wash, where the surfactant

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system acts simultaneously as active ingredient, as a main function, and as emulsifier. This contribution, however, is not intended to give more than an overall picture of an emulsion-based

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product design problem, while it focuses on the surfactant design and selection as a part of the above

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mentioned framework. A comprehensive description of the whole step-by-step methodology is given by Mattei et al. [16].

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4.1 SURFACTANT DESIGN AS EMULSIFIER – EMULSIFIED UV SUNSCREEN

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As seen from Figure 2, in the step-by-step methodology for emulsified product design, the surfactant system is designed after the active ingredients and both the continuous and the dispersed phases

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have been selected. Table S6 in the Supplementary Material provides the output of steps 2 and 3a,

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that is the chemicals chosen as active ingredients and continuous and dispersed solvents, together

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with consumer assessments and the target properties responsible for their design (solvents) and selection (active ingredients), as the output of the step 1 of the methodology. The surfactant to be designed, as previously mentioned, needs to act as emulsifier, ensuring the formation of an oil-in-water emulsion, stable under the range of temperature in which the product is supposed to be used.

With relation to the set of equations (1) to (7), then, proper boundaries are set so that the designed surfactant is safe, non-toxic, not affected by the presence electrolytes and able to generate a stable emulsion. These targets are translated into target properties, as in Table 5. Through a computer-aided molecular design (CAMD) technique, all the above mentioned constraints have been applied, thus

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reducing the search space to a limited number of candidate surfactants. The choice of the most advantageous between the candidates is taken minimizing the cost connected to its use, which means

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the minimum cost at which the critical micelle concentration is reached. Octyl Esaethylene Oxide has been designed as optimum surfactant for this purpose and Table 5 provides the results of the models

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TABLE 5 HERE

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used for the design.

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4.2 SURFACTANT DESIGN/SELECTION AS ACTIVE INGREDIENTS – EMULSIFIED HAND WASH There are some classes of products which contain surfactants not only to keep the formulation in the

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emulsified form, but also to act as active ingredients. These are detergents in general, where

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surfactants are responsible for the wetting of the surface to be cleaned, for the dissolution of the dirt

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and for not allowing the re-deposition of the dirt itself. When these products need to be designed, then, surfactants are included at the second step of the methodology of Figure 2, before any other

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ingredients of the formulation. The consumer assessments to be satisfied might then differ from those listed in the previous paragraph, as different are the target properties and the relative boundaries set on them. A comprehensive list of the consumer assessments for surfactants as active ingredients, together with the property constraints and models used is reported in Table 6. Compared to the previous case-study, here the surfactant system needs to satisfy the main needs of the product. Between them, high foam-ability and non-irritability of the skin. The foam-ability has been qualitative modeled by Pandey et al. [40] as a function of the surface tension of the system and the critical micelle concentration of the surfactant used. More precisely, the lower the surface tension, the

Page 21 of 49

higher the foam-ability, and the lower the critical micelle concentration, the higher the foam-ability. The non-irritability of the skin, on the other hand, is estimated through Hansen solubility parameters

ip t

[31]. If the Hansen solubility parameters of the designed ingredient are compatible with the parameters characterizing the proteins of the skin, then that ingredient is likely to partly dissolve the

cr

protein layer, consequently irritating the skin. Therefore, proper boundaries on the Hansen solubility

us

parameters are set in order to qualitatively satisfy the constraint of non-irritability. Given the above, the foam-ability is usually related to the presence of ionic surfactants (since ionic surfactants are

an

characterized by low surface tension as well as low critical micelle concentration), while the skin-care is usually connected to the use of nonionic surfactants, due to the values of their solubility

M

parameters, usually far from those of the proteins of the skin. Therefore, a mixture of ionic and

d

nonionic surfactants is usually chosen, the stability of which needs to be checked once the other

te

ingredients of the formulation have been chosen. Numerical constraints on the properties, as

Ac ce p

reported in Table 6, are applied and through a CAMD techniques a restrict number of candidates are generated. Between these, Octyl Esaethylene Oxide is designed as the most advantageous nonionic surfactant to be used as a nonionic surfactant, while Sodium Laureth Sulfate is selected with rulebased selection criteria as the most advantageous ionic surfactant to be chosen. Calculated values for the key-properties considered in the case-study are reported in Table 6. TABLE 6 HERE The stability of the two surfactants in the formulation, though, needs to be ensured after all the other ingredients have been selected and the most advantageous overall composition of the product has

