Why is Anionic Surfactant Customize Better?

The manufacture of detergent products such as laundry detergents, household cleaners and fabric softeners are of increasing interest to the consumer oriented chemical industry. Surfactants are the most important ingredient in detergent formulations, as they are responsible for the bulk of the cleaning power. In this research, a methodology has been developed to design a detergent product using computational tools. Different surfactant systems, such as single anionic, single nonionic, and binary mixtures of anionic-nonionic surfactants are covered in this work. Important surfactant properties such as critical micelle concentration (CMC), cloud point (CP), hydrophilic-lipophilic balance (HLB) and molecular weight (MW) have been identified. A group contribution (GC) method with the aid of computer modelling was used to determine the CMC, CP, and MW of surfactant molecules. The design of a surfactant molecule can be formulated as a multi-objective optimization problem that tradeoffs between CMC, CP, HLB and MW. Consequently, a list of plausible nonionic surfactant structures has been developed with the selected surfactant being incorporated into a binary surfactant mixture. Additives such as antimicrobial agents, anti-redeposition agents, builders, enzymes, and fillers were also considered and incorporated into a hypothetical detergent formulation together with the binary surfactant mixture. The typical ingredients and their compositions in detergent formulations are presented in the final stage of the detergent product design.

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1.&#;Introduction

1.1. Background

One of the most prominent applications of detergents is for domestic cleaning. The global market value of laundry detergents was valued at USD 60.9 billion in (Bianchetti et al., ). Detergents are complex mixtures of surfactants, builders, bleaching agents, enzymes and other minor additives (Pedrazzani et al., ). Surfactants are the active ingredients in detergent formulations as they are responsible for the bulk of the cleaning power. They can be divided into 3 main groups: anionic, non-ionic and cationic. Anionic surfactants are effective at removing soil but are sensitive to the presence of multivalent ions present in hard water. Nonionic surfactants have high solubility and are virtually immune to the effects of hard water but are less effective than anionic surfactants at removing soil. Cationic surfactants are generally used as fabric softeners.

Detergent formulations usually incorporate a mixture of anionic and nonionic surfactants as the properties of surfactant mixtures are easier to tune than those of single surfactants. A key advantage of utilizing surfactant mixtures is their lower Critical Micelle Concentration compared to pure anionic surfactants. Additional advantages include increased tolerance towards hard water compared to pure anionic surfactants and a higher effective Cloud Point for the nonionic surfactant(s) in the mixture (Na et al., ). Therefore, it is important to select an optimum mixture of surfactants so that the end-use specifications can be achieved.

The computational tools have been recently employed in producing other types of chemicals and many of those designed chemicals have been tested in the laboratory for their performance. The computational design and validation of insect repellent lotions and sunscreen lotions have been performed by Conte et al., , Conte et al., . The developed methodology also highlighted the importance of a combined computational and experimental approach in the design of personal care products. In another contribution, a green diesel blend has been designed using computer aided molecular design tools (Phoon et al., ). The designed fuel's properties are validated experimentally.

In this work, we have developed a methodology to design a new nonionic surfactant that can be combined with an anionic surfactant to form a mixture with improved properties in terms of CMC. Key properties of nonionic surfactants were studied, and property constraints were set according to the product application. Besides, Computer-Aided Molecular Design (CAMD) techniques were applied to identify the nonionic surfactants that satisfy the desired target properties. Next, multi-objective optimization was carried out to determine an optimum molecular structure after trading off between the surfactant properties. A new finding is expected to be obtained in this research, where CMC is set as primary objective in optimization to design a new nonionic surfactant. Product formulation has been done using the molecular structure found from optimization and suitable additives identified along with their appropriate composition.

1.2. Surfactant properties

The key surfactant properties that are to be considered during the design are Critical micelle concentration (CMC), cloud point, Hydrophilic-lipophilic balance (HLB) and molecular weight.

