TAGS: Science-based Formulation
As part of the Fast-Moving Consumer Goods (FMCG) market, the personal care and cosmetics companies strive for innovative concepts and solutions to stand out or stay ahead of a highly competitive industry. Forecasting future trends is clearly a key factor, but as trends are evolving at a very fast pace, adapting market demands into product developments should be as efficient as possible.
Despite the crucial demands of this fast-growing market, trial-and-error methods are still widely used to formulate such products, whereas opting for science-based approaches could offer more controlled product development processes.
In this context, the use of Hansen Solubility Parameters (HSP) is becoming more and more popular, due to their power in predicting and improving compatibility between ingredients in various formulations.
Amongst others, active ingredients are fundamental components of personal care products: even used at very low doses, they are responsible for delivering the claimed benefits. The ever-growing consumer need for highly effective products makes this market one of the most innovation-driven markets, continuously releasing innovative actives. Formulating with such new ingredients can be a challenging task: complete solubility should be achieved to obtain:
- Stable and homogeneous products
- Optimize their performance, and
- Aesthetical aspect (in the case of clear formulations)
While trial-and-error methods are rather time-consuming, applying the HSP approach offers quick and reliable solutions. By determining the HSP’s of the targeted active, optimal oil or solvent blend compositions can be predicted, ensuring its complete solubility, and stability of the formulated product. Besides bringing more efficacy in formulating, HSP gives a further understanding of interactions between ingredients and is just as applicable for solubility as for compatibility.
The predictive power of HSP goes even beyond formulating: HSP’s of the skin have been established, allowing the formulators to target the active delivery onto or through the skin. Let's learn the same in detail:
Smart & Predictive Formulations via Hansen Solubility Parameters
In 1967, Charles Hansen submitted his doctoral thesis “
The three-dimensional solubility parameter and solvent diffusion coefficient” which introduces the theory which has since become known as the Hansen Solubility Parameters (HSP). These parameters have removed the trial-and-error process and given practical solutions to countless problems across a wide variety of formulation-based industries (Hansen, 2017).
The term “
solubility parameters” is now considered to be quite restrictive, as the use of HSP goes beyond solubility challenges: these parameters can predict the compatibility for various types of chemicals/ingredients, allowing for smart and predictive ingredient matching. Therefore, HSP should be interpreted as “
Hansen Similarity Parameters”, as recognized by Dr. Hansen.
Check out our exclusive interview with Dr. Charles Hansen – Inventor of HSP »
The Hansen Solubility Parameters are made up of
δD (Dispersion forces);
δP (Polar forces) and
δH (Hydrogen bond forces). By plotting these in a 3D space in the HSPiP software, it is easy to visualize and interpret the results. When the HSP have been practically determined, the Compatibility Radius is also provided; all solvents/ingredients within this radius are compatible with the test product. The radius is concentration dependent, the higher the concentration of the product the smaller the radius.
The software HSPiP is now led by Professor Steven Abbott with Dr. Hiroshi Yamamoto. (Abbott, 2017) (Yamamoto, 1999 - Present). VLCI is a certified center for practically determining HSP and has been doing so in close collaboration with Professor Abbott and Dr. Hansen since 2010, for
all areas of the formulation world.
To learn more about the theory behind HSP, see
Professor Steven Abbott’s Tutorial Here!
Practically Determining Hansen Solubility Parameters
The classic method to practically determine a product’s HSP involves the test material being added to a
range of solvents that cover the HSP space. The samples are shaken and left to dissolve. The samples are then visually assessed with a qualitative rank from 1-6, where a 1 means the product is completely dissolved, a 6 means there has been no interaction between the solvent and the product and the other scores indicating various stages of dissolution.
Stages of Dissolution
This data is then entered into the HSPiP Software, which defines a spheroidal cluster of the solvents that dissolve the test material. This cluster is called the Hansen Solubility Sphere and its central coordinates (δD, δP and δH) define the solubility parameters of the test material. The software also performs an analysis of the “fit” of the data to the parameters it has determined, highlighting the validity of the result, which can indicate if more experimental data is needed.
HSP Sphere
This theory can be applied to a wide range of situations, including:
- Oils
- Actives
- Additives
- Pigments
- Polymers etc.
In the case of insoluble (crosslinked) polymers or pigments, the scoring is then based on criteria such as swellability or resistance to sedimentation.