Page 22 of 49

been chosen. Table S7 in the Supplementary Material lists the ingredients chosen, together with the relative consumer assessments and target properties to be satisfied.

ip t

The overall composition of the formulation is needed in order to qualitatively estimate the stability of the emulsion by calculating the HLD-value of each of the two active-ingredients selected at the

cr

second step of the procedure, that are expected to form a stable emulsion.

us

5. FUTURE PERSPECTIVES: SURFACTANT-RELATED PHASE BEHAVIORS

an

The description of binary (water-surfactant and oil-surfactant) and ternary (water-oil-surfactant) phase behaviors with a thermodynamic model, instead of the adoption of simplified heuristic, is

M

considered as a major progress to be implemented in the product design methodology. This way, it is possible to identify boundaries in terms of temperature and, especially, composition for a surfactant

d

to generate a stable emulsified formulation. This chapter will highlight some perspectives for future

te

development of the above mentioned analysis and a few preliminary results; to this point, however,

Ac ce p

the application of surfactant-related phase behaviors is limited to the availability of experimental data relative to the systems of interest. Therefore this approach can be applied for product analysis as well as for verification of the design obtained from the methodology. 5.1 BINARY SYSTEMS: WATER-SURFACTANT AND OIL-SURFACTANT Water-surfactant phase diagrams are fundamental when the surfactant is expected to be mainly dissolved in the water-phase (high values of the hydrophilic-lipophilic balance), which leads to the formulation of oil-in-water emulsions. On the other hand, the phase diagrams between oil and surfactants are to be considered primarily when a water-in-oil emulsion is desired. As an example of water-surfactant phase behaviors, Figure 3 presents the phase diagram of the

Page 23 of 49

system between water and dodecyl-esaethylene oxide. For emulsion-based chemical product design, therefore, it is relevant to define the boundaries in terms of temperature and concentration, so that

ip t

the designed formulation lies in the area defined as L1, where the models presented in Tables S1-S3 in the Supplementary Material can be safely applied. Mitchell et al. [34] and Sjöblom et al. [41] provide a

cr

satisfactory amount of experimental data as well as theoretical explanations for the formation of the

us

different meso-phases, relatively to aqueous surfactant solutions. The methods proposed, however, cannot be used for the prediction of these phase boundaries and therefore they cannot be applied in

an

the framework for chemical product design. Approximate predictions of these phase behaviors, based on the analysis of several phase diagrams in parallel with the molecular structure of the species

M

involved, are however expected to be possible by the authors and they are considered to be a

d

potential important development on the way for a fully model-based methodology for emulsion-

te

based chemical product design. The adoption of such correlations can lead to the calculation of simplified water-surfactant phase behaviors as described in Figure S4 in the Supplementary Material,

Ac ce p

related to the system reported in Figure 3.

When a water-in-oil emulsion is wanted, on the other hand, the phase behavior of surfactant mixtures with oil is relevant. Figure S5 in the Supplementary Material shows an example the phase behaviors between oils and surfactants, in terms of liquid-liquid miscibility boundaries. The reference surfactant is hexyl-pentaethylene oxide, while four different n-alkanes are considered as the oilphase. In Figure S5, only the miscibility curve as function of surfactant concentration and temperature is reported, while the formation of micellar solutions of standard appearance or of viscous meso-phases

Page 24 of 49

with different self-assemblies of the surfactant in absence of water is debatable [43]. For use in emulsion-based chemical product design, it is necessary that the designed formulation lies above the

ip t

line of the miscibility gap. It is easy to determine in Figure S5 a trend of the miscibility curves as a function of the number of carbon atoms of the n-alkane considered. This leads to the consideration

cr

that a correlation based on the molecular structure of the chemicals involved in the phase equilibrium

applied for use in emulsion-based chemical product design.

us

can approximately describe these curves and therefore a correlative model may be developed and

an

In relation to both water-surfactant and oil-surfactant phase behaviors, however, experimental measurements are needed in order to define numerical boundaries on the composition of the desired

M

formulation, since model-based generation of data is not considered yet reliable.

d

5.2 TERNARY SYSTEMS: WATER-OIL-SURFACTANT

te

The understanding of the behavior of ternary water-oil-surfactant systems is also considered to be crucial, in order to determine temperature and composition boundaries for a stable emulsion. These