Critical micelle concentration (CMC) is an important characteristic of surfactants, it is defined as the concentration of the surfactant at which micelles start to form, and any additional surfactant added to the system will go into the micelles. When surfactants in solution have reached the CMC, they undergo spontaneous self-association to form micelles. With the formation of micelles, dirt and oil can be solubilized and lifted off the surface and dispersed into the solution. The CMC corresponds to the minimum value of surface tension &#; the surface tension decreases as the surfactant concentration increases up to the CMC (Rosen and Kunjappu, ). The CMC is also influenced by external factors such as temperature, pressure, pH and the surfactant's chemical structure. The determination of the critical micelle concentration of a surfactant is traditionally done by experimentation. Various studies have correlated the CMC values of surfactants with their molecular structure. Li et al. () proposed s-UNIQUAC (segment-based universal quasi-chemical model) and SAFT (statistical associating fluid theory) equations capable of accurately representing the activity coefficients and CMC values of surfactants in aqueous solutions. Saunders and Platts () developed models for the prediction of CMC for anionic and nonionic surfactants based on the structure of their hydrophobic chain and ethylene oxide groups. Cheng and Chen () utilized a modified excess Gibbs energy model (m-Aranovich and Donohue (m-AD) model) to calculate the CMC values of nonionic polyoxyethylene alcohol surfactants. In a study by Mattei et al. (), a group contribution property model has been developed to estimate the critical micelle concentration of nonionic surfactants based on the Marrero and Gani group contribution method.

Cloud point is defined as the temperature at which the mixture starts to phase separate, thus becoming cloudy (Rosen and Kunjappu, ). Cloud point is an important characteristic for nonionic surfactants as it is correlated with their wetting, cleaning and foaming ability. The polyoxyethylene chains of nonionic surfactants exhibit an reverse solubility versus temperature in water. The solubility of nonionic surfactants is due to their ability to form hydrogen bonds, these bonds are temperature sensitive and will be broken once the temperature is raised to the cloud point, due to high surfactant molecular activity (Sheng, ). When the surfactant molecules become dehydrated the solution becomes cloudy. Hydrophilic-lipophilic balance (HLB) is a measure of the balance between the hydrophilic and lipophilic groups of a surfactant molecule (Rosen and Kunjappu, ). The HLB number indicates the tendency of the surfactant to solubilize in oil or water, forming either a water-in-oil or oil-in-water emulsion. Low HLB numbers are assigned to surfactants that tend to more soluble in oil while high HLB numbers are assigned to surfactants that tend to more soluble in water.

The molecular weight of a surfactant is often used as a general indication of toxicity and biodegradability. As mixed surfactant systems of anionic and nonionic surfactants are most commonly used in detergency focused applications, their discharge into the environment is of great concern. Cowan-Ellsberry et al. () surmised that the alkyl chain length of the surfactant molecule is the most important determinant of its aquatic toxicity. Additionally, they noted that linear alkyl chained surfactants degrade much faster than highly branched surfactants. Liwarska-Bizukojc et al. () reported that the toxicity of surfactant molecules increased as their molecular weight increased while Warne and Schifko () observed an increase in the toxicity of the nonionic surfactants when the number of ethoxylate groups increased. Morrall et al. () reported that the toxicity of a surfactant is depend on its alkyl chain length and number of ethoxylates groups. Therefore, it can be deduced that in general, the lower the molecular weight of a surfactant, the higher its biodegradability and the lower its toxicity.

Hydrophilic-lipophilic balance (HLB) is a measure of the balance between the hydrophilic and lipophilic groups of a surfactant molecule (Rosen and Kunjappu, ). The HLB number indicates the tendency of the surfactant to solubilize in oil or water, forming either a water-in-oil or oil-in-water emulsion. Low HLB numbers are assigned to surfactants that tend to more soluble in oil while high HLB numbers are assigned to surfactants that tend to more soluble in water. A summary of function of HLB values are listed in .

Table 1

DescriptionHLBEmulsionNo emulsion1&#;4NonePoor emulsion3&#;6Water into oil emulsionsMilky emulsion after vigorous agitation6&#;8Water into oil emulsionsStable milky emulsion8&#;10Oil into water emulsionsTranslucent to clear emulsion10&#;13Oil into water emulsionsClear emulsion13+Oil into water emulsionsOpen in a separate window

The HLB of a nonionic surfactant can be determined using the ratio of the molecular weight of the hydrophilic portion of the surfactant (Griffin, ). Davies () also suggested another method to determine HLB value based on the chemical groups of the molecule. A completely hydrophobic surfactant molecule has a HLB value of 0, while a completely hydrophilic surfactant molecule has a HLB value of 20. The calculated HLB values from both methods can be used to predict the surfactant properties of a surfactant molecule, where a value from 1 to 3 indicates a antifoaming agent; a value from 3 to 6 indicates a W/O emulsifier; a value from 7 to 9 indicates a wetting agent; a value from 8 to 12 indicates an O/W emulsifier; a value from 13 to 16 is typical of detergents; a value of 15&#;20 indicates a hydrotrope (Fung et al., ).