VLCI offers the following test methods and HSP workflows for a range of materials and is able to develop bespoke tests for challenging molecules:
Method |
Suitable for |
Dissolution Method |
Simple solutes, mixtures, additives |
Quantitative Swell Test |
Crosslinked and insoluble polymers |
Sedimentation Method |
Insoluble particles, e.g. pigments |
Liquids Method |
Low molar volume liquids, e.g. solvents |
Interpreting the Results
The HSP parameters of a raw material give lots of information but there are many useful ways to implement and interpret them. The HSPiP Software defines the Relative Energy Difference (RED) of any product (oil, active, particle…) to the test material; this is calculated by these two equations:
Equation 1: Rα2 = 4(δD1 - δD2)2 + (δP1 - δP1)2 + (δH1 - δH2)2
Equation 2: RED = Rα/Ro
Ro is the radius of the material’s compatibility sphere. The RED value is very useful when formulating and can be interpreted by the following definitions:
- RED = 0 the product and material have the same HSP.
- RED < 1 the product lies within the materials’ compatibility sphere and therefore will be compatible.
- RED = 1 the product lies at the boundary of the material’s compatibility sphere, so will be borderline compatible. Hansen has noted that many smart formulations work at the borders!
- RED > 1 the product lies outside the material’s solubility sphere and therefore is incompatible.
These rules apply to issues such as:
- Dissolving actives
- Finding compatible additives
- Promoting active penetration through the skin, and much more
In principle, materials with the lowest RED are the most compatible with each other: selecting these will allow you to formulate at the lowest active concentration and will give the maximum performance in the formulation. For example, if an oil (or oil blend) and an active have a RED close to 0, the active will exhibit a perfect solubility in this oil (blend).
Examples of Relative Energy Differences
When searching for new raw materials, the RED values, or just the rankings of Ra (“distance”) values, are crucial for predicting the compatibility between formulation ingredients and therefore allowing you to choose materials that are more likely to be right first time.
By learning more about the nature and compatibility of ingredients with HSP, it is possible to formulate with the minimum concentration, while achieving utmost performance. Applying HSP in this way allows the selection of suitable ingredients with certain properties in an efficient and effective manner.
Implementing HSP
When solvents/oils are combined, the HSP of the resulting blend changes to one that lies between the original solvents/oils, in proportion to the amount of each solvent/oil used. This is shown in the image below, where Propylene Carbonate and Propylene Glycol are blended in steps of 10% (e.g. 90:10, 80:20, 70:30…).
In this example, both
Propylene Carbonate and
Propylene Glycol lie outside of the solubility sphere and therefore are “
bad solvents”. Upon blending these solvents, certain combinations become “
good solvents” and will dissolve the material.
This is an extremely useful application of HSP which can swiftly create novel solutions to problems that otherwise could cause major delays to projects. It allows the formulator to think outside the box (or the sphere!), taking a component that is desirable for other reasons (e.g. low cost) and rationally matching it with another component to create a high-performance solution.
Examples of the HSP of Solvent Blends between
Propylene Carbonate and Propylene Glycol
If two or more raw materials are incompatible, by determining the HSP spheres of all materials, you can then see if there is any region of overlap of their spheres. Products that lie within the
overlap of the solubility sphere of all materials will aid the overall compatibility of the system. If there is no overlap between the ingredients, it is still possible to find an ingredient that lies between the two in the solubility space. This third ingredient can bridge the gap between them and again aid the overall compatibility.
Examples of Finding Solvents within the Overlap of
2 Incompatible Products Solubility Sphere to aid Compatibility
Theoretical Predicted or Practically Determined?
HSP parameters of most molecules can be
theoretically predicted from their SMILES, by using the Yamamoto-Molecular Break (Y-MB) which breaks SMILES into the corresponding functional groups and estimate’s their various properties. The predictive power of the software is an impressive achievement, yet is not perfect and contains known errors.
HSP theory was originally developed to be a practical test and by practically determining the HSP of molecules or products you can be confident that this theoretical error is removed and that you have true HSP values of your product. It is only possible to theoretically predict the D, P and H values and not the Solubility Radius, which is a vital piece of information for many aspects of formulating and to truly implement the HSP of your product. By practically determining the HSP of your product the
Solubility Radius will be calculated, which can even be tailored to the specific concentration of your final formulation.
If you wish to discuss the possibility of determining the HSP of a product, please contact SBFG by sending an
email to
Sreeparna Das
Read other articles on HSP here!