Ac ce p

type of phase envelops can be represented in several ways, since many variables are involved; in relation to emulsion-based chemical product design, the most useful alternative is represented by the use of the so-called Kahlweit’s fish phase diagram [42]. Here, ternary water-oil-surfactant data are drawn in an X-Y diagram, where the surfactant content (usually in weight percentage) is in the X-axis, while the temperature is on the Y-axis. These diagrams represent a valid tool for emulsion-based product design since different types of products can be recognized and the possibilities for the formation of each of them are easily identified, given the temperature and the composition of the

Page 25 of 49

formulation. An example of such as “fish-type phase diagram” for the system water-tetradecane-2butoxyethanol is shown in Figure 6.

ip t

FIGURE 6 HERE

cr

In Figure 6 different areas are can be identified: the region defined with the symbol 1φ represents the area where a micro-emulsion can be formed, the region defined with the symbol 2φ (both 2φ – W/O

us

and 2φ – O/W) is the area where an emulsified product can be formulated, while the region identified

an

by the symbol 3φ is a hybrid domain, where an emulsion and a microemulsion may coexist. The emulsion domain (2φ) consists of two areas: one above the hybrid domain, described by the symbol

M

2φ – W/O, where a water-in-oil emulsion can be formed and another below the hybrid domain, described by the symbol 2φ – O/W, where an oil-in-water emulsion is favored instead. Consequently,

d

the region of the emulsion domain located at intermediate temperatures between the two above

te

mentioned areas represents an unstable region where it is not recommended to design an emulsified

Ac ce p

product, since its life time is expected to be limited. As a part of an emulsion-based chemical product design procedure, then, it is necessary to make sure that the designed formulation lies in the 2φ – W/O area when a water-in-oil emulsion is desired and in the 2φ – O/W if an oil-in-water emulsion is wanted. This type of diagrams can be used both during the design of the surfactant and during the verification of the designed product. The authors are not aware of any reliable model for the prediction of such phase equilibria, and therefore this analysis can be performed up to now only when experimental data are available. When predictions are necessary, the hydrophilic-lipophilic deviation (HLD) approach [32] is applied instead. This method consists of the application of an experimentalbased correlation for each of the surfactant in the formulation, considering several variables such as

Page 26 of 49

the presence of electrolytes, the nature of the oil as well as of the water phases, the temperature, the molecular structures of the surfactant, etc. If the calculated HLD-value is zero, then the formulation is

ip t

located in the 3φ domain of Figure 6 and therefore an unstable system is expected. On the other hand, if a positive value is obtained, then a water-in-oil emulsion is favored, while if a surfactant is

cr

characterized by a negative value of its HLD, then an oil-in-water emulsion may be formed. The higher

us

the absolute value of the HLD of the surfactant is, the more stable the emulsion formed is expected to be, since it is located further away from the unstable region identified by the hybrid domain. This

an

method does not have the thermodynamic basis of the representation of the ternary phase diagram, but it can be used as a qualitative predictive model when the needed experimental data are not

M

available.

d

6. CONCLUSION

te

A comprehensive framework for surfactant design and selection for emulsion-based chemical product

Ac ce p

design has been presented. The necessary properties for the whole procedure have been listed and the need of predictive models for a reliable solution of the reverse problem has been highlighted. Moreover, a group-contribution model, based on the Marrero and Gani method has been developed for the correlation and further prediction of the cloud point of nonionic surfactants. The addition of new dedicated third order groups, in order to take into account the peculiar structures of the surfactants considered, has been necessary in order to achieve acceptable performances. Compared to the existing QSPR models, the group-contribution model here developed performed better given the standard deviation and the average absolute deviation as statistical indices and the number of different families of surfactant considered, as an indicator of the range of application of the model.

Page 27 of 49

The application of the methodology has been then illustrated using two conceptual case-studies. In the first, the surfactant system to be designed was needed in order to keep the formulation in a

ip t

stable emulsified form. So, the surfactant needs to be chosen once the active ingredients and solvents were known. Boundaries were set on the basis of effectiveness (related to the ingredients previously

cr

designed and/or selected), safety and toxicity, while the final decision has been performed by

us

minimizing the cost of the ingredient. In the second case-study, instead, the surfactant system was supposed to act as emulsifier and active ingredient at the same time. The surfactant is then designed

an

before any other ingredient of the formulation, and in order to satisfy the main needs of the product, a surfactant mixture of ionic and nonionic surfactants was needed. The stability of the surfactants in

M

relation to the other chemicals present in the designed product is ensured after the overall

d

composition has been selected. Further perspective for the application of rigorous thermodynamic

te

descriptions of the phase behavior of surfactant-related system, both in binary mixture with water and oil, and in ternary mixture with both an oil- and a water-phase, is considered a relevant