1.3. Types of surfactant

Surfactants are amphiphilic organic compounds &#; composed of a hydrophobic hydrocarbon chain and a hydrophilic group. They are classified by their net charge into three main groups: anionic, cationic, and nonionic. Surfactants function by lowering the surface tension of a liquid allowing the liquid to spread evenly over a surface more easily; the surfactants adsorb onto the soil allowing them to remove the soil from the surface into the bulk liquid. It is important to note that after the soil is removed, it should be stabilized and suspended (via emulsification and dispersion) in the wash liquor to be washed away via mechanical agitation (St. Laurent et al., ).

Anionic surfactants are known to be effective at removing soil, clay, dirt and oily stains. Anionic surfactants ionize in the presence of water and become negatively charged, they then bind to positively charged particles such as clay (Williams, ). Anionic surfactants, in general, tend to generate more foam than other classes of surfactants. Anionic surfactants are used in greater volume than any group of surfactants due to their ease of production and low cost of manufacture. Examples of anionic surfactant families are alkyl sulfates (AS), linear alkylbenzene sulfonates (LAS), and alpha olefin sulfonates (AOS).

Nonionic surfactants are mostly ethylene oxide adducts, with the polyoxyethylene group as the hydrophilic part of the surfactant molecule. Nonionic surfactants are frequently used as their physical properties such as HLB can be easily adjusted by modifying the length of its hydrophilic chain. Nonionic surfactants have good cleaning power, are milder to human skin, and are highly soluble (Fung et al., ). Differing from anionic surfactants, nonionic surfactants are virtually unaffected by the presence of multivalent ions in hard water. Nonionic surfactants are also very effective at stabilizing emulsions. The detergency, wetting, and general usefulness of nonionic surfactants make them widely used in industrial and household products, especially detergents. Examples of nonionic surfactant groups are alcohol ethoxylates (AE), glucamides, ethoxylated amides (EA), alkyl polyglycosides (APG) and carbohydrate-derivate ethoxylates (CDE). Alcohol ethoxylates can be considered the most important of the nonionic surfactants as a wide variety of properties can be achieved by simply varying the alkyl chain length and the number of ethylene oxide moieties (Showell, ).

Cationic surfactants are generally based on a quaternary ammonium structures with several alkyl chains attached to a nitrogen atom which carries a positive charge (Yu et al., ). Cationic surfactants can be used as antistatic agents for hair conditioners due as they possess antistatic properties. Cationic surfactants are the active agents in fabric cleaners, and they work by reducing the friction between the fibers and the skin. Cationic surfactants are rarely utilized in detergents as they tend to rapidly adsorb onto soil without detaching (St. Laurent et al., ).

1.4. Surfactant mixtures

Mixture of surfactant is applied in detergent formulation to accomplish required detergent quality and satisfied cleaning performance. Nonionic surfactant has been extensively used in detergent formulations in combination with anionic surfactant. In a detergent containing both nonionic and anionic surfactant, the anionic surfactant contributes to cleaning performance in soil removal and nonionic surfactant contribute to make the surfactant system less sensitive to water hardness. Nonionic surfactant also increases the solubility of the surfactant mixture due to the ethylene oxide units belong to nonionic surfactant which is highly hydrophilic in nature. Furthermore, synergism occurred when nonionic surfactant mixed with anionic surfactant, results in reduction in the surface tension value of mixed surfactant (Jadidi et al., ). On top of that, the CMC of mixed surfactant also will be shifted to lower values compared to that of single anionic surfactant. Therefore, different type of nonionic surfactants is studied and modelled in this research and combine with an anionic surfactant to form a surfactant mixture at the later stage of work.

1.5. Computer-aided molecular design (CAMD)

Chemical product design is defined as &#;the process of determining new and suitable chemicals for a certain application&#; and is a laborious trial-and-error procedure that is constrained by both time and resources (Maranas, ). Design efforts are high-throughput and focus on small class of chemical compounds. To keep up with the increasing demand for new chemical products, more effective approaches must be utilized. The existence of computational tools enables design problems to be solved much more rapidly, thus the field of computer-aided molecular design (CAMD) is of paramount importance for chemical product design. CAMD is a systematic approach to design an optimal molecular structure, which combines molecular modelling technique, thermodynamics and numerical optimization (Gani et al., ). Property prediction uses the given chemical structure of a molecule to predict its properties, this is known as the forward problem; CAMD is the inversion of property prediction by predicting the chemical structure of a molecule from a given set of target properties, this is known as the reverse problem. There are several quantitative structure property relationships (QSPRs) that are often used in CAMD, such as group contribution methods, topological indices and signature descriptors (Ng et al., ).