Ac ce p

improvement that can help leaving heuristics and rules of thumb which are usually applied to estimate the phase stability of emulsified products. The application range of the methodology is wide, in the sense that other similar products can be designed, once the needs-property relations are established. Further works will focus on the development of group-contribution methods for the pure component properties of ionic surfactants (adequate new first order groups are needed) and the development of predictive, reliable models able to efficiently describe both the binary and the ternary phase diagrams involving surfactants. ACKNOWLEDGEMENTS

Page 28 of 49

Financial support from the Technical University of Denmark is greatly acknowledged. Advice given by Professor Michael Hill, from the Department of Chemical Engineering of Columbia

ip t

University, New York, in the development of one of the case studies is greatly appreciated.

cr

SUPPLEMENTARY MATERIAL

Figure S1: Dependence of the experimental cloud point (in °C) with the number of ethylene oxide

us

groups in the hydrophilic head of some linear alkyl ethoxylates. Figure S2: Dependence of the square

an

of the experimental cloud point (in °C) with the number of ethylene oxide groups in the hydrophilic head of some linear alkyl ethoxylates. Figure S3: Analysis of the absolute errors before (a) and after

M

(b) the introduction of dedicated 3rd order groups. Figure S4: Simplified phase diagram of an aqueous mixture of a polyoxyethylene-based nonionic surfactant over the temperature range 0-100°C, based

d

on the system between water and dodecyl-esaethylene oxide of Figure 3. Figure S5: Phase diagram of

te

the systems between hexyl-pentaethylene oxide and four different n-alkanes. Data are taken from

Ac ce p

[42]. Table S1: List of primary pure component properties needed for surfactant design together with the model used. Table S2: List of secondary pure component properties needed for surfactant design together with the model used. Table S3: List of mixture properties needed for chemical product design together with the model used. Table S4: Statistical indices of performances relative to the correlation of 72 data-points regarding the cloud point of nonionic surfactants using Marrero and Gani method with only 1st and 2nd order groups. Table S5: Statistical indices of performances relative to the correlation of 72 data-points regarding the cloud point of nonionic surfactants using Marrero and Gani method with 1st, 2nd and 3rd order groups. Table S6: List of consumer assessments, target properties, physico-chemical properties considered and chemical selected as active ingredients and

Page 29 of 49

dispersed and continuous phase solvents for the design of an emulsified UV-sunscreen. Table S7: List of consumer assessments, physico-chemical properties considered, chemical selected and molar

ip t

percentage of dispersed and continuous phase solvents, emulsifiers and additives for the design of an emulsified hand-wash.

an

REFERENCES

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TABLE A1 HERE

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APPENDIX

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[1] Villadsen J. Putting structure into chemical engineering: proceeding of an industry/university conference. Chemical Engineering Science 1997; 52: 2857-2864.

te

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[2] Cussler EL, Moggridge GD. Chemical Product Design. 2nd ed. Cambridge University Press; 2012 [3] Gani R. Chemical product design: challenges and opportunities. Computers and Chemical

Ac ce p

Engineering 2004; 28: 2441-2457

[4] Abildskov J, Kontogeorgis GM. Chemical product design. A new challenge of applied thermodynamics. Chemical Engineering Research and Design 2004; 82: 1505-1510. [5] Conte E, Gani R, Ng KM. Design of Formulated Products: A Systematic Methodology. AIChE Journal 2011; 57: 2431-2449.

[6] Conte E, Gani R, Cheng YS, Ng KM. Design of Formulated Products: Experimental Component. AIChE Journal 2012; 58: 173-189.

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[7] Raman VS, Maranas CD. Optimization in product design with properties correlated with topological indices. Computers & Chemical Engineering 1998; 22: 747-763.

ip t

[8] Chemmangattuvalappil NG, Solvason CC, Bommareddy S, Eden MR. Combined property clustering and GC(+) techniques for process and product design. Computers & Chemical Engineering 2010; 34:

cr

582-591.

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[9] Teixeira MA, Rodriguez O, Rodrigues S, Martins I, Rodrigues AE. A case study of product

an

engineering: Performance of microencapsulated parumes on textile applications. AIChE Journal 2012; 58: 1939-1950.

M

[10] Charpentier JC. Perspective on multiscale methodology for product design and engineering.

d

Computers & Chemical Engineering 2009; 33: 936-946.

te

[11] Wibowo C, Ng KM. Product-centered processing: manufacture of chemical-based consumer

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products. AIChE Journal 2002; 48: 1212-1230.

[12] Smith BV, Ierapepritou M. Integrative chemical product design strategies: Reflecting industry trends and challenges. Computers & Chemical Engineering 2010; 34: 857-865. [13] Bernardo FP, Saraiva PM. Integrated process and product design optimization: a cosmetic emulsion application. Computer Aided Chemical Engineering 2012; 20: 1507-1512. [14] Bagajewicz M, Hill S, Robben A, Lopez H, Sanders M, Sposato E, Baade C, Manora S, Coradin JH. Product Design in Price-Competitive Markets: A Case Study of a Skin Moisturizing Lotion. AIChE Journal 2011; 57: 160-177.

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[15] Bagajewicz M. On the role of microeconomics, planning, and finances in product design. AIChE Journal 2007; 53: 3155-3170.

ip t

[16] Mattei M, Kontogeorgis GM, Gani R. A Systematic Methodology for Design of Emulsion Based

cr

Chemical Products. Computer Aided Chemical Engineering 2012; 31: 220-224.

[17] Tiddy GJ. Formulation Science and Technology – Surfactants Needed!. Current Opinion in Colloid

us

& Interface Science 2000; 379-380.

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[18] Marrero J, Gani R. Group-contribution-based estimation of pure component properties. Fluid

M

Phase Equilibria 2001; 183: 183-208.

[19] Constantinou L, Gani R. New group-contribution method for estimating properties of pure

d

compounds. AIChE Journal 1994; 40: 1697-1710.

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[20] Conte E, Martinho A, Matos HA, Gani R. Combined Group-Contribution and Atom Connectivity

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Index-Based Methods for Estimation of Surface tension and Viscosity. Industrial & Engineering Chemistry Research 2008; 47: 7940-7954. [21] Modarresi H, Conte E, Abildskov J, Gani R, Crafts P. Model-based calculation of solid solubility for solvent selection – A review. Industrial & Engineering Chemistry Research 2008; 47: 5234-5242. [22] Hukkerikar AS, Kalakul S, Sarup B, Young DM, Sin G, Gani R. Estimation of Environment-Related Properties of Chemicals for Design of Sustainable Processes: Development of Group-contribution (+) (GC(+)) Property Models and Uncertainty Analysis. Journal of Chemical Information and Modeling 2012; 52: 2823-2839.

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[23] Ceriani R, Gani R, Meirelles AJA. Prediction of heat capacities and heats of vaporization of organic liquids by group contribution methods. Fluid Phase Equilibria 2009; 283: 49-55.

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[24] Mattei M, Kontogeorgis GM, Gani R. Modeling of the Critical Micelle Concentration (CMC) of Nonionic Surfactants with an Extended Group-Contribution Method. Industrial and Engineering

cr

Chemistry Research 2013; 52: 12236-12246.

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[25] Griffin WC. Classification of Surface-Active Agents by “HLB”. Journal of the Society of the Cosmetic

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Chemists 1949; 1: 311-326.

[26] Li Y, Xu G, Luan Y, Yuan S, Xin X. Property Prediction of Surfactant by Quantitative Structure-

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Property Relationship: Krafft Point and Cloud Point. Journal of Dispersion Science and Technology

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2007; 26: 799-808.

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[27] Spencer CF, Danner RP. Improved equation for prediction of saturated liquid density. Journal of

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Chemical and Engineering Data 1972; 17: 236-241. [28] Horvath AL. Molecular Design. Elsevier; 1992. [29] Liaw HJ, Lee YH, Tang CL, Hsu HH, Liu JH. A mathematical model for predicting the flash point of binary solutions. Journal of Loss Prevention in the Process Industries 2002; 15: 429-438. [30] Pal R. Rheology of simple and multiple emulsions. Current Opinion in Colloid & Interface Science 2011; 16: 41-60. [31] Hansen CM. Hansen Solubility Parameters: A User’s Handbook. 2nd ed. CRC Press; 2007.

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[32] Salager JL. Quantifying the concept of physico-chemical formulation in surfactant-oil-water systems – State of the art. Progress in Colloid and Polymer Science 1996; 100: 137-142.

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[33] Hu J, Zhang X, Wang Z. A Review on Progress in QSPR Studies for Surfactants. International

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Journal of Molecular Science 2010; 11: 1020-1047.