Group contribution (GC) methods are the most popular QSPRs in CAMD as they exhibit good accuracy and a great degree of applicability. Group contribution methods operate on the assumption that some of the properties of atoms or molecular functional groups remain constant in many different molecules. The properties of a molecule can be predicted by identifying the existing functional groups within it (Cignitti et al., ). Joback and Reid () developed a GC method which extends the group increment idea to model many different properties with the same set of groups. In a study by Constantinou and Gani (), a two level GC method was developed which considered both the first order groups and second order groups to estimate the properties of pure compounds. The most widely used group contribution method in CAMD is that of Marrero and Gani (). In the Marrero and Gani group contribution method, molecular groups are classified as 1st order, 2nd order and 3rd order, where an increase in order provides more structural information about the molecule.

Signature descriptor is another useful QSPR method, which attempts to retain all the structural and connectivity information for every atom in a molecule. The approach has been applied by Stanton and Jurs to correlate the surface tension of organic molecules with charged partial surface area (CPSA) descriptor (Stanton and Jurs, ). In the studies done by Nelson and Jurs (), the aqueous solubility of organic compounds was correlated to molecular structure for hydrocarbon, halogenated carbons, ethers, and alcohols by using 10 descriptors. Moreover, Huibers et al. () has proposed to correlate the critical micelle concentration of nonionic surfactant with 3 descriptors. The major advantage of signature descriptors is that they can be manipulated through simple functions to represent groups from group contribution method, which means that the large amount of QSPRs derived from group contribution method is accessible using signature descriptor.

CAMD has been recognized as an important tool that can be applied for the design of different classes of chemical products. In the past, most of the applications of CAMD had been focused on the design of solvents and other bulk chemicals. However, in the recent years, CAMD have been successfully integrated into the design of other types of chemical products such as insect repellent and paint (Conte et al., ), adhesives (Jonuzaj et al., ) and biofuel additives (Mah et al., ). A general framework that can be used in the design of chemical products can be found in the literature (Zhang et al., ). One of the key differences in the design of chemical products from the bulk chemicals is the specific nature of design approaches that are appropriate for each class of these products. However, many of the chemical products consists of molecules that provide the key functionalities to the molecule. Products such as detergents, pharmaceutical products and agrochemicals. Therefore, it is expected that unique tools will be developed for the design of chemical products that make use of CAMD.

1.6. Bilevel optimization

Bilevel optimization is a mathematical program, where an optimization problem contains another optimization problem as a constraint (Sinha et al., ). During large-scale optimization and decision-making process, it is common to realized that the outcome of any solution taken by the upper level authority (leader) to optimize their goals is affected by the response of lower level entities (follower). Bilevel optimization problem often appear as leader-follower problems and the basic principle is to have the main optimization problem optimized while recognizing that the second level problems are independently optimized. Therefore, the lower level problems are required to have an optimized solution while optimizing the main objective. Ideally, the constraints of the lower level objectives can be the results of single objective optimization results of the lower level problems itself. However, this will provide the real optimal solution for the primary objective only if all the objectives are not conflicting. A more realistic approach is to set a certain fraction of the optimal value of lower level objectives to be set as constraints in the bi-level optimization formulation.

This approach has been applied in several applications where there is a main optimization target and also a number of important secondary targets. For instance, in the design of integrated biorefineries, Andiappan et al. () has optimized the reaction pathways by keeping economic performance as the main objective while environmental burden and heat of reaction as the secondary objectives. Other applications of this approach are in supply chains (Roghanian et al., ) and production planning (Cao and Chen, ). In the design of surfactants, since CMC is the key property to be targeted, CMC has to be prioritized in the design whereas the cloud point and HLB must be as close as the best possible values. Therefore, bi-level optimization allows us to model CMC as the as the upper level objective while keeping CP and HLB as secondary objectives.

Objective Function:

Minimize CMC

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Subject to:

{CloudPoint&#;w1×MaximumCloudPoint}

{HLB&#;w2×MaximumHLB}

{w3×MolecularWeight&#;MinimumMolecularWeight}

1.7. Problem statement

This research aims to apply CAMD techniques to identify an optimum nonionic surfactant which could be combined with an anionic surfactant to form a mixture that satisfies the primary objective of critical micelle concentration (CMC) while also fulfilling the secondary objectives of cloud point (CP), hydrophilic-lipophilic balance (HLB) and molecular weight (MW).

The proposed objectives of this research are:

• 1.

  Design a feasible nonionic surfactant to be mixed with an anionic surfactant; the combination of surfactants and their proportions should satisfy the target properties of the mixture.

• 2.

  Create a detergent formulation using the surfactant mixture and the appropriate additives.