[34] Mitchell DJ, Tiddy GJT, Wairing L, Bostock T, McDonald M. Phase Behaviour of Polyethylene

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Surfactants with Water. Journal of Chemical Society-Faraday Transactions I 1983; 79: 975-1000.

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[35] Guo C, Ren Y, Zhou P, Shao J, Yang X, Shang Z. Toward a Quantitative Model and Prediction of the Cloud Point of Normal Nonionic Surfactants and Noverl Gemini Surfactants with Heuristic Method and

M

Gaussian Process. Journal of Dispersion Science and Technology 2012; 33: 1401-1410.

d

[36] Ghasemi J, Ahmadi S. Combination of Genetic Algorithm and Partial Lease Squares for Cloud

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Point Prediction on Nonionic Surfactants from Molecular Structures. Annali di Chimica 2007; 97: 69-83

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[37] Ren Y, Zhao B, Chang Q, Yao X. QSPR modeling of nonionic surfactant cloud points: an update. Journal of Colloid and Interface Science 2011; 358: 202-207. [38] Rosen MJ. Structure/Performance Relationships in Surfactants. American Chemical Society, 1984. [39] Hukkerikar AS, Meier RJ, Sin G, Gani R. A Method to Estimate the Enthalpy of Formation of Organic Compounds with Chemical Accuracy, Fluid Phase Equilibria 2013; 348: 23-32. [40] Pandey S, bagwe RP, Shah DO. Effect of counterions on surface and foaming properties of dodecyl sulfate. Journal of Colloid and Interface Science 2003; 267: 160-166.

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[41] Sjöblom J, Stenius P, Danielsson I. Phase Equilibria of Nonionic Surfactants and the Formation of Microemulsions in Schick MJ. Nonionic Surfactants – Physical Chemistry. Surfactant Science Series;

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Volume 23; 1987. [42] Kahlweit M, Strey R. Phase Behavior of Ternary Systems of the Type H2O-Oil-Nonionic

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Amphiphile (Microemulsions). Angewandte Chemie International Edition English 1985; 24: 654-668.

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[43] Ray A. Solvophobic interactions and micelle formation in structure forming nonaqueous solvents.

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Nature 1971; 231: 313-315.

[44] Lin BJ, Chen LJ. Liquid-liquid equilibria for the ternary system water+tetradecane+2-

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te

d

M

butoxyethanol. Fluid Phase Equilibria 2004; 216: 13-20.

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M

an

us

cr

ip t

LIST OF FIGURES

Ac ce p

te

d

Figure 1: The chemical product tree: classification of chemical-based products [3]

Page 36 of 49

ip t cr us an

Ac ce p

te

d

M

Figure 2: Work-flow diagram for the computer-aided stage of the formulation design methodology

Page 37 of 49

ip t cr us an M d

Figure 3: Phase diagram of an aqueous mixture of a polyoxyethylene-based nonionic surfactant over the temperature range 0-100°C

Ac ce p

te

Data are relative to the system between water and dodecyl-esaethylene oxide, data are taken from [34].

Page 38 of 49

ip t cr us an

M

Figure 4: Parity plot relative to the correlation of 72 data-points regarding cloud point (in °C) of nonionic surfactants

Ac ce p

te

d

using the Marrero and Gani GC-method with only first and second order groups

Page 39 of 49

ip t cr us an

M

Figure 5: Parity plot relative to the correlation of 72 data-points regarding cloud point of nonionic surfactants

Ac ce p

te

d

using the Marrero and Gani GC-method after the addition of dedicated third order groups

Page 40 of 49

ip t cr us an

Figure 6: Calculated fish-diagram of the system water-tetradecane-2-butoxyethanol; water-oil ratio: 7.03

Ac ce p

te

d

M

Data taken from [44]

Page 41 of 49

LIST OF TABLES

CPexp

Code

[°C]

CPexp

Code

[°C]

[°C]

CnH2n+1O(C2H4O)mH C9E6

C5E2

36

C10E4

C6E2

0

C10E5

C6E3

40.5

C10E6

C6E4

63.8

C6E5 C6E6

C12E10

95.5

C12E11

100.3

41.6

C13E5

27

60.3

C13E6

42

C10E7

75

C13E8

72.5

75

C10E8

84.5

C14E5

20

83

95

C14E6

42.3

M

19.7

C10E10

te

C7E3

75

an

44.5

d

C4E1

us

Linear alkyl ethoxylates CnEm

CPexp

cr

Code

ip t

Table 1: List of the original data-set of experimental cloud point (in °C) of nonionic surfactants (1% weight percentage). Distinction in classes is based only on the molecular structure, each of which is described. Compounds highlighted in grey have been excluded from the parameter regression step. Data from [35]-[38]

27.6

C11E4

10.5

C14E7

57.6

7

C11E5

37

C14E8

70.5

C8E4

38.5

C11E6

57.5

C15E6

37.5

C8E5

58.6

C11E8

82

C15E8

66

C8E6

72.5

C12E4

6

C16E6

35.5

C8E8

96

C12E5

28.9

C16E7

54

C8E9

100

C12E6

51

C16E8

65

C8E12

106

C12E7

64.7

C16E9

75

C9E4

32

C12E8

77.9

C16E10

66

C9E5

55

C12E9

87.8

C16E12

92

Ac ce p

C8E3

Branched alkyl ethoxylates ICnEm

(C(n-2)/2Hn-1)2CHCH2O(C2H4O)mH

(n = 6, 10)

Page 42 of 49

(C(n-1)/2Hn)2CHO(C2H4O)mH TCnEm

(n=13)

(C(n-1)/3H(2n+1)/3)3CO(C2H4O)mH 78

TC10E7

22

TC13E9

34

IC10E6

27

IC13E9

35

TC16E12

48

ip t

IC6E6

Phenyl alkyl ethoxylates CnH2n+1C6H4O(C2H4O)mH 64.3

C9PE8

34

C12PE9

C8PE7

22

C9PE9

56

C12PE11

C8PE9

54

C9PE10

75

C8PE10

75

C9PE12

87

C8PE13

89

C9PE13

33 50

us

TC8PE9

cr

CnPEm

90

an

C12PE15

89

CnEmPk

M

Alkyl polyoxyethylene-polyoxypropylene copolymers

CnH2n+1C6H4O(C2H4O)m(C3H6O)kH

C12E4P5

22.1

C12E3P6

10.6

C12E5P4

29.8

d

Carbohydrate-derivate ethoxylates

CnH2n+1COO(C2H4O)mH

Ac ce p

CnCOOEm

CnH2n+1COO(C2H4O)mCH3

te

CnCOOEmC

C9COOE7C

44

C9COOE12

74

C9COOE10C

65

C11COOE6

54

C11COOE8

53

Ethoxylated amides CnGEm

CnH2n+1NHCH2COO(C2H4O)mH

CnAEm

CnH2n+1NHCHCH3COO(C2H4O)mH

CnSEm

CnH2n+1NCH3CH2COO(C2H4O)mH

C12GE2

78

C12GE4

75

C12GE3

46

C12AE3

22.5

C12SE3

44

Alkyl branched ethoxylates AGM-n(3)

CnH2n+1CH(O(C2H4O)3H)2

Page 43 of 49

AGM-7(3)

34

AGM-11(3)

30

AGM-13(3)

29

Table 2: Marrero and Gani group definition and contributions after the regression based on 72 experimental data of cloud point st

2

nd

Ci [K ]

2 Order Group (j)

CH3

6.4351e+04

AROMRINGs s

CH2

2

rd

2

Dj [K ]

3 Order Group (k)

Ek [K ]

0

(CH2)n-(OCH2CH2)m (m=3, n<8) and (m=4..5, n>8)

-1.1108e+04

-2.2149e+03

(CH2)n-(OCH2CH2)m (n=5)

-6.7595e+03

CH

-6.5736e+04

(CH2)m-CO-(OCH2CH2) (m=8)

-2.1595e+04

C

-1.4320e+05

(CH2)m- C6H4-(OCH2CH2) (m=8)

6.0521e+03

aCH

-5.8171e+03

((CH2)n)mCOC2H4- (n>2, m>1)

aC- CH2

0

OH

-3.0249e+03

CH2COO

-2.7706e+03

CH3O

3.7198e+04

CH2O

8.9104e+03

aC-O

0

CH2NH

0

OCH2CH2OH

3.3508e+04

-2.4357e+04

Ac ce p

te

d

M

an

us

cr

1 4

ip t

1 Order Group (i)

Page 44 of 49

Table 3: Statistical indices of performances relative to the correlation of 72 data-points regarding the cloud point of nonionic surfactants using Marrero and Gani method before and after rd

Data-points for the regression rd

Marrero and Gani method without 3 order groups

73

8.91

us

rd

SD

AAD

AADmax

cr

Model

ip t

the addition of 3 order groups, compared with 3 different QSPR models [35], [36], and [37].

7.65

25.90

73

5.65

4.62

15.83

QSPR model [35]

81

9.31

7.09

50.2

QSPR model [36]

68

5.89

4.69

17.98

78

7.46

3.13

52.8

an

Marrero and Gani method with 3 order groups

Ac ce p

te

d

M

QSPR model [37]

Page 45 of 49

Table 4: List of the surfactant property applied in the case studies reported together with the model used and statistical indices Model used

Cloud point

Group-contribution method [this work]

Critical micelle concentration

Group-contribution method [previously developed [24]]

Hansen solubility parameters

Group-contribution method [21]

Hydrophilic-lipophilic balance

Definition [24]

Hydrophilic-lipophilic deviation

Definition [32]

Krafft temperature

QSPR model [26]

Open cup flash point

Group-contribution method [29]

R > 0.96

Surface tension reduction

QSPR model [26]

R > 0.99

Toxicity parameter

Group-contribution method [22]

2

R > 0.94

cr

2

us

R > 0.78

an

M

2

R > 0.99

-

a

-

a

2

R > 0.94 2

2

2

R > 0.77

Statistical indices for hydrophilic-lipophilic balance and hydrophilic-lipophilic deviation cannot be provided since relative

Ac ce p

te

experimental values cannot be found.

d

a

Coefficient of determination

ip t

Surfactant property

Page 46 of 49

Table 5: Target properties, relative physico-chemical properties and models needed, constraints and modeled value for surfactant design for an emulsified UV sunscreen Constraints

Oil-in-water emulsion desired

Hydrophilic-lipophilic balance [25]

HLB > 12

Thermal stability

Cloud point

CP > 55˚C

Safety

Flash point [29]

Tf > 55˚C

Non-toxicity

Toxicity parameter [22]

Not influences by electrolytes

Nonionic surfactants preferred

Stability as an emulsion

Hydrophilic-lipophilic deviation [32]

-

Critical micelle concentration [24]

Modeled value

ip t

Properties considered and models used

13.4

73˚ C

us

cr

Target properties

-log(LC50) > 3.16 mol/m

341˚ C

3

3.97 mol/m

3

-

HLD ≠ 0

-0.7

-

0.009 mol/L

Ac ce p

te

d

M

an

-

Page 47 of 49

Table 6: Target properties, relative physico-chemical properties and models needed, constraints and modeled value for surfactant design for an emulsified hand-wash Properties considered

Constraints

Surface tension [26]

σ < 33 mN/m

Critical micelle concentration [24]

CMC < 0.01 mol/L

0.008 mol/L

-

1

δH ≠ 11.9 ± 1.5 Mpa

13.7

-

1

Surface tension [26]

σ < 29.3 mN/m

31 mN/m

27.8 mN/m

Hydrophilic-lipophilic balance [25]

HLB > 10

13.4

40

Tf > 55˚C

341˚C

94˚C

0,5

δP ≠ 9.8 ± 1.5 Mpa

an

-log(LC50) > 3.16 mol/m

te

Cloud point [this work]

M

Toxicity parameter [2]

d

Non-toxicity

Krafft temperature [26] Hydrophilic-lipophilic

Ac ce p

1

0.009 mol/L

9.2

Flash point [29]

an emulsion

27.8 mN/m

1

Safety

Stability as

31 mN/m

-

0,5

Thermal stability

Ionic Surfactant

16.9

Solubility parameters [21]

Cleaning performances

Nonionic Surfactant

us

0,5

δD ≠ 22.4 ± 1.5 Mpa Non-irritability

Calculated value

cr

and models used Foam-ability

Calculated value

ip t

Target properties

3

3.97 mol/m

3

LD50 = 5g/kg

CP > 55˚C

73˚C

-

TK < 20˚C

-

16˚C

HLD ≠ 0

-0.9

-1.5

2

deviation [32]

Hansen solubility parameters are not available, but the surfactant (Sodium Laureth Sulfate) is known to be soluble in water, so it is

unlikely to dissolve the proteins of the skin, given their Hansen solubility parameters 2

LC50 is not available, so a value for LD50, another toxicity parameter, has been provided instead

Page 48 of 49

Table A1: Calculation of the cloud point of Nonyl phenyl octaethylene oxide after the introduction of new third order groups. Calculated value is compared with experimental value and with the rd

Molecular structure

cr

Nonyl phenyl octaethylene glycol

ip t

calculations with the GC-model without 3 order groups and three QSPR models.

Ac ce p

te

d

M

an

us

Molecular formula: C33H60O10

Page 49 of 49